@Preamble{
"\ifx \undefined \Dbar \def \Dbar {\leavevmode\raise0.2ex\hbox{--}\kern-0.5emD} \fi" #
"\ifx \undefined \dbar \def \dbar {\leavevmode\raise0.2ex\hbox{--}\kern-0.5emd} \fi" #
"\ifx \undefined \ocirc \def \ocirc #1{{\accent'27#1}} \fi" #
"\ifx \undefined \varvec \def \varvec #1{\hbox{\boldmath $#1$}} \fi"
}
@String{ack-nhfb = "Nelson H. F. Beebe,
University of Utah,
Department of Mathematics, 110 LCB,
155 S 1400 E RM 233,
Salt Lake City, UT 84112-0090, USA,
Tel: +1 801 581 5254,
e-mail: \path|beebe@math.utah.edu|,
\path|beebe@acm.org|,
\path|beebe@computer.org| (Internet),
URL: \path|https://www.math.utah.edu/~beebe/|"}
@String{j-VLDB-J = "VLDB Journal: Very Large Data Bases"}
@Article{Breitbart:1992:TMI,
author = "Yuri Breitbart and Abraham Silberschatz and Glenn R.
Thompson",
title = "Transaction Management Issues in a Failure-Prone
Multidatabase System Environment",
journal = j-VLDB-J,
volume = "1",
number = "1",
pages = "1--39",
month = jul,
year = "1992",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:23 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb1.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Breitbart:Yuri.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Silberschatz:Abraham.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Thompson:Glenn_R=.html",
abstract = "This paper is concerned with the problem of
integrating a number of existing, off-the-shelf local
database systems into a multidatabase system that
maintains consistency in the face of concurrency and
failures. The major difficulties in designing such
systems stem from the requirements that local
transactions be allowed to execute outside the
multidatabase system control, and that the various
local database systems cannot participate in the
execution of a global commit protocol. A scheme based
on the assumption that the component local database
systems use the strict two-phase locking protocol is
developed. Two major problems are addressed: How to
ensure global transaction atomicity without the
provision of a commit protocol, and how to ensure
freedom from global deadlocks.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "algorithms; deadlock recovery; performance;
reliability; serializibility; transaction log",
xxauthor = "Yuri Breitbart and Avi Silberschatz and Glenn R.
Thompson",
xxpages = "1--40",
}
@Article{Nodine:1992:CTH,
author = "Marian H. Nodine and Stanley B. Zdonik",
title = "Cooperative Transaction Hierarchies: Transaction
Support for Design Applications",
journal = j-VLDB-J,
volume = "1",
number = "1",
pages = "41--80",
month = jul,
year = "1992",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:23 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb1.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/n/Nodine:Marian_H=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/z/Zdonik:Stanley_B=.html",
abstract = "Traditional atomic and nested transactions are not
always well-suited to cooperative applications, such as
design applications. Cooperative applications place
requirements on the database that may conflict with the
serializability requirement. They require transactions
to be long, possibly nested, and able to interact with
each other in a structured way. We define a transaction
framework, called a {\em cooperative transaction
hierarchy}, that allows us to relax the requirement for
atomic, serializable transactions to better support
cooperative applications. In cooperative transaction
hierarchies, we allow the correctness specification for
groups of designers to be tailored to the needs of the
application. We use {\em patterns\/} and {\em
conflicts\/} to specify the constraints imposed on a
group's history for it to be correct. We also provide
some primitives to smooth the operation of the members.
We characterize deadlocks in a cooperative transaction
hierarchy, and provide mechanisms for deadlock
detection and resolution. We examine issues associated
with failure and recovery.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "cooperation; deadlock detection; design transactions;
non-serializability; transaction hierarchies;
transaction synchronization; version management",
}
@Article{Spaccapietra:1992:MIA,
author = "Stefano Spaccapietra and Christine Parent and Yann
Dupont",
title = "Model Independent Assertions for Integration of
Heterogeneous Schemas",
journal = j-VLDB-J,
volume = "1",
number = "1",
pages = "81--126",
month = jul,
year = "1992",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:23 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb1.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/Dupont:Yann.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Parent:Christine.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Spaccapietra:Stefano.html",
abstract = "Due to the proliferation of database applications, the
integration of existing databases into a distributed or
federated system is one of the major challenges in
responding to enterprises' information requirements.
Some proposed integration techniques aim at providing
database administrators (DBAs) with a view definition
language they can use to build the desired integrated
schema. These techniques leave to the DBA the
responsibility of appropriately restructuring schema
elements from existing local schemas and of solving
inter-schema conflicts. This paper investigates the
{\em assertion-based\/} approach, in which the DBA's
action is limited to pointing out corresponding
elements in the schemas and to defining the nature of
the correspondence in between. This methodology is
capable of: ensuring better integration by taking into
account additional semantic information (assertions
about links); automatically solving structural
conflicts; building the integrated schema without
requiring conforming of initial schemas; applying
integration rules to a variety of data models; and
performing view as well as database integration. This
paper presents the basic ideas underlying our approach
and focuses on resolution of structural conflicts.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "conceptual modeling; database design and integration;
distributed databases; federated databases;
heterogeneous databases; schema integration",
}
@Article{Hsiao:1992:FDSa,
author = "David K. Hsiao",
title = "Federated Databases and Systems: {Part I} --- a
Tutorial on Their Data Sharing",
journal = j-VLDB-J,
volume = "1",
number = "1",
pages = "127--179",
month = jul,
year = "1992",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:23 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb1.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Hsiao:David_K=.html",
abstract = "The issues and solutions for the interoperability of a
class of heterogeneous databases and their database
systems are expounded in two parts. Part I presents the
data-sharing issues in federated databases and systems.
Part II, which will appear in a future issue, explores
resource-consolidation issues. {\em Interoperability\/}
in this context refers to data sharing among
heterogeneous databases, and to resource consolidation
of computer hardware, system software, and support
personnel. {\em Resource consolidation\/} requires the
presence of a database system architecture which
supports the heterogeneous system software, thereby
eliminating the need for various computer hardware and
support personnel. The class of heterogeneous databases
and database systems expounded herein is termed {\em
federated}, meaning that they are joined in order to
meet certain organizational requirements and because
they require their respective application
specificities, integrity constraints, and security
requirements to be upheld. Federated databases and
systems are new. While there are no technological
solutions, there has been considerable research towards
their development. This tutorial is aimed at exposing
the need for such solutions. A taxonomy is introduced
in our review of existing research undertakings and
exploratory developments. With this taxonomy, we
contrast and compare various approaches to federating
databases and systems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "attribute-based;
data-model-and-language-to-data-model-and-language
mappings; database conversion; hierarchical; network;
object-oriented; relational; schema transformation;
transaction translation",
xxpages = "127--180",
}
@Article{Breitbart:1992:OMT,
author = "Yuri Breitbart and Hector Garcia-Molina and Abraham
Silberschatz",
title = "Overview of Multidatabase Transaction Management",
journal = j-VLDB-J,
volume = "1",
number = "2",
pages = "181--240",
month = oct,
year = "1992",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:23 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb1.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Breitbart:Yuri.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Garcia=Molina:Hector.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Silberschatz:Abraham.html",
abstract = "A multidatabase system (MDBS) is a facility that
allows users access to data located in multiple
autonomous database management systems (DBMSs). In such
a system, {\em global transactions\/} are executed
under the control of the MDBS. Independently, {\em
local transactions\/} are executed under the control of
the local DBMSs. Each local DBMS integrated by the MDBS
may employ a different transaction management scheme.
In addition, each local DBMS has complete control over
all transactions (global and local) executing at its
site, including the ability to abort at any point any
of the transactions executing at its site. Typically,
no design or internal DBMS structure changes are
allowed in order to accommodate the MDBS. Furthermore,
the local DBMSs may not be aware of each other and, as
a consequence, cannot coordinate their actions. Thus,
traditional techniques for ensuring transaction
atomicity and consistency in homogeneous distributed
database systems may not be appropriate for an MDBS
environment. The objective of this article is to
provide a brief review of the most current work in the
area of multidatabase transaction management. We first
define the problem and argue that the multidatabase
research will become increasingly important in the
coming years. We then outline basic research issues in
multidatabase transaction management and review recent
results in the area. We conclude with a discussion of
open problems and practical implications of this
research.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "multidatabase; recovery; reliability; serializability;
transaction; two-level serializability",
xxauthor = "Yuri Breitbart and Hector Garcia-Molina and Avi
Silberschatz",
}
@Article{Drew:1992:TII,
author = "Pamela Drew and Roger King and Dennis Heimbigner",
title = "A Toolkit for the Incremental Implementation of
Heterogeneous Database Management Systems",
journal = j-VLDB-J,
volume = "1",
number = "2",
pages = "241--284",
month = oct,
year = "1992",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:23 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb1.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/Drew:Pamela.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Heimbigner:Dennis.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/King:Roger.html",
abstract = "The integration of heterogeneous database environments
is a difficult and complex task. The A la carte
Framework addresses this complexity by providing a
reusable and extensible architecture in which a set of
heterogeneous database management systems can be
integrated. The goal is to support incremental
integration of existing database facilities into
heterogeneous, interoperative, distributed systems. The
Framework addresses the three main issues in
heterogeneous systems integration. First, it identifies
the problems in integrating heterogeneous systems.
Second, it identifies the key interfaces and parameters
required for autonomous systems to interoperate
correctly. Third, it demonstrates an approach to
integrating these interfaces in an extensible and
incremental way. The A la carte Framework provides a
set of reusable, integrating components which integrate
the major functional domains, such as transaction
management, that could or should be integrated in
heterogeneous systems. It also provides a mechanism for
capturing key characteristics of the components and
constraints which describe how the components can be
mixed and interchanged, thereby helping to reduce the
complexity of the integration process. Using this
framework, we have implemented an experimental,
heterogeneous configuration as part of the object
management work in the software engineering research
consortium, Arcadia.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database toolkits; extensible databases; heterogeneous
databases; heterogeneous transaction management;
incremental integration; open architectures;
reconfigurable architectures",
}
@Article{Hsiao:1992:FDSb,
author = "David K. Hsiao",
title = "Federated Databases and Systems: {Part II} --- a
Tutorial on Their Resource Consolidation",
journal = j-VLDB-J,
volume = "1",
number = "2",
pages = "285--310",
month = oct,
year = "1992",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:23 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb1.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Hsiao:David_K=.html",
abstract = "The issues and solutions for the interoperability of a
class of heterogeneous databases and their database
systems are expounded in two parts. Part I presented
the data-sharing issues in federated databases and
systems (Hsiao, 1992). The present article explores
resource-consolidation issues. {\em Interoperability\/}
in this context refers to data sharing among
heterogeneous databases, and to resource consolidation
of computer hardware, system software, and support
personnel. {\em Resource consolidation\/} requires the
presence of a database system architecture which
supports the heterogeneous system software, thereby
eliminating the need for various computer hardware and
support personnel. The class of heterogeneous databases
and database systems expounded herein is termed {\em
federated}, meaning that they are joined in order to
meet certain organizational requirements and because
they require their respective application
specificities, integrity constraints, and security
requirements to be upheld. Federated databases and
systems are new. While there are no technological
solutions, there has been considerable research towards
their development. This tutorial is aimed at exposing
the need for such solutions. A taxonomy is introduced
in our review of existing research undertakings and
exploratory developments. With this taxonomy, we
contrast and compare various approaches to federating
databases and systems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "attribute-based;
data-model-and-language-to-data-model-and-language
mappings; database conversion; hierarchical; network;
object-oriented; relational; schema transformation;
transaction translation",
}
@Article{Yu:1993:BMB,
author = "Philip S. Yu and Douglas W. Cornell",
title = "Buffer Management Based on Return on Consumption in a
Multi-Query Environment",
journal = j-VLDB-J,
volume = "2",
number = "1",
pages = "1--37",
month = jan,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:24 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Cornell:Douglas_W=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/y/Yu:Philip_S=.html",
abstract = "In a multi-query environment, the marginal utilities
of allocating additional buffer to the various queries
can be vastly different. The conventional approach
examines each query in isolation to determine the
optimal access plan and the corresponding locality set.
This can lead to performance that is far from optimal.
As each query can have different access plans with
dissimilar locality sets and sensitivities to memory
requirement, we employ the concepts of memory
consumption and return on consumption (ROC) as the
basis for memory allocations. Memory consumption of a
query is its space-time product, while ROC is a measure
of the effectiveness of response-time reduction through
additional memory consumption. A global optimization
strategy using simulated annealing is developed, which
minimizes the average response over all queries under
the constraint that the total memory consumption rate
has to be less than the buffer size. It selects the
optimal join method and memory allocation for all query
types simultaneously. By analyzing the way the optimal
strategy makes memory allocations, a heuristic
threshold strategy is then proposed. The threshold
strategy is based on the concept of ROC. As the memory
consumption rate by all queries is limited by the
buffer size, the strategy tries to allocate the memory
so as to make sure that a certain level of ROC is
achieved. A simulation model is developed to
demonstrate that the heuristic strategy yields
performance that is very close to the optimal strategy
and is far superior to the conventional allocation
strategy.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "buffer management; join methods; query optimization;
queueing model; simulated annealing; simulation",
xxpages = "1--38",
}
@Article{Harder:1993:CCI,
author = "Theo H{\"a}rder and Kurt Rothermel",
title = "Concurrency Control Issues in Nested Transactions",
journal = j-VLDB-J,
volume = "2",
number = "1",
pages = "39--74",
month = jan,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:24 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/H=auml=rder:Theo.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Rothermel:Kurt.html",
abstract = "The concept of nested transactions offers more
decomposable execution units and finer-grained control
over concurrency and recovery than `flat' transactions.
Furthermore, it supports the decomposition of a `unit
of work' into subtasks and their appropriate
distribution in a computer system as a prerequisite of
intratransaction parallelism. However, to exploit its
full potential, suitable granules of concurrency
control as well as access modes for shared data are
necessary. In this article, we investigate various
issues of concurrency control for nested transactions.
First, the mechanisms for cooperation and communication
within nested transactions should not impede parallel
execution of transactions among parent and children or
among siblings. Therefore, a model for nested
transactions is proposed allowing for effective
exploitation of intra-transaction parallelism. Starting
with a set of basic locking rules, we introduce the
concept of `downward inheritance of locks' to make data
manipulated by a parent available to its children. To
support supervised and restricted access, this concept
is refined to `controlled downward inheritance.' The
initial concurrency control scheme was based on S-X
locks for `flat,' non-overlapping data objects. In
order to adjust this scheme for practical applications,
a set of concurrency control rules is derived for
generalized lock modes described by a compatibility
matrix. Also, these rules are combined with a
hierarchical locking scheme to improve selective access
to data granules of varying sizes. After having tied
together both types of hierarchies (transaction and
object), it can be shown how `controlled downward
inheritance' for hierarchical objects is achieved in
nested transactions. Finally, problems of deadlock
detection and resolution in nested transactions are
considered.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "concurrency control; locking; nested transactions;
object hierarchies",
}
@Article{Jensen:1993:UDT,
author = "Christian S. Jensen and Leo Mark and Nick Roussopoulos
and Timos K. Sellis",
title = "Using Differential Techniques to Efficiently Support
Transaction Time",
journal = j-VLDB-J,
volume = "2",
number = "1",
pages = "75--116",
month = jan,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:24 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/j/Jensen:Christian_S=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Mark:Leo.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Roussopoulos:Nick.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sellis:Timos_K=.html",
abstract = "We present an architecture for query processing in the
relational model extended with transaction time. The
architecture integrates standard query optimization and
computation techniques with new differential
computation techniques. Differential computation
computes a query incrementally or decrementally from
the cached and indexed results of previous
computations. The use of differential computation
techniques is essential in order to provide efficient
processing of queries that access very large temporal
relations. Alternative query plans are integrated into
a state transition network, where the state space
includes backlogs of base relations, cached results
from previous computations, a cache index, and
intermediate results; the transitions include standard
relational algebra operators, operators for
constructing differential files, operators for
differential computation, and combined operators. A
rule set is presented to prune away parts of state
transition networks that are not promising, and dynamic
programming techniques are used to identify the optimal
plans from the remaining state transition networks. An
extended logical access path serves as a `structuring'
index on the cached results and contains, in addition,
vital statistics for the query optimization process
(including statistics about base relations, backlogs,
and queries---previously computed and cached,
previously computed, or just previously estimated).",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "efficient query processing; incremental and
decremental computation; temporal databases;
transaction time",
}
@Article{Haritsa:1993:VBS,
author = "Jayant R. Haritsa and Michael J. Carey and Miron
Livny",
title = "Value-Based Scheduling in Real-Time Database Systems",
journal = j-VLDB-J,
volume = "2",
number = "2",
pages = "117--152",
month = apr,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:25 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Carey:Michael_J=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Haritsa:Jayant_R=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Livny:Miron.html",
abstract = "In a real-time database system, an application may
assign a {\em value\/} to a transaction to reflect the
return it expects to receive if the transaction commits
before its deadline. Most research on real-time
database systems has focused on systems where all
transactions are assigned the same value, the
performance goal being to minimize the number of missed
deadlines. When transactions are assigned different
values, the goal of the system shifts to maximizing the
sum of the values of those transactions that commit by
their deadlines. Minimizing the number of missed
deadlines becomes a secondary concern. In this article,
we address the problem of establishing a priority
ordering among transactions characterized by both
values and deadlines that results in maximizing the
realized value. Of particular interest is the tradeoff
established between these values and deadlines in
constructing the priority ordering. Using a detailed
simulation model, we evaluate the performance of
several priority mappings that make this tradeoff in
different, but fixed, ways. In addition, a `bucket'
priority mechanism that allows the relative importance
of values and deadlines to be controlled is introduced
and studied. The notion of associating a penalty with
transactions whose deadlines are not met is also
briefly considered.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "priority and concurrency algorithms; priority mapping;
resource and data contention; transaction values and
deadlines",
}
@Article{Grant:1993:QLR,
author = "John Grant and Witold Litwin and Nick Roussopoulos and
Timos K. Sellis",
title = "Query Languages for Relational Multidatabases",
journal = j-VLDB-J,
volume = "2",
number = "2",
pages = "153--171",
month = apr,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:25 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Grant:John.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Litwin:Witold.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Roussopoulos:Nick.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sellis:Timos_K=.html",
abstract = "With the existence of many autonomous databases widely
accessible through computer networks, users will
require the capability to jointly manipulate data in
different databases. A multidatabase system provides
such a capability through a multidatabase manipulation
language, such as MSQL. We propose a theoretical
foundation for such languages by presenting a
multirelational algebra and calculus based on the
relational algebra and calculus. The proposal is
illustrated by various queries on an example
multidatabase. It is shown that properties of the
multirelational algebra may be used for optimization
and that every multirelational algebra query can be
expressed as a multirelational calculus query. The
connection between the multirelational languages and
MSQL, the multidatabase version of SQL, is also
investigated.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "multidatabase; multirelational algebra;
multirelational calculus; query optimization",
xxpages = "153--172",
}
@Article{Neufeld:1993:GCT,
author = "Andrea Neufeld and Guido Moerkotte and Peter C.
Lockemann",
title = "Generating Consistent Test Data for a Variable Set of
General Consistency Constraints",
journal = j-VLDB-J,
volume = "2",
number = "2",
pages = "173--213",
month = apr,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:25 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Lockemann:Peter_C=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Moerkotte:Guido.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/n/Neufeld:Andrea.html",
abstract = "To address the problem of generating test data for a
set of general consistency constraints, we propose a
new two-step approach: First the interdependencies
between consistency constraints are explored and a
generator formula is derived on their basis. During its
creation, the user may exert control. In essence, the
generator formula contains information to restrict the
search for consistent test databases. In the second
step, the test database is generated. Here, two
different approaches are proposed. The first adapts an
already published approach to generating finite models
by enhancing it with requirements imposed by test data
generation. The second, a new approach, operationalizes
the generator formula by translating it into a sequence
of operators, and then executes it to construct the
test database. For this purpose, we introduce two
powerful operators: the generation operator and the
test-and-repair operator. This approach also allows for
enhancing the generation operators with heuristics for
generating facts in a goal-directed fashion. It avoids
the generation of test data that may contradict the
consistency constraints, and limits the search space
for the test data. This article concludes with a
careful evaluation and comparison of the performance of
the two approaches and their variants by describing a
number of benchmarks and their results.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "consistency; design; logic; test data; validation",
xxpages = "173--214",
xxtitle = "Generating consistent test data: restricting the
search space by a generator formula",
}
@Article{Du:1993:SCU,
author = "Weimin Du and Ahmed K. Elmagarmid and Won Kim and
Omran A. Bukhres",
title = "Supporting Consistent Updates in Replicated
Multidatabase Systems",
journal = j-VLDB-J,
volume = "2",
number = "2",
pages = "215--241",
month = apr,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:25 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Bukhres:Omran_A=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/Du:Weimin.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/e/Elmagarmid:Ahmed_K=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kim:Won.html",
abstract = "Replication is useful in multidatabase systems (MDBSs)
because, as in traditional distributed database
systems, it increases data availability in the presence
of failures and decreases data retrieval costs by
reading local or close copies of data. Concurrency
control, however, is more difficult in replicated MDBSs
than in ordinary distributed database systems. This is
the case not only because local concurrency controllers
may schedule global transactions inconsistently, but
also because local transactions (at different sites)
may access the same replicated data. In this article,
we propose a decentralized concurrency control protocol
for a replicated MDBS. The proposed strategy supports
prompt and consistent updates of replicated data by
both local and global applications without a central
coordinator.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "concurrency control; multidatabases; replica control;
replicated data management; resolvable conflicts;
serializability",
}
@Article{Anonymous:1993:Ca,
author = "Anonymous",
title = "Column",
journal = j-VLDB-J,
volume = "2",
number = "2",
pages = "??--??",
month = apr,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:25 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Anonymous:1993:Cb,
author = "Anonymous",
title = "Column",
journal = j-VLDB-J,
volume = "2",
number = "2",
pages = "??--??",
month = apr,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:25 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Tomasic:1993:SIP,
author = "Anthony Tomasic and Hector Garcia-Molina",
title = "Special Issue in Parallelism in Database Systems:
Query Processing and Inverted Indices in Shared-Nothing
Document Information Retrieval Systems",
journal = j-VLDB-J,
volume = "2",
number = "3",
pages = "243--275",
month = jul,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 27 08:46:01 MDT 2000",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Garcia=Molina:Hector.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Tomasic:Anthony.html",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Tomasic:1993:QPI,
author = "Anthony Tomasic and Hector Garcia-Molina",
title = "Query processing and inverted indices in shared:
nothing text document information retrieval systems",
journal = j-VLDB-J,
volume = "2",
number = "3",
pages = "243--276",
month = jul,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:26 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The performance of distributed text document retrieval
systems is strongly influenced by the organization of
the inverted text. This article compares the
performance impact on query processing of various
physical organizations for inverted lists. We present a
new probabilistic model of the database and queries.
Simulation experiments determine those variables that
most strongly influence response time and throughput.
This leads to a set of design trade-offs over a wide
range of hardware configurations and new parallel query
processing strategies.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "file organization; full text information retrieval;
inverted file; inverted index; performance; query
processing; shared-nothing; striping",
}
@Article{Ziane:1993:PQP,
author = "Mikal Ziane and Mohamed Za{\"\i}t and Pascale
Borla-Salamet",
title = "Parallel Query Processing with Zigzag Trees",
journal = j-VLDB-J,
volume = "2",
number = "3",
pages = "277--301",
month = jul,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:26 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Borla=Salamet:Pascale.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/z/Za=iuml=t:Mohamed.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/z/Ziane:Mikal.html",
abstract = "In this article, we describe our approach to the
compile-time optimization and parallelization of
queries for execution in DBS3 or EDS. DBS3 is a
shared-memory parallel database system, while the EDS
system has a distributed-memory architecture. Because
DBS3 implements a parallel dataflow execution model,
this approach applies to both architectures. Using
randomized search strategies enables the exploration of
a search space large enough to include zigzag trees,
which are intermediate between left-deep and right-deep
trees. Zigzag trees are shown to provide better
response time than right-deep trees in case of limited
memory. Performance measurements obtained using the
DBS3 prototype show the advantages of zigzag trees
under various conditions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "cost function; fragmentation; pipeline; search space",
xxpages = "277--302",
}
@Article{Hua:1993:CDS,
author = "Kien A. Hua and Yu-lung Lo and Honesty C. Young",
title = "Considering Data Skew Factor in Multi-Way Join Query
Optimization for Parallel Execution",
journal = j-VLDB-J,
volume = "2",
number = "3",
pages = "303--330",
month = jul,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:26 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Hua:Kien_A=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Lo:Yu=lung.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/y/Young:Honesty_C=.html",
abstract = "A consensus on parallel architecture for very large
database management has emerged. This architecture is
based on a shared-nothing hardware organization. The
computation model is very sensitive to skew in tuple
distribution, however. Recently, several parallel join
algorithms with dynamic load balancing capabilities
have been proposed to address this issue, but none of
them consider multi-way join problems. In this article
we propose a dynamic load balancing technique for
multi-way joins, and investigate the effect of load
balancing on query optimization. In particular, we
present a join-ordering strategy that takes
load-balancing issues into consideration. Our
performance study indicates that the proposed query
optimization technique can provide very impressive
performance improvement over conventional approaches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "load balancing; multi-way join; parallel-database
computer; query optimization",
xxauthor = "Kien A. Hua and Yo Lung Lo and Honesty C. Young",
}
@Article{Zhang:1993:TGC,
author = "Aidong Zhang and Ahmed K. Elmagarmid",
title = "A Theory of Global Concurrency Control in
Multidatabase Systems",
journal = j-VLDB-J,
volume = "2",
number = "3",
pages = "331--360",
month = jul,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:26 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/e/Elmagarmid:Ahmed_K=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/z/Zhang:Aidong.html",
abstract = "This article presents a theoretical basis for global
concurrency control to maintain global serializability
in multidatabase systems. Three correctness criteria
are formulated that utilize the intrinsic
characteristics of global transactions to determine the
serialization order of global subtransactions at each
local site. In particular, two new types of
serializability, chain-conflicting serializability and
sharing serializability, are proposed and hybrid
serializability, which combines these two basic
criteria, is discussed. These criteria offer the
advantage of imposing no restrictions on local sites
other than local serializability while retaining global
serializability. The graph testing techniques of the
three criteria are provided as guidance for global
transaction scheduling. In addition, an optimal
property of global transactions for determinating the
serialization order of global subtransactions at local
sites is formulated. This property defines the upper
limit on global serializability in multidatabase
systems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "chain-conflicting serializability; hybrid
serializability; optimality; sharing serializability",
}
@Article{Anonymous:1993:SIP,
author = "Anonymous",
title = "Special issue in parallelism in database systems",
journal = j-VLDB-J,
volume = "2",
number = "3",
pages = "??--??",
month = jul,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:26 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Srinivasan:1993:PBT,
author = "V. Srinivasan and Michael J. Carey",
title = "Performance of {B$^+$} tree concurrency control
algorithms",
journal = j-VLDB-J,
volume = "2",
number = "4",
pages = "361--406",
month = oct,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:27 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Carey:Michael_J=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Srinivasan:V=.html",
abstract = "A number of algorithms have been proposed to access
B$^+$-trees concurrently, but they are not well
understood. In this article, we study the performance
of various B$^+$-tree concurrency control algorithms
using a detailed simulation model of B$^+$-tree
operations in a centralized DBMS. Our study covers a
wide range of data contention situations and resource
conditions. In addition, based on the performance of
the set of B$^+$-tree concurrency control algorithms,
which includes one new algorithm, we make projections
regarding the performance of other algorithms in the
literature. Our results indicate that algorithms with
updaters that lock-couple using exclusive locks perform
poorly as compared to those that permit more optimistic
index descents. In particular, the B-link algorithms
are seen to provide the most concurrency and the best
overall performance. Finally, we demonstrate the need
for a highly concurrent long-term lock holding strategy
to obtain the full benefits of a highly concurrent
algorithm for index operations.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "B+-tree structures; data contention; lock modes;
performance; resource conditions; simulation models;
workload parameters",
xxtitle = "Performance of {B+} Tree Concurrency Algorithms",
}
@Article{Weikum:1993:MLT,
author = "Gerhard Weikum and Christof Hasse",
title = "Multi-Level Transaction Management for Complex
Objects: Implementation, Performance, Parallelism",
journal = j-VLDB-J,
volume = "2",
number = "4",
pages = "407--453",
month = oct,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:27 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Hasse:Christof.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/w/Weikum:Gerhard.html",
abstract = "Multi-level transactions are a variant of open-nested
transactions in which the subtransactions correspond to
operations at different levels of a layered system
architecture. They allow the exploitation of semantics
of high-level operations to increase concurrency. As a
consequence, undoing a transaction requires
compensation of completed subtransactions. In addition,
multi-level recovery methods must take into
consideration that high-level operations are not
necessarily atomic if multiple pages are updated in a
single subtransaction. This article presents algorithms
for multi-level transaction management that are
implemented in the database kernel system (DASDBS). In
particular, we show that multi-level recovery can be
implemented in an efficient way. We discuss performance
measurements using a synthetic benchmark for processing
complex objects in a multi-user environment. We show
that multi-level transaction management can be extended
easily to cope with parallel subtransactions within a
single transaction. Performance results are presented
with varying degrees of inter- and intratransaction
parallelism.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "atomicity; complex objects; inter- and
intratransaction parallelism; multi-level transactions;
performance; persistence; recovery",
xxpages = "407--454",
}
@Article{Storey:1993:USR,
author = "Veda C. Storey",
title = "Understanding Semantic Relationships",
journal = j-VLDB-J,
volume = "2",
number = "4",
pages = "455--488",
month = oct,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:27 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Storey:Veda_C=.html",
abstract = "To develop sophisticated database management systems,
there is a need to incorporate more understanding of
the real world in the information that is stored in a
database. Semantic data models have been developed to
try to capture some of the meaning, as well as the
structure, of data using abstractions such as
inclusion, aggregation, and association. Besides these
well-known relationships, a number of additional
semantic relationships have been identified by
researchers in other disciplines such as linguistics,
logic, and cognitive psychology. This article explores
some of the lesser-recognized semantic relationships
and discusses both how they could be captured, either
manually or by using an automated tool, and their
impact on database design. To demonstrate the
feasibility of this research, a prototype system for
analyzing semantic relationships, called the Semantic
Relationship Analyzer, is presented.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database design; database design systems;
entity-relationship model; relational model; semantic
relationships",
}
@Article{Tseng:1993:SMS,
author = "Frank Shou-Cheng Tseng and Arbee L. P. Chen and W.-P.
Yang",
title = "Searching a Minimal Semantically-Equivalent Subset of
a Set of Partial Values",
journal = j-VLDB-J,
volume = "2",
number = "4",
pages = "489--512",
month = oct,
year = "1993",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:27 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb2.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Chen:Arbee_L=_P=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Tseng:Frank_Shou=Cheng.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/y/Yang:W==P=.html",
abstract = "Imprecise data exist in databases due to their
unavailability or to data/schema incompatibilities in a
multidatabase system. Partial values have been used to
represent imprecise data. Manipulation of partial
values is therefore necessary to process queries
involving imprecise data. In this article, we study the
problem of eliminating redundant partial values that
result from a projection on an attribute with partial
values. The redundancy of partial values is defined
through the interpretation of a set of partial values.
This problem is equivalent to searching a minimal
semantically-equivalent subset of a set of partial
values. A semantically-equivalent subset contains
exactly the same information as the original set. We
derive a set of useful properties and apply a graph
matching technique to develop an efficient algorithm
for searching such a minimal subset and therefore
eliminating redundant partial values. By this process,
we not only provide a concise answer to the user, but
also reduce the communication cost when partial values
are requested to be transmitted from one site to
another site in a distributed environment. Moreover,
further manipulation of the partial values can be
simplified. This work is also extended to the case of
multi-attribute projections.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "bipartite graph; graph matching; imprecise data;
minimal elements; multidatabase systems; partial
values",
xxauthor = "Frank S. C. Tseng and Arbee L. P. Chen and Wei Pang
Yang",
}
@Article{Georgakopoulos:1994:CST,
author = "Dimitrios Georgakopoulos and Marek Rusinkiewicz and
Witold Litwin",
title = "Chronological Scheduling of Transactions with Temporal
Dependencies",
journal = j-VLDB-J,
volume = "3",
number = "1",
pages = "1--28",
month = jan,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:28 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Georgakopoulos:Dimitrios.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Litwin:Witold.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Rusinkiewicz:Marek.html",
abstract = "Database applications often impose temporal
dependencies between transactions that must be
satisfied to preserve data consistency. The extant
correctness criteria used to schedule the execution of
concurrent transactions are either time independent or
use strict, difficult to satisfy real-time constraints.
On one end of the spectrum, serializability completely
ignores time. On the other end, deadline scheduling
approaches consider the outcome of each transaction
execution correct only if the transaction meets its
real-time deadline. In this article, we explore new
correctness criteria and scheduling methods that
capture temporal transaction dependencies and belong to
the broad area between these two extreme approaches. We
introduce the concepts of {\em succession dependency\/}
and {\em chronological dependency\/} and define
correctness criteria under which temporal dependencies
between transactions are preserved even if the
dependent transactions execute concurrently. We also
propose a {\em chronological scheduler\/} that can
guarantee that transaction executions satisfy their
chronological constraints. The advantages of
chronological scheduling over traditional scheduling
methods, as well as the main issues in the
implementation and performance of the proposed
scheduler, are discussed.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "concurrent succession; execution correctness; partial
rollbacks; synchronization; transaction ordering",
}
@Article{Whang:1994:DMD,
author = "Kyu Young Whang and Sang Wook Kim and Gio Wiederhold",
title = "Dynamic Maintenance of Data Distribution for
Selectivity Estimation",
journal = j-VLDB-J,
volume = "3",
number = "1",
pages = "29--51",
month = jan,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:28 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kim:Sang=Wook.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/w/Whang:Kyu=Young.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/w/Wiederhold:Gio.html",
abstract = "We propose a new dynamic method for multidimensional
selectivity estimation for range queries that works
accurately independent of data distribution. Good
estimation of selectivity is important for query
optimization and physical database design. Our method
employs the multilevel grid file (MLGF) for accurate
estimation of multidimensional data distribution. The
MLGF is a dynamic, hierarchical, balanced,
multidimensional file structure that gracefully adapts
to nonuniform and correlated distributions. We show
that the MLGF directory naturally represents a
multidimensional data distribution. We then extend it
for further refinement and present the selectivity
estimation method based on the MLGF. Extensive
experiments have been performed to test the accuracy of
selectivity estimation. The results show that
estimation errors are very small independent of
distributions, even with correlated and/or highly
skewed ones. Finally, we analyze the cause of errors in
estimation and investigate the effects of various
parameters on the accuracy of estimation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "multidimensional file structure; multilevel grid
files; physical database design; query optimization",
}
@Article{Kamel:1994:PBO,
author = "Nabil Kamel and Ping Wu and Stanley Y. W. Su",
title = "A Pattern-Based Object Calculus",
journal = j-VLDB-J,
volume = "3",
number = "1",
pages = "53--76",
month = jan,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:28 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kamel:Nabil.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Su:Stanley_Y=_W=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/w/Wu:Ping.html",
abstract = "Several object-oriented database management systems
have been implemented without an accompanying
theoretical foundation for constraint, query
specification, and processing. The pattern-based object
calculus presented in this article provides such a
theoretical foundation for describing and processing
object-oriented databases. We view an object-oriented
database as a network of interrelated classes (i.e.,
the intension) and a collection of time-varying object
association patterns (i.e., the extension). The object
calculus is based on first-order logic. It provides the
formalism for interpreting precisely and uniformly the
semantics of queries and integrity constraints in
object-oriented databases. The power of the object
calculus is shown in four aspects. First, associations
among objects are expressed explicitly in an
object-oriented database. Second, the `nonassociation'
operator is included in the object calculus. Third,
set-oriented operations can be performed on both
homogeneous and heterogeneous object association
patterns. Fourth, our approach does not assume a
specific form of database schema. A proposed formalism
is also applied to the design of high-level
object-oriented query and constraint languages.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "association patterns; Object-oriented databases; query
expressions; semantic constraints",
}
@Article{Sciore:1994:VCM,
author = "Edward Sciore",
title = "Versioning and Configuration Management in an
Object-Oriented Data Model",
journal = j-VLDB-J,
volume = "3",
number = "1",
pages = "77--106",
month = jan,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:28 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sciore:Edward.html",
abstract = "Many database applications require the storage and
manipulation of different versions of data objects. To
satisfy the diverse needs of these applications,
current database systems support versioning at a very
low level. This article demonstrates that
application-independent versioning can be supported at
a significantly higher level. In particular, we extend
the EXTRA data model and EXCESS query language so that
configurations can be specified conceptually and
non-procedurally. We also show how version sets can be
viewed multidimensionally, thereby allowing
configurations to be expressed at a higher level of
abstraction. The resulting model integrates and
generalizes ideas in CAD systems, CASE systems, and
temporal databases.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "EXTRA/EXCESS data models; generic and specific
references; query language; semantically based
configuration specifications",
}
@Article{Ramamohanarao:1994:IDD,
author = "Kotagiri Ramamohanarao and James Harland",
title = "An introduction to deductive database languages and
systems",
journal = j-VLDB-J,
volume = "3",
number = "2",
pages = "107--122",
month = apr,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ramamohanarao:1994:SIP,
author = "Kotagiri Ramamohanarao and James Harland",
title = "Special Issue on Prototypes of Deductive Database
Systems: An Introduction to Deductive Database
Languages and Systems",
journal = j-VLDB-J,
volume = "3",
number = "2",
pages = "107--122",
month = apr,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 27 08:46:01 MDT 2000",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Harland:James.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Ramamohanarao:Kotagiri.html",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Derr:1994:GND,
author = "Marcia A. Derr and Shinichi Morishita and Geoffrey
Phipps",
title = "The Glue-Nail Deductive Database System: Design,
Implementation, and Evaluation",
journal = j-VLDB-J,
volume = "3",
number = "2",
pages = "123--160",
month = apr,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:29 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/Derr:Marcia_A=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Morishita:Shinichi.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Phipps:Geoffrey.html",
abstract = "We describe the design and implementation of the
Glue-Nail deductive database system. Nail is a purely
declarative query language; Glue is a procedural
language used for non-query activities. The two
languages combined are sufficient to write a complete
application. Nail and Glue code are both compiled into
the target language IGlue. The Nail compiler uses
variants of the magic sets algorithm and supports
well-founded models. The Glue compiler's static
optimizer uses peephole techniques and data flow
analysis to improve code. The IGlue interpreter
features a run-time adaptive optimizer that reoptimizes
queries and automatically selects indexes. We also
describe the Glue-Nail benchmark suite, a set of
applications developed to evaluate the Glue-Nail
language and to measure the performance of the
system.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "language; performance; query optimization",
}
@Article{Ramakrishnan:1994:CDS,
author = "Raghu Ramakrishnan and Divesh Srivastava and S.
Sudarshan and Praveen Seshadri",
title = "The {CORAL} Deductive System",
journal = j-VLDB-J,
volume = "3",
number = "2",
pages = "161--210",
month = apr,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:29 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Ramakrishnan:Raghu.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Seshadri:Praveen.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Srivastava:Divesh.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sudarshan:S=.html",
abstract = "CORAL is a deductive system that supports a rich
declarative language, and an interface to C++, which
allows for a combination of declarative and imperative
programming. A CORAL declarative program can be
organized as a collection of interacting modules. CORAL
supports a wide range of evaluation strategies, and
automatically chooses an efficient strategy for each
module in the program. Users can guide query
optimization by selecting from a wide range of control
choices. The CORAL system provides imperative
constructs to update, insert, and delete facts. Users
can program in a combination of declarative CORAL and
C++ extended with CORAL primitives. A high degree of
extensibility is provided by allowing C++ programmers
to use the class structure of C++ to enhance the CORAL
implementation. CORAL provides support for main-memory
data and, using the EXODUS storage manager,
disk-resident data. We present a comprehensive view of
the system from broad design goals, the language, and
the architecture, to language interfaces and
implementation details.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "deductive database; logic programming system; query
language",
}
@Article{Kiessling:1994:DSE,
author = "Werner Kie{\ss}ling and Helmut Schmidt and Werner
Strau{\ss} and Gerhard D{\"u}nzinger",
title = "{DECLARE} and {SDS}: Early Efforts to Commercialize
Deductive Database Technology",
journal = j-VLDB-J,
volume = "3",
number = "2",
pages = "211--243",
month = apr,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:29 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/D=uuml=nzinger:Gerhard.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kie=szlig=ling:Werner.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Schmidt:Helmut.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Strau=szlig=:Werner.html",
abstract = "The Smart Data System (SDS) and its declarative query
language, Declarative Reasoning, represent the first
large-scale effort to commercialize deductive database
technology. SDS offers the functionality of deductive
reasoning in a distributed, heterogeneous database
environment. In this article we discuss several
interesting aspects of the query compilation and
optimization process. The emphasis is on the query
execution plan data structure and its transformations
by the optimizing rule compiler. Through detailed case
studies we demonstrate that efficient and very compact
runtime code can be generated. We also discuss our
experiences gained from a large pilot application (the
MVV-expert) and report on several issues of practical
interest in engineering such a complex system,
including the migration from Lisp to C. We argue that
heuristic knowledge and control should be made an
integral part of deductive databases.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "declarative reasoning; distributed query processing;
heuristic control; multi-databases; productization;
query optimizer",
}
@Article{Vaghani:1994:ADD,
author = "Jayen Vaghani and Kotagiri Ramamohanarao and David B.
Kemp and Zoltan Somogyi and Peter J. Stuckey and Tim S.
Leask and James Harland",
title = "The {Aditi} Deductive Database System",
journal = j-VLDB-J,
volume = "3",
number = "2",
pages = "245--288",
month = apr,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:29 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Harland:James.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kemp:David_B=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Leask:Tim_S=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Ramamohanarao:Kotagiri.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Somogyi:Zoltan.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Stuckey:Peter_J=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/v/Vaghani:Jayen.html",
abstract = "Deductive databases generalize relational databases by
providing support for recursive views and non-atomic
data. Aditi is a deductive system based on the
client-server model; it is inherently multi-user and
capable of exploiting parallelism on shared-memory
multiprocessors. The back-end uses relational
technology for efficiency in the management of
disk-based data and uses optimization algorithms
especially developed for the bottom-up evaluation of
logical queries involving recursion. The front-end
interacts with the user in a logical language that has
more expressive power than relational query languages.
We present the structure of Aditi, discuss its
components in some detail, and present performance
figures.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "implementation; logic; multi-user; parallelism;
relational database",
}
@Article{Anonymous:1994:SIP,
author = "Anonymous",
title = "Special issue on prototypes of deductive database
systems",
journal = j-VLDB-J,
volume = "3",
number = "2",
pages = "??--??",
month = apr,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lee:1994:EIV,
author = "Byung Suk Lee and Gio Wiederhold",
title = "Efficiently Instantiating View-Objects From Remote
Relational Databases",
journal = j-VLDB-J,
volume = "3",
number = "3",
pages = "289--323",
month = jul,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:30 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Lee:Byung_Suk.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/w/Wiederhold:Gio.html",
abstract = "View-objects are complex objects that are instantiated
by delivering a query to a database and converting the
query result into a nested structure. In relational
databases, query results are conventionally retrieved
as a single flat relation, which contains duplicate
subtuples in its composite tuples. These duplicate
subtuples increase the amount of data to be handled and
thus degrade performance. In this article, we describe
two new methods that retrieve a query result in
structures other than a single flat relation. One
method retrieves a set of relation fragments, and the
other retrieves a single-nested relation. We first
describe their algorithms and cost models, and then
present the cost comparison results in a client-server
architecture with a relational main memory database
residing on a server.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "client server; complex object; nested relation; query
optimization; relation fragments",
}
@Article{Barbara-Milla:1994:DPT,
author = "Daniel Barbar{\'a}-Mill{\'a} and Hector
Garcia-Molina",
title = "The demarcation protocol: a technique for maintaining
constraints in distributed database systems",
journal = j-VLDB-J,
volume = "3",
number = "3",
pages = "325--353",
month = jul,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Traditional protocols for distributed database
management have a high message overhead; restrain or
lock access to resources during protocol execution; and
may become impractical for some scenarios like
real-time systems and very large distributed databases.
In this article, we present the demarcation protocol;
it overcomes these problems by using explicit
consistency constraints as the correctness criteria.
The method establishes safe limits as `lines drawn in
the sand' for updates, and makes it possible to change
these limits dynamically, enforcing the constraints at
all times. We show how this technique can be applied to
linear arithmetic, existential, key, and approximate
copy constraints.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "consistency constraints; serializability; transaction
limits",
}
@Article{Barbara:1994:DPT,
author = "Daniel Barbar{\'a} and Hector Garcia-Molina",
title = "The Demarcation Protocol: a Technique for Maintaining
Constraints in Distributed Database Systems",
journal = j-VLDB-J,
volume = "3",
number = "3",
pages = "325--353",
month = jul,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 27 08:46:01 MDT 2000",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Barbar=aacute=:Daniel.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Garcia=Molina:Hector.html",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bertino:1994:ICO,
author = "Elisa Bertino",
title = "Index Configuration in Object-Oriented Databases",
journal = j-VLDB-J,
volume = "3",
number = "3",
pages = "355--399",
month = jul,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:30 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Bertino:Elisa.html",
abstract = "In relational databases, an attribute of a relation
can have only a single primitive value, making it
cumbersome to model complex objects. The
object-oriented paradigm removes this difficulty by
introducing the notion of nested objects, which allows
the value of an object attribute to be another object
or a set of other objects. This means that a class
consists of a set of attributes, and the values of the
attributes are objects that belong to other classes;
that is, the definition of a class forms a hierarchy of
classes. All attributes of the nested classes are
nested attributes of the root of the hierarchy. A
branch of such hierarchy is called a {\em path}. In
this article, we address the problem of index
configuration for a given path. We first summarize some
basic concepts, and introduce the concept of index
configuration for a path. Then we present cost formulas
to evaluate the costs of the various configurations.
Finally, we present the algorithm that determines the
optimal configuration, and show its correctness.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "index selection; physical database design; query
optimization",
}
@Article{Guting:1994:ISD,
author = "Ralf Hartmut G{\"u}ting",
title = "An introduction to spatial database systems",
journal = j-VLDB-J,
volume = "3",
number = "4",
pages = "357--399",
month = oct,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:31 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We propose a definition of a spatial database system
as a database system that offers spatial data types in
its data model and query language, and supports spatial
data types in its implementation, providing at least
spatial indexing and spatial join methods. Spatial
database systems offer the underlying database
technology for geographic information systems and other
applications. We survey data modeling, querying, data
structures and algorithms, and system architecture for
such systems. The emphasis is on describing known
technology in a coherent manner, rather than listing
open problems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Guting:1994:SIS,
author = "Ralf Hartmut G{\"u}ting",
title = "Special Issue on Spatial Database Systems: An
Introduction to Spatial Database Systems",
journal = j-VLDB-J,
volume = "3",
number = "4",
pages = "357--399",
month = oct,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 27 08:46:01 MDT 2000",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/G=uuml=ting:Ralf_Hartmut.html",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Baumann:1994:MMD,
author = "Peter Baumann",
title = "Management of Multidimensional Discrete Data",
journal = j-VLDB-J,
volume = "3",
number = "4",
pages = "401--444",
month = oct,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:31 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Baumann:Peter.html",
abstract = "Spatial database management involves two main
categories of data: vector and raster data. The former
has received a lot of in-depth investigation; the
latter still lacks a sound framework. Current DBMSs
either regard raster data as pure byte sequences where
the DBMS has no knowledge about the underlying
semantics, or they do not complement array structures
with storage mechanisms suitable for huge arrays, or
they are designed as specialized systems with
sophisticated imaging functionality, but no general
database capabilities (e.g., a query language). Many
types of array data will require database support in
the future, notably 2-D images, audio data and general
signal-time series (1-D), animations (3-D), static or
time-variant voxel fields (3-D and 4-D), and the
ISO/IEC PIKS (Programmer's Imaging Kernel System)
BasicImage type (5-D). In this article, we propose a
comprehensive support of {\em multidimensional discrete
data\/} (MDD) in databases, including operations on
arrays of arbitrary size over arbitrary data types. A
set of requirements is developed, a small set of
language constructs is proposed (based on a formal
algebraic semantics), and a novel MDD architecture is
outlined to provide the basis for efficient MDD query
evaluation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "image database systems; multimedia database systems;
spatial index; tiling",
}
@Article{Chu:1994:SMA,
author = "Wesley W. Chu and Ion Tim Ieong and Ricky K. Taira",
title = "A Semantic Modeling Approach for Image Retrieval by
Content",
journal = j-VLDB-J,
volume = "3",
number = "4",
pages = "445--477",
month = oct,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:31 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Chu:Wesley_W=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/i/Ieong:Ion_Tim.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Taira:Ricky_K=.html",
abstract = "We introduce a semantic data model to capture the
hierarchical, spatial, temporal, and evolutionary
semantics of images in pictorial databases. This model
mimics the user's conceptual view of the image content,
providing the framework and guidelines for
preprocessing to extract image features. Based on the
model constructs, a spatial evolutionary query language
(SEQL), which provides direct image object manipulation
capabilities, is presented. With semantic information
captured in the model, spatial evolutionary queries are
answered efficiently. Using an object-oriented
platform, a prototype medical-image management system
was implemented at UCLA to demonstrate the feasibility
of the proposed approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "image; medical; multimedia databases; spatial query
processing; temporal evolutionary query processing",
}
@Article{Papadias:1994:QRS,
author = "Dimitris Papadias and Timos K. Sellis",
title = "Qualitative Representation of Spatial Knowledge in
Two-Dimensional Space",
journal = j-VLDB-J,
volume = "3",
number = "4",
pages = "479--516",
month = oct,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:31 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Papadias:Dimitris.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sellis:Timos_K=.html",
abstract = "Various relation-based systems, concerned with the
qualitative representation and processing of spatial
knowledge, have been developed in numerous application
domains. In this article, we identify the common
concepts underlying qualitative spatial knowledge
representation, we compare the representational
properties of the different systems, and we outline the
computational tasks involved in relation-based spatial
information processing. We also describe {\em symbolic
spatial indexes}, relation-based structures that
combine several ideas in spatial knowledge
representation. A symbolic spatial index is an array
that preserves only a set of spatial relations among
distinct objects in an image, called the modeling
space; the index array discards information, such as
shape and size of objects, and irrelevant spatial
relations. The construction of a symbolic spatial index
from an input image can be thought of as a
transformation that keeps only a set of representative
points needed to define the relations of the modeling
space. By keeping the relative arrangements of the
representative points in symbolic spatial indexes and
discarding all other points, we maintain enough
information to answer queries regarding the spatial
relations of the modeling space without the need to
access the initial image or an object database.
Symbolic spatial indexes can be used to solve problems
involving route planning, composition of spatial
relations, and update operations.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "qualitative spatial information processing;
representation of direction and topological relations;
spatial data models; spatial query languages",
}
@Article{Lin:1994:TTI,
author = "King Ip Lin and H. V. Jagadish and Christos
Faloutsos",
title = "The {TV}-Tree: An Index Structure for High-Dimensional
Data",
journal = j-VLDB-J,
volume = "3",
number = "4",
pages = "517--542",
month = oct,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:31 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb3.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/f/Faloutsos:Christos.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/j/Jagadish:H=_V=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Lin:King=Ip.html",
abstract = "We propose a file structure to index
high-dimensionality data, which are typically points in
some feature space. The idea is to use only a few of
the features, using additional features only when the
additional discriminatory power is absolutely
necessary. We present in detail the design of our tree
structure and the associated algorithms that handle
such `varying length' feature vectors. Finally, we
report simulation results, comparing the proposed
structure with the $ R*$-tree, which is one of the most
successful methods for low-dimensionality spaces. The
results illustrate the superiority of our method, which
saves up to 80\% in disk accesses.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "query by content; similarity retrieval; spatial
index",
}
@Article{Anonymous:1994:SIS,
author = "Anonymous",
title = "Special issue on spatial database systems",
journal = j-VLDB-J,
volume = "3",
number = "4",
pages = "??--??",
month = oct,
year = "1994",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:31 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Constantopoulos:1995:SIB,
author = "Panos Constantopoulos and Matthias Jarke and John
Mylopoulos and Yannis Vassiliou",
title = "The Software Information Base: a Server for Reuse",
journal = j-VLDB-J,
volume = "4",
number = "1",
pages = "1--43",
month = jan,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:32 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Constantopoulos:Panos.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/j/Jarke:Matthias.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Mylopoulos:John.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/v/Vassiliou:Yannis.html",
abstract = "We present an experimental software repository system
that provides organization, storage, management, and
access facilities for reusable software components. The
system, intended as part of an applications development
environment, supports the representation of information
about requirements, designs and implementations of
software, and offers facilities for visual presentation
of the software objects. This article details the
features and architecture of the repository system, the
technical challenges and the choices made for the
system development along with a usage scenario that
illustrates its functionality. The system has been
developed and evaluated within the context of the
ITHACA project, a technology integration/software
engineering project sponsored by the European
Communities through the ESPRIT program, aimed at
developing an integrated reuse-centered application
development and support environment based on
object-oriented techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "conceptual modeling; information storage and
retrieval; object-oriented databases; reuse; software
engineering",
}
@Article{Clifton:1995:HDQ,
author = "Chris Clifton and Hector Garcia-Molina and David
Bloom",
title = "{HyperFile}: a Data and Query Model for Documents",
journal = j-VLDB-J,
volume = "4",
number = "1",
pages = "45--86",
month = jan,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:32 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Bloom:David.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Clifton:Chris.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Garcia=Molina:Hector.html",
abstract = "Non-quantitative information such as documents and
pictures pose interesting new problems in the database
world. Traditional data models and query languages do
not provide appropriate support for this information.
Such data are typically stored in file systems, which
do not provide the security, integrity, or query
features of database management systems. The hypertext
model has emerged as a good interface to this
information; however, {\em finding\/} information using
hypertext browsing does not scale well. We developed a
query interface that serves as an extension of the
browsing model of hypertext systems. These queries
minimize the repeated user interactions required to
locate data in a standard hypertext system. HyperFile
is a prototype data server interface. In this article,
we describe HyperFile, including a number of issues
such as query generation, query processing, and
indexing.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "hypertext; indexing; user interface",
}
@Article{Agrawal:1995:OSL,
author = "Divyakant Agrawal and Amr {El Abbadi} and Richard
Jeffers and Lijing Lin",
title = "Ordered Shared Locks for Real-Time Databases",
journal = j-VLDB-J,
volume = "4",
number = "1",
pages = "87--126",
month = jan,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:32 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Abbadi:Amr_El.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Agrawal:Divyakant.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/j/Jeffers:Richard.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Lin:Lijing.html",
abstract = "We propose locking protocols for real-time databases.
Our approach has two main motivations: First, locking
protocols are widely accepted and used in most database
systems. Second, in real-time databases it has been
shown that the blocking behavior of transactions in
locking protocols results in performance degradation.
We use a new relationship between locks called ordered
sharing to eliminate blocking that arises in the
traditional locking protocols. Ordered sharing
eliminates blocking of read and write operations but
may result in delayed termination. Since timeliness and
not response time is the crucial factor in real-time
databases, our protocols exploit this delay to allow
transactions to execute within the slacks of delayed
transactions. We compare the performance of the
proposed protocols with the two-phase locking protocol
for real-time databases. Our experiments indicate that
the proposed protocols significantly reduce the
percentage of missed deadlines in the system for a
variety of workloads.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "concurrency control; time-critical scheduling;
transaction management",
}
@Article{Dan:1995:CDA,
author = "Asit Dan and Philip S. Yu and Jen Yao Chung",
title = "Characterization of Database Access Pattern for
Analytic Prediction of Buffer Hit Probability",
journal = j-VLDB-J,
volume = "4",
number = "1",
pages = "127--154",
month = jan,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:32 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Chung:Jen=Yao.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/Dan:Asit.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/y/Yu:Philip_S=.html",
abstract = "The analytic prediction of buffer hit probability,
based on the characterization of database accesses from
real reference traces, is extremely useful for workload
management and system capacity planning. The knowledge
can be helpful for proper allocation of buffer space to
various database relations, as well as for the
management of buffer space for a mixed transaction and
query environment. Access characterization can also be
used to predict the buffer invalidation effect in a
multi-node environment which, in turn, can influence
transaction routing strategies. However, it is a
challenge to characterize the database access pattern
of a real workload reference trace in a simple manner
that can easily be used to compute buffer hit
probability. In this article, we use a characterization
method that distinguishes three types of access
patterns from a trace: (1) locality within a
transaction, (2) random accesses by transactions, and
(3) sequential accesses by long queries. We then
propose a concise way to characterize the access skew
across randomly accessed pages by logically grouping
the large number of data pages into a small number of
partitions such that the frequency of accessing each
page within a partition can be treated as equal. Based
on this approach, we present a recursive binary
partitioning algorithm that can infer the access skew
characterization from the buffer hit probabilities for
a subset of the buffer sizes. We validate the buffer
hit predictions for single and multiple node systems
using production database traces. We further show that
the proposed approach can predict the buffer hit
probability of a composite workload from those of its
component files.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access skew; analytic prediction; database access
characterization; reference trace; sequential access;
workload management",
}
@Article{Peckham:1995:DME,
author = "Joan Peckham and Bonnie MacKellar and Michael
Doherty",
title = "Data Model for Extensible Support of Explicit
Relationships in Design Databases",
journal = j-VLDB-J,
volume = "4",
number = "2",
pages = "157--191",
month = apr,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:33 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/Doherty:Michael.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/MacKellar:Bonnie.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Peckham:Joan.html",
abstract = "We describe the conceptual model of SORAC, a data
modeling system developed at the University of Rhode
Island. SORAC supports both semantic objects and
relationships, and provides a tool for modeling
databases needed for complex design domains. SORAC's
set of built-in semantic relationships permits the
schema designer to specify enforcement rules that
maintain constraints on the object and relationship
types. SORAC then automatically generates C++ code to
maintain the specified enforcement rules, producing a
schema that is compatible with Ontos. This facilitates
the task of the schema designer, who no longer has to
ensure that all methods on object classes correctly
maintain necessary constraints. In addition, explicit
specification of enforcement rules permits automated
analysis of enforcement propagations. We compare the
interpretations of relationships within the semantic
and object-oriented models as an introduction to the
mixed model that SORAC supports. Next, the set of
built-in SORAC relationship types is presented in terms
of the enforcement rules permitted on each relationship
type. We then use the modeling requirements of an
architectural design support system, called
ArchObjects, to demonstrate the capabilities of SORAC.
The implementation of the current SORAC prototype is
also briefly discussed.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "computer-aided architectural design; database
constraints; relationship semantics; semantic and
object-oriented data modeling",
xxpages = "157--192",
}
@Article{Teniente:1995:UKB,
author = "Ernest Teniente and Antoni Oliv{\'e}",
title = "Updating Knowledge Bases While Maintaining Their
Consistency",
journal = j-VLDB-J,
volume = "4",
number = "2",
pages = "193--241",
month = apr,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:33 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/o/Oliv=eacute=:Antoni.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Teniente:Ernest.html",
abstract = "When updating a knowledge base, several problems may
arise. One of the most important problems is that of
integrity constraints satisfaction. The classic
approach to this problem has been to develop methods
for {\em checking\/} whether a given update violates an
integrity constraint. An alternative approach consists
of trying to repair integrity constraints violations by
performing additional updates that {\em maintain\/}
knowledge base consistency. Another major problem in
knowledge base updating is that of {\em view updating},
which determines how an update request should be
translated into an update of the underlying base facts.
We propose a new method for updating knowledge bases
while maintaining their consistency. Our method can be
used for both integrity constraints maintenance and
view updating. It can also be combined with any
integrity checking method for view updating and
integrity checking. The kind of updates handled by our
method are: updates of base facts, view updates,
updates of deductive rules, and updates of integrity
constraints. Our method is based on events and
transition rules, which explicitly define the
insertions and deletions induced by a knowledge base
update. Using these rules, an extension of the SLDNF
procedure allows us to obtain all possible minimal ways
of updating a knowledge base without violating any
integrity constraint.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "integrity checking; integrity maintenance; view
updating",
}
@Article{Guting:1995:RBS,
author = "Ralf Hartmut G{\"u}ting and Markus Schneider",
title = "Realm-Based Spatial Data Types: The {ROSE} Algebra",
journal = j-VLDB-J,
volume = "4",
number = "2",
pages = "243--286",
month = apr,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:33 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/G=uuml=ting:Ralf_Hartmut.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Schneider:Markus.html",
abstract = "Spatial data types or algebras for database systems
should (1) be fully general, that is, closed under set
operations, (2) have formally defined semantics, (3) be
defined in terms of finite representations available in
computers, (4) offer facilities to enforce geometric
consistency of related spatial objects, and (5) be
independent of a particular DBMS data model, but
cooperate with any. We present an algebra that uses
{\em realms\/} as geometric domains underlying spatial
data types. A realm, as a general database concept, is
a finite, dynamic, user-defined structure underlying
one or more system data types. Problems of numerical
robustness and topological correctness are solved
within and below the realm layer so that spatial
algebras defined above a realm have very nice algebraic
properties. Realms also interact with a DMBS to enforce
geometric consistency on object creation or update. The
ROSE algebra is defined on top of realms and offers
general types to represent point, line, and region
features, together with a comprehensive set of
operations. It is described within a polymorphic type
system and interacts with a DMBS data model and query
language through an abstract {\em object model
interface.} An example integration of ROSE into the
object-oriented data model $ O^2 $ and its query
language is presented.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "finite resolution; geometric consistency; numerical
robustness; object model interface; realm; topological
correctness",
}
@Article{Templeton:1995:IDC,
author = "Marjorie Templeton and Herbert Henley and Edward Maros
and Darrel J. {Van Buer}",
title = "{InterViso}: Dealing With the Complexity of Federated
Database Access",
journal = j-VLDB-J,
volume = "4",
number = "2",
pages = "287--317",
month = apr,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:33 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Buer:Darrel_J=_Van.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Henley:Herbert.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Maros:Edward.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Templeton:Marjorie.html",
abstract = "Connectivity products are finally available to provide
the `highways' between computers containing data. IBM
has provided strong validation of the concept with
their `Information Warehouse.' DBMS vendors are
providing gateways into their products, and SQL is
being retrofitted on many older DBMSs to make it easier
to access data from standard 4GL products and
application development systems. The next step needed
for data integration is to provide (1) a common data
dictionary with a conceptual schema across the data to
mask the many differences that occur when databases are
developed independently and (2) a server that can
access and integrate the databases using information
from the data dictionary. In this article, we discuss
InterViso, one of the first commercial federated
database products. InterViso is based on Mermaid, which
was developed at SDC and Unisys (Templeton et al.,
1987b). It provides a value added layer above
connectivity products to handle views across databases,
schema translation, and transaction management.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data warehouse; database integration; federated
database",
xxpages = "287--318",
}
@Article{Atkinson:1995:SIP,
author = "Malcolm P. Atkinson and Ronald Morrison",
title = "Special Issue on Persistent Object Systems:
Orthogonally Persistent Object Systems",
journal = j-VLDB-J,
volume = "4",
number = "3",
pages = "319--401",
month = jul,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 27 08:46:01 MDT 2000",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Atkinson:Malcolm_P=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Morrison:Ronald.html",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Atkinson:1995:OPO,
author = "Malcolm Atkinson and Ronald Morrison",
title = "Orthogonally persistent object systems",
journal = j-VLDB-J,
volume = "4",
number = "3",
pages = "319--402",
month = jul,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:34 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Persistent Application Systems (PASs) are of
increasing social and economic importance. They have
the potential to be long-lived, concurrently accessed,
and consist of large bodies of data and programs.
Typical examples of PASs are CAD/CAM systems, office
automation, CASE tools, software engineering
environments, and patient-care support systems in
hospitals. Orthogonally persistent object systems are
intended to provide improved support for the design,
construction, maintenance, and operation of PASs.
Persistence abstraction allows the creation and
manipulation of data in a manner that is independent of
its lifetime, thereby integrating the database view of
information with the programming language view. This
yields a number of advantages in terms of orthogonal
design and programmer productivity which are beneficial
for PASs. Design principles have been proposed for
persistent systems. By following these principles,
languages that provide persistence as a basic
abstraction have been developed. In this paper, the
motivation for orthogonal persistence is reviewed along
with the above mentioned design principles. The
concepts for integrating programming languages and
databases through the persistence abstraction, and
their benefits, are given. The technology to support
persistence, the achievements, and future directions of
persistence research are then discussed.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database programming languages; orthogonal
persistence; persistent application systems; persistent
programming languages",
}
@Article{Albano:1995:FPL,
author = "Antonio Albano and Giorgio Ghelli and Renzo Orsini",
title = "{Fibonacci}: a Programming Language for Object
Databases",
journal = j-VLDB-J,
volume = "4",
number = "3",
pages = "403--444",
month = jul,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:34 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Albano:Antonio.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Ghelli:Giorgio.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/o/Orsini:Renzo.html",
abstract = "Fibonacci is an object-oriented database programming
language characterized by static and strong typing, and
by new mechanisms for modeling databases in terms of
objects with roles, classes, and associations. A brief
introduction to the language is provided to present
those features, which are particularly suited to
modeling complex databases. Examples of the use of
Fibonacci are given with reference to the prototype
implementation of the language.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data models; database programming languages; objects
with roles",
}
@Article{Ozsu:1995:TUB,
author = "M. Tamer {\"O}zsu and Randal J. Peters and Duane
Szafron and Boman Irani and Anna Lipka and Adriana
Mu{\~n}oz",
title = "{TIGUKAT}: a Uniform Behavioral Objectbase Management
System",
journal = j-VLDB-J,
volume = "4",
number = "3",
pages = "445--492",
month = jul,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:34 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/=/=Ouml=zsu:M=_Tamer.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/i/Irani:Boman.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Lipka:Anna.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Mu=ntilde=oz:Adriana.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Peters:Randal_J=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Szafron:Duane.html",
abstract = "We describe the TIGUKAT objectbase management system,
which is under development at the Laboratory for
Database Systems Research at the University of Alberta.
TIGUKAT has a novel object model, whose identifying
characteristics include a purely behavioral semantics
and a uniform approach to objects. Everything in the
system, including types, classes, collections,
behaviors, and functions, as well as meta-information,
is a first-class object with well-defined behavior. In
this way, the model abstracts everything, including
traditional structural notions such as instance
variables, method implementation, and schema
definition, into a uniform semantics of behaviors on
objects. Our emphasis in this article is on the object
model, its implementation, the persistence model, and
the query language. We also (briefly) present other
database management functions that are under
development such as the query optimizer, the version
control system, and the transaction manager.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database management; objectbase management; persistent
storage system; reflective system",
}
@Article{Benzaken:1995:TDP,
author = "V{\'e}ronique Benzaken and Anne Doucet",
title = "{Th{\'e}mis}: a Database Programming Language Handling
Integrity Constraints",
journal = j-VLDB-J,
volume = "4",
number = "3",
pages = "493--517",
month = jul,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:34 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Benzaken:V=eacute=ronique.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/Doucet:Anne.html",
abstract = "This article presents a database programming language,
Th{\'e}mis, which supports subtyping and class
hierarchies, and allows for the definition of integrity
constraints in a global and declarative way. We first
describe the salient features of the language: types,
names, classes, integrity constraints (including
methods), and transactions. The inclusion of methods
into integrity constraints allows an increase of the
declarative power of these constraints. Indeed, the
information needed to define a constraint is not always
stored in the database through attributes, but is
sometimes computed or derived data. Then, we address
the problem of efficiently checking constraints. More
specifically, we consider two different problems: (1)
statically reducing the number of constraints to be
checked, and (2) generating an efficient run-time
checker. Using simple strategies, one can significantly
improve the efficiency of the verification. We show how
to reduce the number of constraints to be checked by
characterizing the portions of the database that are
involved in both the constraints and in a transaction.
We also show how to generate efficient algorithms for
checking a large class of constraints. We show how all
the techniques presented exploit the underlying type
system, which provides significant help in solving (1)
and \1. Last, the current status of the Th{\'e}mis
prototype is presented.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database programming language; integrity constraints;
program analysis",
}
@Article{Kemper:1995:APS,
author = "Alfons Kemper and Donald Kossmann",
title = "Adaptable Pointer Swizzling Strategies in Object
Bases: Design, Realization, and Quantitative Analysis",
journal = j-VLDB-J,
volume = "4",
number = "3",
pages = "519--566",
month = jul,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:34 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kemper:Alfons.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kossmann:Donald.html",
abstract = "In this article, different techniques for {\em
`pointer swizzling'\/} are classified and evaluated for
optimizing the access to main-memory resident
persistent objects. To speed up the access along
inter-object references, the persistent pointers in the
form of unique object identifiers (OIDs) are
transformed (swizzled) into main-memory pointers
(addresses). Pointer swizzling techniques can be
divided into two classes: (1) those that allow
replacement of swizzled objects from the buffer before
the end of an application program, and (2) those that
rule out the displacement of swizzled objects. The
first class (i.e., techniques that take `precautions'
for the replacement of swizzled objects) has not yet
been thoroughly investigated. Four different pointer
swizzling techniques allowing object replacement are
investigated and compared with the performance of an
object manager employing no pointer swizzling. The
extensive qualitative and quantitative
evaluation---only part of which could be presented in
this article---demonstrate that there is no {\em one\/}
superior pointer swizzling strategy for {\em all\/}
application profiles. Therefore, an adaptable object
base run-time system is devised that employs the full
range of pointer swizzling strategies, depending on the
application profile characteristics that are determined
by, for example, monitoring in combination with
sampling, user specifications, and/or program
analysis.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "object-oriented database systems; performance
evaluation; pointer swizzling",
xxpages = "519--567",
}
@Article{Anonymous:1995:SIP,
author = "Anonymous",
title = "Special issue on persistent object systems",
journal = j-VLDB-J,
volume = "4",
number = "3",
pages = "??--??",
month = jul,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:34 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Barbara:1995:SSO,
author = "Daniel Barbar{\'a} and Tomasz Imielinski",
title = "Special System-oriented Section: The Best of {SIGMOD}
1994: Sleepers and Workaholics: Caching Strategies in
Mobile Environments",
journal = j-VLDB-J,
volume = "4",
number = "4",
pages = "567--602",
month = oct,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 27 08:46:01 MDT 2000",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Barbar=aacute=:Daniel.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/i/Imielinski:Tomasz.html",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Barbara:1995:SWC,
author = "Daniel Barbar{\'a} and Tomasz Imieli{\'n}ski",
title = "Sleepers and workaholics: caching strategies in mobile
environments (extended version)",
journal = j-VLDB-J,
volume = "4",
number = "4",
pages = "567--602",
month = oct,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:35 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In the mobile wireless computing environment of the
future, a large number of users, equipped with
low-powered palmtop machines, will query databases over
wireless communication channels. Palmtop-based units
will often be disconnected for prolonged periods of
time, due to battery power saving measures; palmtops
also will frequently relocate between different cells,
and will connect to different data servers at different
times. Caching of frequently accessed data items will
be an important technique that will reduce contention
on the narrow-bandwidth, wireless channel. However,
cache individualization strategies will be severely
affected by the disconnection and mobility of the
clients. The server may no longer know which clients
are currently residing under its cell, and which of
them are currently on. We propose a taxonomy of
different cache invalidation strategies, and study the
impact of clients' disconnection times on their
performance. We study ways to improve further the
efficiency of the invalidation techniques described. We
also describe how our techniques can be implemented
over different network environments.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "caching; data management; information services;
wireless",
}
@Article{Nyberg:1995:ACS,
author = "Chris Nyberg and Tom Barclay and Zarka Cvetanovic and
Jim Gray and David B. Lomet",
title = "{AlphaSort}: a Cache-Sensitive Parallel External
Sort",
journal = j-VLDB-J,
volume = "4",
number = "4",
pages = "603--627",
month = oct,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:35 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Barclay:Tom.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Cvetanovic:Zarka.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Gray:Jim.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Lomet:David_B=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/n/Nyberg:Chris.html",
abstract = "A new sort algorithm, called AlphaSort, demonstrates
that commodity processors and disks can handle
commercial batch workloads. Using commodity processors,
memory, and arrays of SCSI disks, AlphaSort runs the
industry-standard sort benchmark in seven seconds. This
beats the best published record on a 32-CPU 32-disk
Hypercube by 8:1. On another benchmark, AlphaSort
sorted more than a gigabyte in one minute. AlphaSort is
a cache-sensitive, memory-intensive sort algorithm. We
argue that modern architectures require algorithm
designers to re-examine their use of the memory
hierarchy. AlphaSort uses clustered data structures to
get good cache locality, file striping to get high disk
bandwidth, QuickSort to generate runs, and
replacement-selection to merge the runs. It uses shared
memory multiprocessors to break the sort into subsort
chores. Because startup times are becoming a
significant part of the total time, we propose two new
benchmarks: (1) MinuteSort: how much can you sort in
one minute, and (2) PennySort: how much can you sort
for one penny.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Alpha; cache; DEC 7000; disk; memory; parallel; sort;
striping",
xxpages = "603--628",
}
@Article{White:1995:QHP,
author = "Seth J. White and David J. DeWitt",
title = "{QuickStore}: a High Performance Mapped Object Store",
journal = j-VLDB-J,
volume = "4",
number = "4",
pages = "629--673",
month = oct,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:35 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/DeWitt:David_J=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/w/White:Seth_J=.html",
abstract = "QuickStore is a memory-mapped storage system for
persistent C++, built on top of the EXODUS Storage
Manager. QuickStore provides fast access to in-memory
objects by allowing application programs to access
objects via normal virtual memory pointers. This
article presents the results of a detailed performance
study using the OO7 benchmark. The study compares the
performance of QuickStore with the latest
implementation of the E programming language. The
QuickStore and E systems exemplify the two basic
approaches (hardware and software) that have been used
to implement persistence in object-oriented database
systems. In addition, both systems use the same
underlying storage manager and compiler, allowing us to
make a truly apples-to-apples comparison of the
hardware and software techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "benchmark; client-server; memory-mapped;
object-oriented; performance; pointer swizzling",
}
@Article{Swami:1995:EPF,
author = "Arun N. Swami and K. Bernhard Schiefer",
title = "Estimating Page Fetches for Index Scans with Finite
{LRU} Buffers",
journal = j-VLDB-J,
volume = "4",
number = "4",
pages = "675--701",
month = oct,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:35 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Schiefer:K=_Bernhard.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Swami:Arun_N=.html",
abstract = "We describe an algorithm for estimating the number of
page fetches for a partial or complete scan of a B-tree
index. The algorithm obtains estimates for the number
of page fetches for an index scan when given the number
of tuples selected and the number of LRU buffers
currently available. The algorithm has an initial phase
that is performed exactly once before any estimates are
calculated. This initial phase, involving LRU buffer
modeling, requires a scan of all the index entries and
calculates the number of page fetches for different
buffer sizes. An approximate empirical model is
obtained from this data. Subsequently, an inexpensive
estimation procedure is called by the query optimizer
whenever it needs an estimate of the page fetches for
the index scan. This procedure utilizes the empirical
model obtained in the initial phase.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "estimation; index scan; LRU; query optimization",
}
@Article{Landau:1995:HQA,
author = "Gad M. Landau and Jeanette P. Schmidt and Vassilis J.
Tsotras",
title = "Historical queries along multiple lines of time
evolution",
journal = j-VLDB-J,
volume = "4",
number = "4",
pages = "703--726",
month = oct,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:35 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Traditional approaches to addressing historical
queries assume a {\em single\/} line of time evolution;
that is, a system (database, relation) evolves over
time through a sequence of transactions. Each
transaction always applies to the unique, current state
of the system, resulting in a new current state. There
are, however, complex applications where the system's
state evolves into {\em multiple\/} lines of evolution.
In general, this creates a tree (hierarchy) of
evolution lines, where each tree node represents the
time evolution of a particular subsystem. Multiple
lines create novel historical queries, such as {\em
vertical\/} or {\em horizontal\/} historical queries.
The key characteristic of these problems is that
portions of the history are shared; answering
historical queries should not necessitate duplication
of shared histories as this could increase the storage
requirements dramatically. Both the vertical and
horizontal historical queries have two parts: a
`search' part, where the time of interest is located
together with the appropriate subsystem, and a
reconstruction part, where the subsystem's state is
reconstructed for that time. This article focuses on
the search part; several reconstruction methods,
designed for single evolution lines can be applied once
the appropriate time of interest is located. For both
the vertical and the horizontal historical queries, we
present algorithms that work without duplicating shared
histories. Combinations of the vertical and horizontal
queries are possible, and enable searching in both
dimensions of the tree of evolutions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access methods; CAD databases; data-structures;
rollback databases",
}
@Article{Landau:1995:RJA,
author = "Gad M. Landau and Jeanette P. Schmidt and Vassilis J.
Tsotras",
title = "Regular Journal Articles: Historical Queries Along
Multiple Lines of Time Evolution",
journal = j-VLDB-J,
volume = "4",
number = "4",
pages = "703--726",
month = oct,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 27 08:46:01 MDT 2000",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Landau:Gad_M=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Schmidt:Jeanette_P=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Tsotras:Vassilis_J=.html",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Abiteboul:1995:PLM,
author = "Serge Abiteboul and Catriel Beeri",
title = "The Power of Languages for the Manipulation of Complex
Values",
journal = j-VLDB-J,
volume = "4",
number = "4",
pages = "727--794",
month = oct,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:35 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb4.html;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Abiteboul:Serge.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Beeri:Catriel.html",
abstract = "Various models and languages for describing and
manipulating hierarchically structured data have been
proposed. Algebraic, calculus-based, and
logic-programming oriented languages have all been
considered. This article presents a general model for
complex values (i.e., values with hierarchical
structures), and languages for it based on the three
paradigms. The algebraic language generalizes those
presented in the literature; it is shown to be related
to the functional style of programming advocated by
Backus (1978). The notion of domain independence (from
relational databases) is defined, and syntactic
restrictions (referred to as safety conditions) on
calculus queries are formulated to guarantee domain
independence. The main results are: The
domain-independent calculus, the safe calculus, the
algebra, and the logic-programming oriented language
have equivalent expressive power. In particular,
recursive queries, such as the transitive closure, can
be expressed in each of the languages. For this result,
the algebra needs the powerset operation. A more
restricted version of safety is presented, such that
the restricted safe calculus is equivalent to the
algebra without the powerset. The results are extended
to the case where arbitrary functions and predicates
are used in the languages.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "complex object; complex value; database; database
model; query language",
}
@Article{Anonymous:1995:SSO,
author = "Anonymous",
title = "Special system-oriented section: the best of {SIGMOD}
`94",
journal = j-VLDB-J,
volume = "4",
number = "4",
pages = "??--??",
month = oct,
year = "1995",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:35 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{DeWitt:1996:POT,
author = "David J. {De Witt} and Jeffrey F. Naughton and John C.
Shafer and Shivakumar Venkataraman",
title = "Parallelizing {OODBMS} traversals: a performance
evaluation",
journal = j-VLDB-J,
volume = "5",
number = "1",
pages = "3--18",
month = jan,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:36 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/DeWitt:David_J=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/n/Naughton:Jeffrey_F=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Shafer:John_C=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/v/Venkataraman:Shivakumar.html;
http://link.springer.de/link/service/journals/00778/bibs/6005001/60050003.htm;
http://link.springer.de/link/service/journals/00778/papers/6005001/60050003.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005001/60050003.ps.gz",
abstract = "In this paper we describe the design and
implementation of {\em ParSets}, a means of exploiting
parallelism in the SHORE OODBMS. We used ParSets to
parallelize the graph traversal portion of the OO7
OODBMS benchmark, and present speedup and scaleup
results from parallel SHORE running these traversals on
a cluster of commodity workstations connected by a
standard Ethernet. For some OO7 traversals, SHORE
achieved excellent speedup and scaleup; for other OO7
traversals, only marginal speedup and scaleup occurred.
The characteristics of these traversals shed light on
when the ParSet approach to parallelism can and cannot
be applied to speed up an application.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Object-oriented database management systems;
Parallelism; ParSets; SHORE",
}
@Article{Sivasankaran:1996:PAR,
author = "Rajendran M. Sivasankaran and John A. Stankovic and
Donald F. Towsley and Bhaskar Purimetla and Krithi
Ramamritham",
title = "Priority Assignment in Real-Time Active Databases",
journal = j-VLDB-J,
volume = "5",
number = "1",
pages = "19--34",
month = jan,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:36 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Purimetla:Bhaskar.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Ramamritham:Krithi.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sivasankaran:Rajendran_M=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Stankovic:John_A=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Towsley:Donald_F=.html;
http://link.springer.de/link/service/journals/00778/bibs/6005001/60050019.htm;
http://link.springer.de/link/service/journals/00778/papers/6005001/60050019.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005001/60050019.ps.gz",
abstract = "Active databases and real-time databases have been
important areas of research in the recent past. It has
been recognized that many benefits can be gained by
integrating real-time and active database technologies.
However, not much work has been done in the area of
transaction processing in real-time active databases.
This paper deals with an important aspect of
transaction processing in real-time active databases,
namely the problem of assigning priorities to
transactions. In these systems, time-constrained
transactions trigger other transactions during their
execution. We present three policies for assigning
priorities to parent, immediate and deferred
transactions executing on a multiprocessor system and
then evaluate the policies through simulation. The
policies use different amounts of semantic information
about transactions to assign the priorities. The
simulator has been validated against the results of
earlier published studies. We conducted experiments in
three settings: a task setting, a main memory database
setting and a disk-resident database setting. Our
results demonstrate that dynamically changing the
priorities of transactions, depending on their behavior
(triggering rules), yields a substantial improvement in
the number of triggering transactions that meet their
deadline in all three settings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Active databases; Coupling mode; Deadlines;
ECA-priority assignment; Real-time databases",
}
@Article{Keller:1996:PBC,
author = "Arthur M. Keller and Julie Basu",
title = "A Predicate-based Caching Scheme for Client-Server
Database Architectures",
journal = j-VLDB-J,
volume = "5",
number = "1",
pages = "35--47",
month = jan,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:36 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Basu:Julie.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Keller:Arthur_M=.html;
http://link.springer.de/link/service/journals/00778/bibs/6005001/60050035.htm;
http://link.springer.de/link/service/journals/00778/papers/6005001/60050035.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005001/60050035.ps.gz",
abstract = "We propose a new client-side data-caching scheme for
relational databases with a central server and multiple
clients. Data are loaded into each client cache based
on queries executed on the central database at the
server. These queries are used to form predicates that
describe the cache contents. A subsequent query at the
client may be satisfied in its local cache if we can
determine that the query result is entirely contained
in the cache. This issue is called {\em cache
completeness}. A separate issue, {\em cache currency},
deals with the effect on client caches of updates
committed at the central database. We examine the
various performance tradeoffs and optimization issues
involved in addressing the questions of cache currency
and completeness using predicate descriptions and
suggest solutions that promote good dynamic behavior.
Lower query-response times, reduced message traffic,
higher server throughput, and better scalability are
some of the expected benefits of our approach over
commonly used relational server-side and object
ID-based or page-based client-side caching.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "cache completeness; cache currency; caching; multiple
clients; relational databases",
}
@Article{Stonebraker:1996:MWA,
author = "Michael Stonebraker and Paul M. Aoki and Witold Litwin
and Avi Pfeffer and Adam Sah and Jeff Sidell and Carl
Staelin and Andrew Yu",
title = "{Mariposa}: a Wide-Area Distributed Database System",
journal = j-VLDB-J,
volume = "5",
number = "1",
pages = "48--63",
month = jan,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:36 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Aoki:Paul_M=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Litwin:Witold.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Pfeffer:Avi.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sah:Adam.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sidell:Jeff.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Staelin:Carl.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Stonebraker:Michael.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/y/Yu:Andrew.html;
http://link.springer.de/link/service/journals/00778/bibs/6005001/60050048.htm;
http://link.springer.de/link/service/journals/00778/papers/6005001/60050048.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005001/60050048.ps.gz",
abstract = "The requirements of wide-area distributed database
systems differ dramatically from those of local-area
network systems. In a wide-area network (WAN)
configuration, individual sites usually report to
different system administrators, have different access
and charging algorithms, install site-specific data
type extensions, and have different constraints on
servicing remote requests. Typical of the last point
are production transaction environments, which are
fully engaged during normal business hours, and cannot
take on additional load. Finally, there may be many
sites participating in a WAN distributed DBMS.In this
world, a single program performing global query
optimization using a cost-based optimizer will not work
well. Cost-based optimization does not respond well to
site-specific type extension, access constraints,
charging algorithms, and time-of-day constraints.
Furthermore, traditional cost-based distributed
optimizers do not scale well to a large number of
possible processing sites. Since traditional
distributed DBMSs have all used cost-based optimizers,
they are not appropriate in a WAN environment, and a
new architecture is required. We have proposed and
implemented an economic paradigm as the solution to
these issues in a new distributed DBMS called Mariposa.
In this paper, we present the architecture and
implementation of Mariposa and discuss early feedback
on its operating characteristics.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "autonomy; databases; distributed systems; economic
site; name service; wide-area network",
}
@Article{Harris:1996:JAC,
author = "Evan P. Harris and Kotagiri Ramamohanarao",
title = "Join Algorithm Costs Revisited",
journal = j-VLDB-J,
volume = "5",
number = "1",
pages = "64--84",
month = jan,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:36 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Harris:Evan_P=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Ramamohanarao:Kotagiri.html;
http://link.springer.de/link/service/journals/00778/bibs/6005001/60050064.htm;
http://link.springer.de/link/service/journals/00778/papers/6005001/60050064.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005001/60050064.ps.gz",
abstract = "A method of analysing join algorithms based upon the
time required to access, transfer and perform the
relevant CPU-based operations on a disk page is
proposed. The costs of variations of several of the
standard join algorithms, including nested block,
sort-merge, GRACE hash and hybrid hash, are presented.
For a given total buffer size, the cost of these join
algorithms depends on the parts of the buffer allocated
for each purpose. For example, when joining two
relations using the nested block join algorithm, the
amount of buffer space allocated for the outer and
inner relations can significantly affect the cost of
the join. Analysis of expected and experimental results
of various join algorithms show that a combination of
the optimal nested block and optimal GRACE hash join
algorithms usually provide the greatest cost benefit,
unless the relation size is a small multiple of the
memory size. Algorithms to quickly determine a buffer
allocation producing the minimal cost for each of these
algorithms are presented. When the relation size is a
small multiple of the amount of main memory available
(typically up to three to six times), the hybrid hash
join algorithm is preferable.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "join algorithms; minimisation; optimal buffer
allocation",
}
@Article{Ramamritham:1996:TCC,
author = "Krithi Ramamritham and Panos K. Chrysanthis",
title = "A taxonomy of correctness criteria in database
applications (*)",
journal = j-VLDB-J,
volume = "5",
number = "1",
pages = "85--97",
month = jan,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:36 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Chrysanthis:Panos_K=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Ramamritham:Krithi.html;
http://link.springer.de/link/service/journals/00778/bibs/6005001/60050085.htm;
http://link.springer.de/link/service/journals/00778/papers/6005001/60050085.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005001/60050085.ps.gz",
abstract = "Whereas serializability captures {\em database
consistency requirements\/} and {\em transaction
correctness properties\/} via a single notion, recent
research has attempted to come up with correctness
criteria that view these two types of requirements
independently. The search for more flexible correctness
criteria is partly motivated by the introduction of new
transaction models that extend the traditional atomic
transaction model. These extensions came about because
the atomic transaction model in conjunction with
serializability is found to be very constraining when
used in advanced applications (e.g., design databases)
that function in distributed, cooperative, and
heterogeneous environments. In this article we develop
a taxonomy of various {\em correctness criteria\/} that
focus on database consistency requirements and
transaction correctness properties from the viewpoint
of {\em what\/} the different dimensions of these two
are. This taxonomy allows us to categorize correctness
criteria that have been proposed in the literature. To
help in this categorization, we have applied a uniform
specification technique, based on ACTA, to express the
various criteria. Such a categorization helps shed
light on the similarities and differences between
different criteria and places them in perspective.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "concurrency control; database correctness criteria;
formal specifications; transaction processing",
}
@Article{Tsatalos:1996:GVT,
author = "Odysseas G. Tsatalos and Marvin H. Solomon and Yannis
E. Ioannidis",
title = "The {GMAP}: a Versatile Tool for Physical Data
Independence",
journal = j-VLDB-J,
volume = "5",
number = "2",
pages = "101--118",
month = apr,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:38 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/i/Ioannidis:Yannis_E=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Solomon:Marvin_H=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Tsatalos:Odysseas_G=.html;
http://link.springer.de/link/service/journals/00778/bibs/6005002/60050101.htm;
http://link.springer.de/link/service/journals/00778/papers/6005002/60050101.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005002/60050101.ps.gz",
abstract = "Physical data independence is touted as a central
feature of modern database systems. It allows users to
frame queries in terms of the logical structure of the
data, letting a query processor automatically translate
them into optimal plans that access physical storage
structures. Both relational and object-oriented
systems, however, force users to frame their queries in
terms of a logical schema that is directly tied to
physical structures. We present an approach that
eliminates this dependence. All storage structures are
defined in a declarative language based on relational
algebra as functions of a logical schema. We present an
algorithm, integrated with a conventional query
optimizer, that translates queries over this logical
schema into plans that access the storage structures.
We also show how to compile update requests into plans
that update all relevant storage structures
consistently and optimally. Finally, we report on
experiments with a prototype implementation of our
approach that demonstrate how it allows storage
structures to be tuned to the expected or observed
workload to achieve significantly better performance
than is possible with conventional techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "indexing; materialized views; physical data
independence; physical database design",
}
@Article{Poulovassilis:1996:AQO,
author = "Alexandra Poulovassilis and Carol Small",
title = "Algebraic Query Optimisation for Database Programming
Languages",
journal = j-VLDB-J,
volume = "5",
number = "2",
pages = "119--132",
month = apr,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:38 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Poulovassilis:Alexandra.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Small:Carol.html;
http://link.springer.de/link/service/journals/00778/bibs/6005002/60050119.htm;
http://link.springer.de/link/service/journals/00778/papers/6005002/60050119.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005002/60050119.ps.gz",
abstract = "A major challenge still facing the designers and
implementors of database programming languages (DBPLs)
is that of query optimisation. We investigate algebraic
query optimisation techniques for DBPLs in the context
of a purely declarative functional language that
supports sets as first-class objects. Since the
language is computationally complete issues such as
non-termination of expressions and construction of
infinite data structures can be investigated, whilst
its declarative nature allows the issue of side effects
to be avoided and a richer set of equivalences to be
developed. The language has a well-defined semantics
which permits us to reason formally about the
properties of expressions, such as their equivalence
with other expressions and their termination. The
support of a set bulk data type enables much prior work
on the optimisation of relational languages to be
utilised. In the paper we first give the syntax of our
archetypal DBPL and briefly discuss its semantics. We
then define a small but powerful algebra of operators
over the set data type, provide some key equivalences
for expressions in these operators, and list
transformation principles for optimising expressions.
Along the way, we identify some caveats to well-known
equivalences for non-deductive database languages. We
next extend our language with two higher level
constructs commonly found in functional DBPLs: set
comprehensions and functions with known inverses. Some
key equivalences for these constructs are provided, as
are transformation principles for expressions in them.
Finally, we investigate extending our equivalences for
the set operators to the analogous operators over bags.
Although developed and formally proved in the context
of a functional language, our findings are directly
applicable to other DBPLs of similar expressiveness.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "algebraic manipulation; database management; database
programming languages; functional languages; query
optimisation",
}
@Article{Amiel:1996:TSR,
author = "Eric Amiel and Marie-Jo Bellosta and Eric Dujardin and
Eric Simon",
title = "Type-safe Relaxing of Schema Consistency Rules for
Flexible Modeling in {OODBMS}",
journal = j-VLDB-J,
volume = "5",
number = "2",
pages = "133--150",
month = apr,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:38 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Amiel:Eric.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Bellosta:Marie=Jo.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/Dujardin:Eric.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Simon:Eric.html;
http://link.springer.de/link/service/journals/00778/bibs/6005002/60050133.htm;
http://link.springer.de/link/service/journals/00778/papers/6005002/60050133.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005002/60050133.ps.gz",
abstract = "Object-oriented databases enforce behavioral schema
consistency rules to guarantee type safety, i.e., that
no run-time type error can occur. When the schema must
evolve, some schema updates may violate these rules. In
order to maintain behavioral schema consistency,
traditional solutions require significant changes to
the types, the type hierarchy and the code of existing
methods. Such operations are very expensive in a
database context. To ease schema evolution, we propose
to support exceptions to the behavioral consistency
rules without sacrificing type safety. The basic idea
is to detect unsafe statements in a method code at
compile-time and check them at run-time. The run-time
check is performed by a specific clause that is
automatically inserted around unsafe statements. This
check clause warns the programmer of the safety problem
and lets him provide exception-handling code. Schema
updates can therefore be performed with only minor
changes to the code of methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "contravariance; covariance; object-oriented databases;
schema evolution; type safety",
xxtitle = "Type-safe relaxing of schema consistency rules for
flexible modelling in {OODBMS}",
}
@Article{Fang:1996:EOB,
author = "Doug Fang and Shahram Ghandeharizadeh and Dennis
McLeod",
title = "An experimental object-based sharing system for
networked databases",
journal = j-VLDB-J,
volume = "5",
number = "2",
pages = "151--165",
month = apr,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:38 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/f/Fang:Doug.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Ghandeharizadeh:Shahram.html;
http://link.springer.de/link/service/journals/00778/bibs/6005002/60050151.htm;
http://link.springer.de/link/service/journals/00778/papers/6005002/60050151.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005002/60050151.ps.gz;
http://link.springer.de/link/service/journals/00778/tocs/mailto: HREF="mailto:helpdesk@link.springer.de">helpdesk@link.springer.de",
abstract = "An approach and mechanism for the transparent sharing
of objects in an environment of interconnected
(networked), autonomous database systems is presented.
An experimental prototype system has been designed and
implemented, and an analysis of its performance
conducted. Previous approaches to sharing in this
environment typically rely on the use of a global,
integrated conceptual database schema; users and
applications must pose queries at this new level of
abstraction to access remote information. By contrast,
our approach provides a mechanism that allows users to
import remote objects directly into their local
database transparently; access to remote objects is
virtually the same as access to local objects. The
experimental prototype system that has been designed
and implemented is based on the Iris and Omega
object-based database management systems; this system
supports the sharing of data and meta-data objects
(information units) as well as units of behavior. The
results of experiments conducted to evaluate the
performance of our mechanism demonstrate the
feasibility of database transparent object sharing in a
federated environment, and provide insight into the
performance overhead and tradeoffs involved.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database system interoperability; experimental
prototype benchmarking; object sharing",
xxtitle = "An Experimental System for Object-Based Sharing in
Federated Databases",
}
@Article{Dey:1996:CTR,
author = "Debabrata Dey and Terence M. Barron and Veda C.
Storey",
title = "A Complete Temporal Relational Algebra",
journal = j-VLDB-J,
volume = "5",
number = "3",
pages = "167--180",
month = aug,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:39 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Barron:Terence_M=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/Dey:Debabrata.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Storey:Veda_C=.html;
http://link.springer.de/link/service/journals/00778/bibs/6005003/60050167.htm;
http://link.springer.de/link/service/journals/00778/papers/6005003/60050167.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005003/60050167.ps.gz",
abstract = "Various temporal extensions to the relational model
have been proposed. All of these, however, deviate
significantly from the original relational model. This
paper presents a temporal extension of the relational
algebra that is not significantly different from the
original relational model, yet is at least as
expressive as any of the previous approaches. This
algebra employs multidimensional tuple time-stamping to
capture the complete temporal behavior of data. The
basic relational operations are redefined as consistent
extensions of the existing operations in a manner that
preserves the basic algebraic equivalences of the
snapshot (i.e., conventional static) algebra. A new
operation, namely {\em temporal projection}, is
introduced. The complete update semantics are formally
specified and aggregate functions are defined. The
algebra is closed, and reduces to the snapshot algebra.
It is also shown to be at least as expressive as the
calculus-based temporal query language TQuel. In order
to assess the algebra, it is evaluated using a set of
twenty-six criteria proposed in the literature, and
compared to existing temporal relational algebras. The
proposed algebra appears to satisfy more criteria than
any other existing algebra.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "historical databases; relational algebra; temporal
databases; transaction time; valid time",
remark = "Check month: July or August??",
}
@Article{Shyy:1996:DIK,
author = "Yuh-Ming Shyy and Javier Arroyo and Stanley Y. W. Su
and Herman Lam",
title = "The Design and Implementation of {K}: a High-Level
Knowledge-Base Programming Language of {OSAM*.KBMS}",
journal = j-VLDB-J,
volume = "5",
number = "3",
pages = "181--195",
month = aug,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:39 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Arroyo:Javier.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Lam:Herman.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Shyy:Yuh=Ming.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Su:Stanley_Y=_W=.html;
http://link.springer.de/link/service/journals/00778/bibs/6005003/60050181.htm;
http://link.springer.de/link/service/journals/00778/papers/6005003/60050181.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005003/60050181.ps.gz",
abstract = "The OSAM*.KBMS is a knowledge-base management system,
or the so-called next-generation database management
system, for non-traditional data/knowledge-intensive
applications. In order to define, query, and manipulate
a knowledge base, as well as to write codes to
implement any application system, we have developed an
object-oriented knowledge-base programming language
called K to serve as the high-level interface of
OSAM*.KBMS. This paper presents the design of K, its
implementation, and its supporting KBMS developed at
the Database Systems Research and Development Center of
the University of Florida.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "abstractions; association patterns; knowledge-base
programming language; object-oriented knowledge model;
structural associations",
remark = "Check month: July or August??",
}
@Article{Harder:1996:APS,
author = "Theo H{\"a}rder and Joachim Reinert",
title = "Access Path Support for Referential Integrity in
{SQL2}",
journal = j-VLDB-J,
volume = "5",
number = "3",
pages = "196--214",
month = aug,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:39 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/H=auml=rder:Theo.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Reinert:Joachim.html;
http://link.springer.de/link/service/journals/00778/bibs/6005003/60050196.htm;
http://link.springer.de/link/service/journals/00778/papers/6005003/60050196.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005003/60050196.ps.gz",
abstract = "The relational model of data incorporates fundamental
assertions for entity integrity and referential
integrity. Recently, these so-called relational
invariants were more precisely specified by the new
SQL2 standard. Accordingly, they have to be guaranteed
by a relational DBMS to its users and, therefore, all
issues of semantics and implementation became very
important. The specification of referential integrity
embodies quite a number of complications including the
MATCH clause and a collection of referential actions.
In particular, $ \hbox {{\tt MATCH PARTIAL}} $ turns
out to be hard to understand and, if applied, difficult
and expensive to maintain. In this paper, we identify
the functional requirements for preserving referential
integrity. At a level free of implementational
considerations, the number and kinds of searches
necessary for referential integrity maintenance are
derived. Based on these findings, our investigation is
focused on the question of how the functional
requirements can be supported by implementation
concepts in an efficient way. We determine the search
cost for referential integrity maintenance (in terms of
page references) for various possible access path
structures. Our main result is that a combined access
path structure is the most appropriate for checking the
regular MATCH option, whereas $ \hbox {{\tt MATCH
PARTIAL}} $ requires very expensive and complicated
check procedures. If it cannot be avoided at all, the
best support is achieved by a combination of multiple $
\mbox {B}^*$-trees.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access path support; MATCH clause; referential
integrity; relational databases; SQL2",
remark = "Check month: July or August??",
}
@Article{Ooi:1996:INE,
author = "Beng Chin Ooi and Jiawei Han and Hongjun Lu and Kian
Lee Tan",
title = "Index Nesting --- An Efficient Approach to Indexing in
Object-Oriented Databases",
journal = j-VLDB-J,
volume = "5",
number = "3",
pages = "215--228",
month = aug,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:39 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Han:Jiawei.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Lu:Hongjun.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/o/Ooi:Beng_Chin.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Tan:Kian=Lee.html;
http://link.springer.de/link/service/journals/00778/bibs/6005003/60050215.htm;
http://link.springer.de/link/service/journals/00778/papers/6005003/60050215.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005003/60050215.ps.gz;
http://link.springer.de/link/service/journals/00778/tocs/mailto: HREF="mailto:helpdesk@link.springer.de">helpdesk@link.springer.de",
abstract = "In object-oriented database systems where the concept
of the superclass-subclass is supported, an instance of
a subclass is also an instance of its superclass.
Consequently, the access scope of a query against a
class in general includes the access scope of all its
subclasses, unless specified otherwise. An index to
support superclass-subclass relationship efficiently
must provide efficient associative retrievals of
objects from a single class or from several classes in
a class hierarchy. This paper presents an efficient
index called the hierarchical tree (the H-tree). For
each class, an H-tree is maintained, allowing efficient
search on a single class. These H-trees are
appropriately linked to capture the superclass-subclass
relationships, thus allowing efficient retrievals of
instances from a class hierarchy. Both experimental and
analytical results indicate that the H-tree is an
efficient indexing structure.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "indexing structures; OODB; query retrieval",
remark = "Check month: July or August??",
}
@Article{Antoshenkov:1996:QPO,
author = "Gennady Antoshenkov and Mohamed Ziauddin",
title = "Query Processing and Optimization in {Oracle Rdb}",
journal = j-VLDB-J,
volume = "5",
number = "4",
pages = "229--237",
month = dec,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:39 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Antoshenkov:Gennady.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/z/Ziauddin:Mohamed.html;
http://link.springer.de/link/service/journals/00778/bibs/6005004/60050229.htm;
http://link.springer.de/link/service/journals/00778/papers/6005004/60050229.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005004/60050229.ps.gz",
abstract = "This paper contains an overview of the technology used
in the query processing and optimization component of
Oracle Rdb, a relational database management system
originally developed by Digital Equipment Corporation
and now under development by Oracle Corporation. Oracle
Rdb is a production system that supports the most
demanding database applications, runs on multiple
platforms and in a variety of environments.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "dynamic optimization; optimizer; query transformation;
relational database; sampling",
}
@Article{Mylopoulos:1996:BKB,
author = "John Mylopoulos and Vinay K. Chaudhri and Dimitris
Plexousakis and Adel Shrufi and Thodoros Topologlou",
title = "Building Knowledge Base Management Systems",
journal = j-VLDB-J,
volume = "5",
number = "4",
pages = "238--263",
month = dec,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:39 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Chaudhri:Vinay_K=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Mylopoulos:John.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Plexousakis:Dimitris.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Shrufi:Adel.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Topaloglou:Thodoros.html;
http://link.springer.de/link/service/journals/00778/bibs/6005004/60050238.htm;
http://link.springer.de/link/service/journals/00778/papers/6005004/60050238.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005004/60050238.ps.gz",
abstract = "Advanced applications in fields such as CAD, software
engineering, real-time process control, corporate
repositories and digital libraries require the
construction, efficient access and management of large,
shared knowledge bases. Such knowledge bases cannot be
built using existing tools such as expert system
shells, because these do not scale up, nor can they be
built in terms of existing database technology, because
such technology does not support the rich
representational structure and inference mechanisms
required for knowledge-based systems. This paper
proposes a generic architecture for a knowledge base
management system intended for such applications. The
architecture assumes an object-oriented knowledge
representation language with an assertional sublanguage
used to express constraints and rules. It also provides
for general-purpose deductive inference and
special-purpose temporal reasoning. Results reported in
the paper address several knowledge base management
issues. For storage management, a new method is
proposed for generating a logical schema for a given
knowledge base. Query processing algorithms are offered
for semantic and physical query optimization, along
with an enhanced cost model for query cost estimation.
On concurrency control, the paper describes a novel
concurrency control policy which takes advantage of
knowledge base structure and is shown to outperform
two-phase locking for highly structured knowledge bases
and update-intensive transactions. Finally, algorithms
for compilation and efficient processing of constraints
and rules during knowledge base operations are
described. The paper describes original results,
including novel data structures and algorithms, as well
as preliminary performance evaluation data. Based on
these results, we conclude that knowledge base
management systems which can accommodate large
knowledge bases are feasible.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "concurrency control; constraint enforcement; knowledge
base management systems; rule management; storage
management",
}
@Article{Becker:1996:AOM,
author = "Bruno Becker and Stephan Gschwind and Thomas Ohler and
Bernhard Seeger and Peter Widmayer",
title = "An Asymptotically Optimal Multiversion {B}-Tree",
journal = j-VLDB-J,
volume = "5",
number = "4",
pages = "264--275",
month = dec,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:39 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Becker:Bruno.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Gschwind:Stephan.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/o/Ohler:Thomas.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Seeger:Bernhard.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/w/Widmayer:Peter.html;
http://link.springer.de/link/service/journals/00778/bibs/6005004/60050264.htm;
http://link.springer.de/link/service/journals/00778/papers/6005004/60050264.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005004/60050264.ps.gz",
abstract = "In a variety of applications, we need to keep track of
the development of a data set over time. For
maintaining and querying these multiversion data
efficiently, external storage structures are an
absolute necessity. We propose a multiversion B-tree
that supports insertions and deletions of data items at
the current version and range queries and exact match
queries for any version, current or past. Our
multiversion B-tree is asymptotically optimal in the
sense that the time and space bounds are asymptotically
the same as those of the (single-version) B-tree in the
worst case. The technique we present for transforming a
(single-version) B-tree into a multiversion B-tree is
quite general: it applies to a number of hierarchical
external access structures with certain properties
directly, and it can be modified for others.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access methods; information systems; physical design;
versioned data",
}
@Article{Kashyap:1996:SSS,
author = "Vipul Kashyap and Amit P. Sheth",
title = "Semantic and Schematic Similarities Between Database
Objects: a Context-Based Approach",
journal = j-VLDB-J,
volume = "5",
number = "4",
pages = "276--304",
month = dec,
year = "1996",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:39 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb5.html;
http://link.springer.de/link/service/journals/00778/tocs/t6005004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kashyap:Vipul.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sheth:Amit_P=.html;
http://link.springer.de/link/service/journals/00778/bibs/6005004/60050276.htm;
http://link.springer.de/link/service/journals/00778/papers/6005004/60050276.pdf;
http://link.springer.de/link/service/journals/00778/papers/6005004/60050276.ps.gz;
http://link.springer.de/link/service/journals/00778/tocs/mailto: HREF="mailto:helpdesk@link.springer.de">helpdesk@link.springer.de",
abstract = "In a multidatabase system, schematic conflicts between
two objects are usually of interest only when the
objects have some semantic similarity. We use the
concept of {\em semantic proximity}, which is
essentially an {\em abstraction/mapping\/} between the
domains of the two objects associated with the {\em
context of comparison}. An explicit though partial
context representation is proposed and the specificity
relationship between contexts is defined. The contexts
are organized as a meet semi-lattice and associated
operations like the greatest lower bound are defined.
The context of comparison and the type of abstractions
used to relate the two objects form the basis of a
semantic taxonomy. At the {\em semantic level}, the
intensional description of database objects provided by
the context is expressed using description logics. The
terms used to construct the contexts are obtained from
{\em domain-specific ontologies}. {\em Schema
correspondences\/} are used to store mappings from the
semantic level to the data level and are associated
with the respective contexts. Inferences about database
content at the federation level are modeled as changes
in the context and the associated schema
correspondences. We try to reconcile the dual
(schematic and semantic) perspectives by enumerating
{\em possible semantic similarities\/} between objects
having schema and data conflicts, and modeling schema
correspondences as the projection of semantic proximity
{\em with respect to (wrt)\/} context.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Evangelidis:1997:HTM,
author = "Georgios Evangelidis and David B. Lomet and Betty
Salzberg",
title = "The {hB} {$^{\Pi }$}{-tree}: a multi-attribute index
supporting concurrency, recovery and node
consolidation",
journal = j-VLDB-J,
volume = "6",
number = "1",
pages = "1--25",
month = feb,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:40 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t7006001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/e/Evangelidis:Georgios.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Lomet:David_B=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Salzberg:Betty.html;
http://link.springer.de/link/service/journals/00778/bibs/7006001/70060001.htm;
http://link.springer.de/link/service/journals/00778/papers/7006001/70060001.pdf;
http://link.springer.de/link/service/journals/00778/papers/7006001/70060001.ps.gz",
abstract = "We propose a new multi-attribute index. Our approach
combines the hB-tree, a multi-attribute index, and the
$ \Pi $-tree, an abstract index which offers efficient
concurrency and recovery methods. We call the resulting
method the hB $^\Pi $-tree. We describe several
versions of the hB $^\Pi $-tree, each using a different
node-splitting and index-term-posting algorithm. We
also describe a new node deletion algorithm. We have
implemented all the versions of the hB $^\Pi $-tree.
Our performance results show that even the version that
offers no performance guarantees, actually performs
very well in terms of storage utilization, index size
(fan-out), exact-match and range searching, under
various data types and distributions. We have also
shown that our index is fairly insensitive to increases
in dimension. Thus, it is suitable for indexing
high-dimensional applications. This property and the
fact that all our versions of the hB $^\Pi $-tree can
use the $ \Pi $-tree concurrency and recovery
algorithms make the hB $^\Pi $-tree a promising
candidate for inclusion in a general-purpose DBMS.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "concurrency; multi-attribute index; node
consolidation; recovery",
remark = "Check month: January or February??",
}
@Article{Antoshenkov:1997:DBO,
author = "Gennady Antoshenkov",
title = "Dictionary-based order-preserving string compression
(*)",
journal = j-VLDB-J,
volume = "6",
number = "1",
pages = "26--39",
month = feb,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:40 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t7006001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Antoshenkov:Gennady.html;
http://link.springer.de/link/service/journals/00778/bibs/7006001/70060026.htm;
http://link.springer.de/link/service/journals/00778/papers/7006001/70060026.pdf;
http://link.springer.de/link/service/journals/00778/papers/7006001/70060026.ps.gz",
abstract = "As no database exists without indexes, no index
implementation exists without order-preserving key
compression, in particular, without prefix and tail
compression. However, despite the great potentials of
making indexes smaller and faster, application of
general compression methods to ordered data sets has
advanced very little. This paper demonstrates that the
fast dictionary-based methods can be applied to
order-preserving compression almost with the same
freedom as in the general case. The proposed new
technology has the same speed and a compression rate
only marginally lower than the traditional
order-indifferent dictionary encoding. Procedures for
encoding and generating the encode tables are described
covering such order-related features as ordered data
set restrictions, sensitivity and insensitivity to a
character position, and one-symbol encoding of each
frequent trailing character sequence. The experimental
results presented demonstrate five-folded compression
on real-life data sets and twelve-folded compression on
Wisconsin benchmark text fields.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "indexing; order-preserving key compression",
remark = "Check month: January or February??",
}
@Article{Singhal:1997:ALB,
author = "Vigyan Singhal and Alan Jay Smith",
title = "Analysis of Locking Behavior in Three Real Database
Systems",
journal = j-VLDB-J,
volume = "6",
number = "1",
pages = "40--52",
month = feb,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:40 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t7006001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Singhal:Vigyan.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Smith:Alan_Jay.html;
http://link.springer.de/link/service/journals/00778/bibs/7006001/70060040.htm;
http://link.springer.de/link/service/journals/00778/papers/7006001/70060040.pdf;
http://link.springer.de/link/service/journals/00778/papers/7006001/70060040.ps.gz",
abstract = "Concurrency control is essential to the correct
functioning of a database due to the need for correct,
reproducible results. For this reason, and because
concurrency control is a well-formulated problem, there
has developed an enormous body of literature studying
the performance of concurrency control algorithms. Most
of this literature uses either analytic modeling or
random number-driven simulation, and explicitly or
implicitly makes certain assumptions about the behavior
of transactions and the patterns by which they set and
unset locks. Because of the difficulty of collecting
suitable measurements, there have been only a few
studies which use trace-driven simulation, and still
less study directed toward the characterization of
concurrency control behavior of real workloads. In this
paper, we present a study of three database workloads,
all taken from IBM DB2 relational database systems
running commercial applications in a production
environment. This study considers topics such as
frequency of locking and unlocking, deadlock and
blocking, duration of locks, types of locks,
correlations between applications of lock types,
two-phase versus non-two-phase locking, when locks are
held and released, etc. In each case, we evaluate the
behavior of the workload relative to the assumptions
commonly made in the research literature and discuss
the extent to which those assumptions may or may not
lead to erroneous conclusions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "concurrency control; trace-driven simulation; workload
characterization",
remark = "Check month: January or February??",
}
@Article{Mehta:1997:DPS,
author = "Manish Mehta and David J. DeWitt",
title = "Data placement in shared-nothing parallel database
systems (*)",
journal = j-VLDB-J,
volume = "6",
number = "1",
pages = "53--72",
month = feb,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:40 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t7006001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/DeWitt:David_J=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Mehta:Manish.html;
http://link.springer.de/link/service/journals/00778/bibs/7006001/70060053.htm;
http://link.springer.de/link/service/journals/00778/papers/7006001/70060053.pdf;
http://link.springer.de/link/service/journals/00778/papers/7006001/70060053.ps.gz;
http://link.springer.de/link/service/journals/00778/tocs/mailto: HREF="mailto:helpdesk@link.springer.de">helpdesk@link.springer.de",
abstract = "Data placement in shared-nothing database systems has
been studied extensively in the past and various
placement algorithms have been proposed. However, there
is no consensus on the most efficient data placement
algorithm and placement is still performed manually by
a database administrator with periodic reorganization
to correct mistakes. This paper presents the first
comprehensive simulation study of data placement issues
in a shared-nothing system. The results show that
current hardware technology trends have significantly
changed the performance tradeoffs considered in past
studies. A simplistic data placement strategy based on
the new results is developed and shown to perform well
for a variety of workloads.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "declustering; disk allocation; resource allocation;
resource scheduling",
remark = "Check month: January or February??",
}
@Article{Papazoglou:1997:DMO,
author = "Mike P. Papazoglou and Bernd J. Kr{\"a}mer",
title = "A Database Model for Object Dynamics",
journal = j-VLDB-J,
volume = "6",
number = "2",
pages = "73--96",
month = aug,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:41 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t7006002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition. See erratum
\cite{Papazoglou:1997:EDM}.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kr=auml=mer:Bernd_J=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Papazoglou:Mike_P=.html;
http://link.springer.de/link/service/journals/00778/bibs/7006002/70060073.htm;
http://link.springer.de/link/service/journals/00778/papers/7006002/70060073.pdf;
http://link.springer.de/link/service/journals/00778/papers/7006002/70060073.ps.gz",
abstract = "To effectively model complex applications in which
constantly changing situations can be represented, a
database system must be able to support the runtime
specification of structural and behavioral nuances for
objects on an individual or group basis. This paper
introduces the role mechanism as an extension of
object-oriented databases to support unanticipated
behavioral oscillations for objects that may attain
many types and share a single object identity. A role
refers to the ability to represent object dynamics by
seamlessly integrating idiosyncratic behavior, possibly
in response to external events, with pre-existing
object behavior specified at instance creation time. In
this manner, the same object can simultaneously be an
instance of different classes which symbolize the
different roles that this object assumes. The role
concept and its underlying linguistic scheme simplify
the design requirements of complex applications that
need to create and manipulate dynamic objects.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "dynamic class hierarchy; dynamic object
re-classification; object migration; object role model;
object-oriented database systems",
remark = "Check month: May or August??",
}
@Article{Catarci:1997:GIH,
author = "Tiziana Catarci and Giuseppe Santucci and John
Cardiff",
title = "Graphical interaction with heterogeneous databases
(*)",
journal = j-VLDB-J,
volume = "6",
number = "2",
pages = "97--120",
month = aug,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:41 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t7006002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Cardiff:John.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Catarci:Tiziana.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Santucci:Giuseppe.html;
http://link.springer.de/link/service/journals/00778/bibs/7006002/70060097.htm;
http://link.springer.de/link/service/journals/00778/papers/7006002/70060097.pdf;
http://link.springer.de/link/service/journals/00778/papers/7006002/70060097.ps.gz",
abstract = "During the past few years our research efforts have
been inspired by two different needs. On one hand, the
number of non-expert users accessing databases is
growing apace. On the other, information systems will
no longer be characterized by a single centralized
architecture, but rather by several heterogeneous
component systems. In order to address such needs we
have designed a new query system with both
user-oriented and multidatabase features. The system's
main components are an adaptive visual interface,
providing the user with different and interchangeable
interaction modalities, and a ``translation layer'',
which creates and offers to the user the illusion of a
single homogeneous schema out of several heterogeneous
components. Both components are founded on a common
ground, i.e. a formally defined and semantically rich
data model, the Graph Model, and a minimal set of
Graphical Primitives, in terms of which general query
operations may be visually expressed. The Graph Model
has a visual syntax, so that graphical operations can
be applied on its components without unnecessary
mappings, and an object-based semantics. The aim of
this paper is twofold. We first present an overall view
of the system architecture and then give a
comprehensive description of the lower part of the
system itself. In particular, we show how schemata
expressed in different data models can be translated in
terms of Graph Model, possibly by exploiting reverse
engineering techniques. Moreover, we show how mappings
can be established between well-known query languages
and the Graphical Primitives. Finally, we describe in
detail how queries expressed by using the Graphical
Primitives can be translated in terms of relational
expressions so to be processed by actual DBMSs.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
remark = "Check month: May or August??",
}
@Article{Chen:1997:AHF,
author = "Ming-Syan Chen and Hui-I Hsiao and Philip S. Yu",
title = "On Applying Hash Filters to Improving the Execution of
Multi-Join Queries",
journal = j-VLDB-J,
volume = "6",
number = "2",
pages = "121--131",
month = aug,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:41 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t7006002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Chen:Ming=Syan.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Hsiao:Hui=I.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/y/Yu:Philip_S=.html;
http://link.springer.de/link/service/journals/00778/bibs/7006002/70060121.htm;
http://link.springer.de/link/service/journals/00778/papers/7006002/70060121.pdf;
http://link.springer.de/link/service/journals/00778/papers/7006002/70060121.ps.gz",
abstract = "In this paper, we explore an approach of interleaving
a bushy execution tree with hash filters to improve the
execution of multi-join queries. Similar to semi-joins
in distributed query processing, hash filters can be
applied to eliminate non-matching tuples from joining
relations before the execution of a join, thus reducing
the join cost. Note that hash filters built in
different execution stages of a bushy tree can have
different costs and effects. The effect of hash filters
is evaluated first. Then, an efficient scheme to
determine an effective sequence of hash filters for a
bushy execution tree is developed, where hash filters
are built and applied based on the join sequence
specified in the bushy tree so that not only is the
reduction effect optimized but also the cost associated
is minimized. Various schemes using hash filters are
implemented and evaluated via simulation. It is
experimentally shown that the application of hash
filters is in general a very powerful means to improve
the execution of multi-join queries, and the
improvement becomes more prominent as the number of
relations in a query increases.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "bushy trees; hash filters; parallel query processing;
sort-merge joins",
remark = "Check month: May or August??",
}
@Article{Ioannidis:1997:PQO,
author = "Yannis E. Ioannidis and Raymond T. Ng and Kyuseok Shim
and Timos K. Sellis",
title = "Parametric Query Optimization",
journal = j-VLDB-J,
volume = "6",
number = "2",
pages = "132--151",
month = aug,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:41 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t7006002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/i/Ioannidis:Yannis_E=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/n/Ng:Raymond_T=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sellis:Timos_K=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Shim:Kyuseok.html;
http://link.springer.de/link/service/journals/00778/bibs/7006002/70060132.htm;
http://link.springer.de/link/service/journals/00778/papers/7006002/70060132.pdf;
http://link.springer.de/link/service/journals/00778/papers/7006002/70060132.ps.gz",
abstract = "In most database systems, the values of many important
run-time parameters of the system, the data, or the
query are unknown at query optimization time.
Parametric query optimization attempts to identify at
compile time several execution plans, each one of which
is optimal for a subset of all possible values of the
run-time parameters. The goal is that at run time, when
the actual parameter values are known, the appropriate
plan should be identifiable with essentially no
overhead. We present a general formulation of this
problem and study it primarily for the buffer size
parameter. We adopt randomized algorithms as the main
approach to this style of optimization and enhance them
with a {\em sideways information passing\/} feature
that increases their effectiveness in the new task.
Experimental results of these enhanced algorithms show
that they optimize queries for large numbers of buffer
sizes in the same time needed by their conventional
versions for a single buffer size, without much
sacrifice in the output quality and with essentially
zero run-time overhead.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
remark = "Check month: May or August??",
}
@Article{Mehrotra:1997:CCH,
author = "Sharad Mehrotra and Henry F. Korth and Avi
Silberschatz",
title = "Concurrency Control in Hierarchical Multidatabase
Systems",
journal = j-VLDB-J,
volume = "6",
number = "2",
pages = "152--172",
month = aug,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:41 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t7006002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Korth:Henry_F=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Mehrotra:Sharad.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Silberschatz:Abraham.html;
http://link.springer.de/link/service/journals/00778/bibs/7006002/70060152.htm;
http://link.springer.de/link/service/journals/00778/papers/7006002/70060152.pdf;
http://link.springer.de/link/service/journals/00778/papers/7006002/70060152.ps.gz;
http://link.springer.de/link/service/journals/00778/tocs/mailto: HREF="mailto:helpdesk@link.springer.de">helpdesk@link.springer.de",
abstract = "Over the past decade, significant research has been
done towards developing transaction management
algorithms for multidatabase systems. Most of this work
assumes a monolithic architecture of the multidatabase
system with a single software module that follows a
single transaction management algorithm to ensure the
consistency of data stored in the local databases. This
monolithic architecture is not appropriate in a
multidatabase environment where the system spans
multiple different organizations that are distributed
over various geographically distant locations. In this
paper, we propose an alternative multidatabase
transaction management architecture, where the system
is hierarchical in nature. Hierarchical architecture
has consequences on the design of transaction
management algorithms. An implication of the
architecture is that the transaction management
algorithms followed by a multidatabase system must be
{\em composable\/} --- that is, it must be possible to
incorporate individual multidatabase systems as
elements in a larger multidatabase system. We present a
hierarchical architecture for a multidatabase
environment and develop techniques for concurrency
control in such systems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "concurrency control; database management; distributed
databases; multidatabase management",
remark = "Check month: May or August??",
xxauthor = "Sharad Mehrotra and Henry F. Korth and Abraham
Silberschatz",
}
@Article{Cobb:1997:IOT,
author = "Edward E. Cobb",
title = "The impact of object technology on commercial
transaction processing",
journal = j-VLDB-J,
volume = "6",
number = "3",
pages = "173--190",
month = aug,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:42 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Businesses today are searching for information
solutions that enable them to compete in the global
marketplace. To minimize risk, these solutions must
build on existing investments, permit the best
technology to be applied to the problem, and be
manageable. Object technology, with its promise of
improved productivity and quality in application
development, delivers these characteristics but, to
date, its deployment in commercial business
applications has been limited. One possible reason is
the absence of the transaction paradigm, widely used in
commercial environments and essential for reliable
business applications. For object technology to be a
serious contender in the construction of these
solutions requires: --- technology for transactional
objects. In December 1994, the Object Management Group
adopted a specification for an object {\em transaction
service\/} (OTS). The OTS specifies mechanisms for
defining and manipulating transactions. Though derived
from the X/Open distributed transaction processing
model, OTS contains additional enhancements
specifically designed for the object environment.
Similar technology from Microsoft appeared at the end
of 1995. --- methodologies for building new business
systems from existing parts. Business process
re-engineering is forcing businesses to improve their
operations which bring products to market. {\em
Workflow computing}, when used in conjunction with {\em
``object wrappers''\/} provides tools to both define
and track execution of business processes which
leverage existing applications and infrastructure. --
an execution environment which satisfies the
requirements of the operational needs of the business.
Transaction processing (TP) monitor technology, though
widely accepted for mainframe transaction processing,
has yet to enjoy similar success in the client/server
marketplace. Instead the database vendors, with their
extensive tool suites, dominate. As object brokers
mature they will require many of the functions of
today's TP monitors. Marrying these two technologies
can produce a robust execution environment which offers
a superior alternative for building and deploying
client/server applications.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "objects; transaction processing; workflow",
}
@Article{Cobb:1997:ITC,
author = "Edward E. Cobb",
title = "The Impact of Technology on Commercial Transaction
Processing",
journal = j-VLDB-J,
volume = "6",
number = "3",
pages = "173--190",
month = aug,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 27 10:11:57 MDT 2000",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t0006003.htm;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Cobb:Edward_E=.html;
http://link.springer.de/link/service/journals/00778/bibs/7006003/70060173.htm",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
remark = "Check month: May or August??",
xxtitle = "The impact of object technology on commercial
transaction processing",
}
@Article{Steinbrunn:1997:HRO,
author = "Michael Steinbrunn and Guido Moerkotte and Alfons
Kemper",
title = "Heuristic and Randomized Optimization for the Join
Ordering Problem",
journal = j-VLDB-J,
volume = "6",
number = "3",
pages = "191--208",
month = aug,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:42 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t0006003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kemper:Alfons.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Moerkotte:Guido.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Steinbrunn:Michael.html;
http://link.springer.de/link/service/journals/00778/bibs/7006003/70060191.htm",
abstract = "Recent developments in database technology, such as
deductive database systems, have given rise to the
demand for new, cost-effective optimization techniques
for join expressions. In this paper many different
algorithms that compute approximate solutions for
optimizing join orders are studied since traditional
dynamic programming techniques are not appropriate for
complex problems. Two possible solution spaces, the
space of left-deep and bushy processing trees, are
evaluated from a statistical point of view. The result
is that the common limitation to left-deep processing
trees is only advisable for certain join graph types.
Basically, optimizers from three classes are analysed:
heuristic, randomized and genetic algorithms. Each one
is extensively scrutinized with respect to its working
principle and its fitness for the desired application.
It turns out that randomized and genetic algorithms are
well suited for optimizing join expressions. They
generate solutions of high quality within a reasonable
running time. The benefits of heuristic optimizers,
namely the short running time, are often outweighed by
merely moderate optimization performance.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "genetic algorithms; heuristic algorithms; join
ordering; query optimization; randomized algorithms",
remark = "Check month: May or August??",
}
@Article{Panagos:1997:SRC,
author = "Euthimios Panagos and Alexandros Biliris",
title = "Synchronization and Recovery in a Client-Server
Storage System",
journal = j-VLDB-J,
volume = "6",
number = "3",
pages = "209--223",
month = aug,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:42 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t0006003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Biliris:Alexandros.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Panagos:Euthimios.html;
http://link.springer.de/link/service/journals/00778/bibs/7006003/70060209.htm",
abstract = "Client-server object-oriented database management
systems differ significantly from traditional
centralized systems in terms of their architecture and
the applications they target. In this paper, we present
the client-server architecture of the EOS storage
manager and we describe the concurrency control and
recovery mechanisms it employs. EOS offers a
semi-optimistic locking scheme based on the
multi-granularity two-version two-phase locking
protocol. Under this scheme, multiple concurrent
readers are allowed to access a data item while it is
being updated by a single writer. Recovery is based on
write-ahead redo-only logging. Log records are
generated at the clients and they are shipped to the
server during normal execution and at transaction
commit. Transaction rollback is fast because there are
no updates that have to be undone, and recovery from
system crashes requires only one scan of the log for
installing the changes made by transactions that
committed before the crash. We also present a
preliminary performance evaluation of the
implementation of the above mechanisms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "checkpoint; client-server architecture; concurrency
control; locking; logging; object management; recovery;
transaction management",
remark = "Check month: May or August??",
}
@Article{Lomet:1997:CRI,
author = "David B. Lomet and Betty Salzberg",
title = "Concurrency and Recovery for Index Trees",
journal = j-VLDB-J,
volume = "6",
number = "3",
pages = "224--240",
month = aug,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:42 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t0006003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Lomet:David_B=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Salzberg:Betty.html;
http://link.springer.de/link/service/journals/00778/bibs/7006003/70060224.htm",
abstract = "Although many suggestions have been made for
concurrency in B$^+$-trees, few of these have
considered recovery as well. We describe an approach
which provides high concurrency while preserving
well-formed trees across system crashes. Our approach
works for a class of index trees that is a
generalization of the B$^{\rm link}$-tree. This class
includes some multi-attribute indexes and temporal
indexes. Structural changes in an index tree are
decomposed into a sequence of atomic actions, each one
leaving the tree well-formed and each working on a
separate level of the tree. All atomic actions on
levels of the tree above the leaf level are independent
of database transactions, and so are of short duration.
Incomplete structural changes are detected in normal
operations and trigger completion.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access methods; B-trees; concurrency; indexing;
recovery",
remark = "Check month: May or August??",
}
@Article{Haas:1997:STA,
author = "Laura M. Haas and Michael J. Carey and Miron Livny and
Amit Shukla",
title = "Seeking the truth about {\em ad hoc\/} join costs",
journal = j-VLDB-J,
volume = "6",
number = "3",
pages = "241--256",
month = aug,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:42 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t0006003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Carey:Michael_J=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Haas:Laura_M=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Livny:Miron.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Shukla:Amit.html;
http://link.springer.de/link/service/journals/00778/bibs/7006003/70060241.htm",
abstract = "In this paper, we re-examine the results of prior work
on methods for computing {\em ad hoc\/} joins. We
develop a detailed cost model for predicting join
algorithm performance, and we use the model to develop
cost formulas for the major {\em ad hoc\/} join methods
found in the relational database literature. We show
that various pieces of ``common wisdom'' about join
algorithm performance fail to hold up when analyzed
carefully, and we use our detailed cost model to derive
optimal buffer allocation schemes for each of the join
methods examined here. We show that optimizing their
buffer allocations can lead to large performance
improvements, e.g., as much as a 400\% improvement in
some cases. We also validate our cost model's
predictions by measuring an actual implementation of
each join algorithm considered. The results of this
work should be directly useful to implementors of
relational query optimizers and query processing
systems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "buffer allocation; cost models; join methods;
optimization; performance",
remark = "Check month: May or August??",
}
@Article{Papazoglou:1997:EDM,
author = "Mike P. Papazoglou and Bernd J. Kr{\"a}mer",
title = "Erratum --- {A} database model for object dynamics",
journal = j-VLDB-J,
volume = "6",
number = "3",
pages = "257--260",
month = aug,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:42 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t0006003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition. See \cite{Papazoglou:1997:DMO}.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kr=auml=mer:Bernd_J=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Papazoglou:Mike_P=.html;
http://link.springer.de/link/service/journals/00778/bibs/7006003/70060257.htm",
abstract = "Due to a technical error, some figures of the above
paper were not reproduced satisfactorily. They are
printed again below.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
remark = "Check month: May or August??",
}
@Article{Fahl:1997:QPO,
author = "Gustav Fahl and Tore Risch",
title = "Query Processing Over Object Views of Relational
Data",
journal = j-VLDB-J,
volume = "6",
number = "4",
pages = "261--281",
month = nov,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:44 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t7006004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/f/Fahl:Gustav.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Risch:Tore.html;
http://link.springer.de/link/service/journals/00778/bibs/7006004/70060261.htm;
http://link.springer.de/link/service/journals/00778/papers/7006004/70060261.pdf",
abstract = "This paper presents an approach to {\em object view\/}
management for relational databases. Such a view
mechanism makes it possible for users to transparently
work with data in a relational database as if it was
stored in an object-oriented (OO) database. A query
against the object view is translated to one or several
queries against the relational database. The results of
these queries are then processed to form an answer to
the initial query. The approach is not restricted to a
`pure' object view mechanism for the relational data,
since the object view can also store its own data and
methods. Therefore it must be possible to process
queries that combine local data residing in the object
view with data retrieved from the relational database.
We discuss the key issues when object views of
relational databases are developed, namely: how to map
relational structures to sub-type/supertype hierarchies
in the view, how to represent relational database
access in OO query plans, how to provide the concept of
object identity in the view, how to handle the fact
that the extension of types in the view depends on the
state of the relational database, and how to process
and optimize queries against the object view. The
results are based on experiences from a running
prototype implementation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "object views; object-oriented federated databases;
query optimization; query processing; relational
databases",
}
@Article{Diaz:1997:EEA,
author = "Oscar D{\'\i}az and Arturo Jaime",
title = "{EXACT}: An Extensible Approach to Active
Object-Oriented Databases",
journal = j-VLDB-J,
volume = "6",
number = "4",
pages = "282--295",
month = nov,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:44 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t7006004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/D=iacute=az:Oscar.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/j/Jaime:Arturo.html;
http://link.springer.de/link/service/journals/00778/bibs/7006004/70060282.htm;
http://link.springer.de/link/service/journals/00778/papers/7006004/70060282.pdf",
abstract = "Active database management systems (DBMSs) are a
fast-growing area of research, mainly due to the large
number of applications which can benefit from this
active dimension. These applications are far from being
homogeneous, requiring different kinds of
functionalities. However, most of the active DBMSs
described in the literature only provide a {\em fixed,
hard-wired\/} execution model to support the active
dimension. In object-oriented DBMSs,
event-condition-action rules have been proposed for
providing active behaviour. This paper presents EXACT,
a rule manager for object-oriented DBMSs which provides
a variety of options from which the designer can choose
the one that best fits the semantics of the concept to
be supported by rules. Due to the difficulty of
foreseeing future requirements, special attention has
been paid to making rule management easily extensible,
so that the user can tailor it to suit specific
applications. This has been borne out by an
implementation in ADAM, an object-oriented DBMS. An
example is shown of how the default mechanism can be
easily extended to support new requirements.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "active DBMS; extensibility; metaclasses;
object-oriented DBMS",
}
@Article{Bohm:1997:SDS,
author = "Klemens B{\"o}hm and Karl Aberer and Erich J. Neuhold
and Xiaoya Yang",
title = "Structured Document Storage and Refined Declarative
and Navigational Access Mechanisms in {HyperStorM}",
journal = j-VLDB-J,
volume = "6",
number = "4",
pages = "296--311",
month = nov,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:44 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t7006004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Aberer:Karl.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/B=ouml=hm:Klemens.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/n/Neuhold:Erich_J=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/y/Yang:Xiaoya.html;
http://link.springer.de/link/service/journals/00778/bibs/7006004/70060296.htm;
http://link.springer.de/link/service/journals/00778/papers/7006004/70060296.pdf",
abstract = "The combination of SGML and database technology allows
to refine both declarative and navigational access
mechanisms for structured document collection: with
regard to declarative access, the user can formulate
complex information needs without knowing a query
language, the respective document type definition (DTD)
or the underlying modelling. Navigational access is
eased by hyperlink-rendition mechanisms going beyond
plain link-integrity checking. With our approach, the
database-internal representation of documents is
configurable. It allows for an efficient implementation
of operations, because DTD knowledge is not needed for
document structure recognition. We show how the number
of method invocations and the cost of parsing can be
significantly reduced.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "document query languages; navigation; OODBMSs; SGML",
}
@Article{Muck:1997:CTH,
author = "Thomas A. M{\"u}ck and Martin L. Polaschek",
title = "A Configurable Type Hierarchy Index for {OODB}",
journal = j-VLDB-J,
volume = "6",
number = "4",
pages = "312--332",
month = nov,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 27 08:46:02 MDT 2000",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/M=uuml=ck:Thomas_A=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Polaschek:Martin_L=.html",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mueck:1997:CTH,
author = "Thomas A. Mueck and Martin L. Polaschek",
title = "A configurable type hierarchy index for {OODB}",
journal = j-VLDB-J,
volume = "6",
number = "4",
pages = "312--332",
month = nov,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:44 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t7006004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/7006004/70060312.htm;
http://link.springer.de/link/service/journals/00778/papers/7006004/70060312.pdf",
abstract = "With respect to the specific requirements of advanced
OODB applications, index data structures for type
hierarchies in OODBMS have to provide efficient support
for multiattribute queries and have to allow index
optimization for a particular query profile. We
describe the {\em multikey type index\/} and an
efficient implementation of this indexing scheme. It
meets both requirements: in addition to its
multiattribute query capabilities it is designed as a
mediator between two standard design alternatives,
key-grouping and type-grouping. A prerequisite for the
multikey type index is a linearization algorithm which
maps type hierarchies to linearly ordered attribute
domains in such a way that each subhierarchy is
represented by an interval of this domain. The
algorithm extends previous results with respect to
multiple inheritance. The subsequent evaluation of our
proposal focuses on storage space overhead as well as
on the number of disk I/O operations needed for query
execution. The analytical results for the multikey type
index are compared to previously published figures for
well-known single-key search structures. The comparison
clearly shows the superiority of the multikey type
index for a large class of query profiles.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access methods; indexing; multiple inheritance; OODB;
type hierarchies",
}
@Article{Berchtold:1997:UEF,
author = "Stefan Berchtold and Daniel A. Keim and Hans-Peter
Kriegel",
title = "Using Extended Feature Objects for Partial Similarity
Retrieval",
journal = j-VLDB-J,
volume = "6",
number = "4",
pages = "333--348",
month = nov,
year = "1997",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:44 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb6.html;
http://link.springer.de/link/service/journals/00778/tocs/t7006004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Berchtold:Stefan.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Keim:Daniel_A=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kriegel:Hans=Peter.html;
http://link.springer.de/link/service/journals/00778/bibs/7006004/70060333.htm;
http://link.springer.de/link/service/journals/00778/papers/7006004/70060333.pdf",
abstract = "In this paper, we introduce the concept of extended
feature objects for similarity retrieval. Conventional
approaches for similarity search in databases map each
object in the database to a point in some
high-dimensional feature space and define similarity as
some distance measure in this space. For many
similarity search problems, this feature-based approach
is not sufficient. When retrieving partially similar
polygons, for example, the search cannot be restricted
to edge sequences, since similar polygon sections may
start and end anywhere on the edges of the polygons. In
general, inherently continuous problems such as the
partial similarity search cannot be solved by using
point objects in feature space. In our solution, we
therefore introduce extended feature objects consisting
of an infinite set of feature points. For an efficient
storage and retrieval of the extended feature objects,
we determine the minimal bounding boxes of the feature
objects in multidimensional space and store these boxes
using a spatial access structure. In our concrete
polygon problem, sets of polygon sections are mapped to
2D feature objects in high-dimensional space which are
then approximated by minimal bounding boxes and stored
in an R$^*$-tree. The selectivity of the index is
improved by using an adaptive decomposition of very
large feature objects and a dynamic joining of small
feature objects. For the polygon problem, translation,
rotation, and scaling invariance is achieved by using
the Fourier-transformed curvature of the normalized
polygon sections. In contrast to vertex-based
algorithms, our algorithm guarantees that no false
dismissals may occur and additionally provides fast
search times for realistic database sizes. We evaluate
our method using real polygon data of a supplier for
the car manufacturing industry.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "CAD databases; Fourier transformation; indexing and
query processing of spatial objects; partial similarity
retrieval",
}
@Article{Han:1998:ORQ,
author = "Jia Liang Han",
title = "Optimizing Relational Queries in Connection
Hypergraphs: Nested Queries, Views, and Binding
Propagations",
journal = j-VLDB-J,
volume = "7",
number = "1",
pages = "1--11",
month = feb,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:45 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Han:Jia_Liang.html;
http://link.springer.de/link/service/journals/00778/bibs/8007001/80070001.htm;
http://link.springer.de/link/service/journals/00778/papers/8007001/80070001.pdf",
abstract = "We optimize relational queries using connection
hypergraphs (CHGs). All operations including
value-passing between SQL blocks can be set-oriented.
By introducing partial evaluations, reordering
operations can be achieved for nested queries. For a
query using views, we merge CHGs for the views and the
query into one CHG and then apply query optimization.
Furthermore, we may simulate magic sets methods
elegantly in a CHG. Sideways information-passing
strategies (SIPS) in a CHG amount to partial
evaluations of SIPS paths. We introduce the maximum
SIPS strategy, which performs SIPS for all bindings and
all SIPS paths for a query. The new method has several
advantages. First, the maximum SIPS strategy can be
more efficient than the previous SIPS based on simple
heuristics. Second, it is conceptually simple and easy
to implement. Third, the processing strategies may be
incorporated with the search space for query execution
plans, which is a proven optimization strategy
introduced by System R. Fourth, it provides a general
framework of query optimization and may potentially be
used to optimize next-generation database systems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "connection hypergraphs; partial evaluations;
relational query optimization; search space; SIPS",
}
@Article{Hanson:1998:FRC,
author = "Eric N. Hanson and I.-Cheng Chen and Roxana Dastur and
Kurt Engel and Vijay Ramaswamy and Wendy Tan and Chun
Xu",
title = "A Flexible and Recoverable Client\slash Server
Database Event Notification System",
journal = j-VLDB-J,
volume = "7",
number = "1",
pages = "12--24",
month = feb,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:45 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Chen:I==Cheng.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/Dastur:Roxana.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/e/Engel:Kurt.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Hanson:Eric_N=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Ramaswamy:Vijay.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Tan:Wendy.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/x/Xu:Chun.html;
http://link.springer.de/link/service/journals/00778/bibs/8007001/80070012.htm;
http://link.springer.de/link/service/journals/00778/papers/8007001/80070012.pdf",
abstract = "A software architecture is presented that allows
client application programs to interact with a DBMS
server in a flexible and powerful way, using either
direct, volatile messages, or messages sent via
recoverable queues. Normal requests from clients to the
server and replies from the server to clients can be
transmitted using direct or recoverable messages. In
addition, an application event notification mechanism
is provided, whereby client applications running
anywhere on the network can register for events, and
when those events are raised, the clients are notified.
A novel parameter passing mechanism allows a set of
tuples to be included in an event notification. The
event mechanism is particularly useful in an active
DBMS, where events can be raised by triggers to signal
running application programs.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mehta:1998:OPM,
author = "Ashish Mehta and James Geller and Yehoshua Perl and
Erich J. Neuhold",
title = "The {OODB} Path-Method Generator ({PMG}) Using Access
Weights and Precomputed Access Relevance",
journal = j-VLDB-J,
volume = "7",
number = "1",
pages = "25--47",
month = feb,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:45 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Geller:James.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Mehta:Ashish.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/n/Neuhold:Erich_J=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Perl:Yehoshua.html;
http://link.springer.de/link/service/journals/00778/bibs/8007001/80070025.htm;
http://link.springer.de/link/service/journals/00778/papers/8007001/80070025.pdf",
abstract = "A {\em path-method\/} is used as a mechanism in
object-oriented databases (OODBs) to retrieve or to
update information relevant to one class that is not
stored with that class but with some other class. A
path-method is a method which traverses from one class
through a chain of connections between classes and
accesses information at another class. However, it is a
difficult task for a casual user or even an application
programmer to write path-methods to facilitate queries.
This is because it might require comprehensive
knowledge of many classes of the conceptual schema that
are not directly involved in the query, and therefore
may not even be included in a user's (incomplete) view
about the contents of the database. We have developed a
system, called {\em path-method generator\/} (PMG),
which generates path-methods automatically according to
a user's database-manipulating requests. The PMG offers
the user one of the possible path-methods and the user
verifies from his knowledge of the intended purpose of
the request whether that path-method is the desired
one. If the path method is rejected, then the user can
utilize his now increased knowledge about the database
to request (with additional parameters given) another
offer from the PMG. The PMG is based on {\em access
weights\/} attached to the connections between classes
and precomputed {\em access relevance\/} between every
pair of classes of the OODB. Specific rules for access
weight assignment and algorithms for computing access
relevance appeared in our previous papers [MGPF92,
MGPF93, MGPF96]. In this paper, we present a variety of
traversal algorithms based on access weights and
precomputed access relevance. Experiments identify some
of these algorithms as very successful in generating
most desired path-methods. The PMG system utilizes
these successful algorithms and is thus an efficient
tool for aiding the user with the difficult task of
querying and updating a large OODB.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access relevance; access weight; object-oriented
databases; OODB queries; path-method; traversal
algorithms",
}
@Article{Scheuermann:1998:DPL,
author = "Peter Scheuermann and Gerhard Weikum and Peter
Zabback",
title = "Data Partitioning and Load Balancing in Parallel Disk
Systems",
journal = j-VLDB-J,
volume = "7",
number = "1",
pages = "48--66",
month = feb,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:45 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Scheuermann:Peter.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/w/Weikum:Gerhard.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/z/Zabback:Peter.html;
http://link.springer.de/link/service/journals/00778/bibs/8007001/80070048.htm;
http://link.springer.de/link/service/journals/00778/papers/8007001/80070048.pdf",
abstract = "Parallel disk systems provide opportunities for
exploiting I/O parallelism in two possible ways, namely
via inter-request and intra-request parallelism. In
this paper, we discuss the main issues in performance
tuning of such systems, namely striping and load
balancing, and show their relationship to response time
and throughput. We outline the main components of an
intelligent, self-reliant file system that aims to
optimize striping by taking into account the
requirements of the applications, and performs load
balancing by judicious file allocation and dynamic
redistributions of the data when access patterns
change. Our system uses simple but effective heuristics
that incur only little overhead. We present performance
experiments based on synthetic workloads and real-life
traces.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data allocation; disk cooling; file striping; load
balancing; parallel disk systems; performance tuning",
}
@Article{Ishakbeyoglu:1998:MII,
author = "Naci S. Ishakbeyo{\u{g}}lu and Z. Meral
{\"O}zsoyo{\u{g}}lu",
title = "Maintenance of Implication Integrity Constraints Under
Updates to Constraints",
journal = j-VLDB-J,
volume = "7",
number = "2",
pages = "67--78",
month = may,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:45 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/=/=Ouml=zsoyoglu:Z=_Meral.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/i/Ishakbeyoglu:Naci.html;
http://link.springer.de/link/service/journals/00778/bibs/8007002/80070067.htm;
http://link.springer.de/link/service/journals/00778/papers/8007002/80070067.pdf",
abstract = "Semantic integrity constraints are used for enforcing
the integrity of the database as well as for improving
the efficiency of the database utilization. Although
semantic integrity constraints are usually much more
static as compared to the data itself, changes in the
data semantics may necessitate corresponding changes in
the constraint base. In this paper we address the
problems related with maintaining a consistent and
non-redundant set of constraints satisfied by the
database in the case of updates to the constraint base.
We consider implication constraints as semantic
integrity constraints. The constraints are represented
as conjunctions of inequalities. We present a
methodology to determine whether a constraint is
redundant or contradictory with respect to a set of
constraints. The methodology is based on the
partitioning of the constraint base which improves the
efficiency of algorithms that check whether a
constraint is redundant or contradictory with respect
to a constraint base.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "implication integrity constraints; integrity
constraints; partitioning; redundancy; satisfiability",
}
@Article{Dessloch:1998:ADP,
author = "Stefan De{\ss}loch and Theo H{\"a}rder and Nelson
Mendon{\c{c}}a Mattos and Bernhard Mitschang and
Joachim Thomas",
title = "Advanced Data Processing in {KRISYS}: Modeling
Concepts, Implementation Techniques, and Client\slash
Server Issues",
journal = j-VLDB-J,
volume = "7",
number = "2",
pages = "79--95",
month = may,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:45 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/De=szlig=loch:Stefan.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/H=auml=rder:Theo.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Mattos:Nelson_Mendon=ccedil=a.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Mitschang:Bernhard.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Thomas:Joachim.html;
http://link.springer.de/link/service/journals/00778/bibs/8007002/80070079.htm;
http://link.springer.de/link/service/journals/00778/papers/8007002/80070079.pdf",
abstract = "The increasing power of modern computers is steadily
opening up new application domains for advanced data
processing such as engineering and knowledge-based
applications. To meet their requirements, concepts for
advanced data management have been investigated during
the last decade, especially in the field of object
orientation. Over the last couple of years, the
database group at the University of Kaiserslautern has
been developing such an advanced database system, the
KRISYS prototype. In this article, we report on the
results and experiences obtained in the course of this
project. The primary objective for the first version of
KRISYS was to provide semantic features, such as an
expressive data model, a set-oriented query language,
deductive as well as active capabilities. The first
KRISYS prototype became completely operational in 1989.
To evaluate its features and to stabilize its
functionality, we started to develop several
applications with the system. These experiences marked
the starting point for an overall redesign of KRISYS.
Major goals were to tune KRISYS and its
query-processing facilities to a suitable client/server
environment, as well as to provide elaborate mechanisms
for consistency control comprising semantic integrity
constraints, multi-user synchronization, and failure
recovery. The essential aspects of the resulting
client/server architecture are embodied by the
client-side data management needed to effectively
support advanced applications and to gain the required
system performance for interactive work. The project
stages of KRISYS properly reflect the essential
developments that have taken place in the research on
advanced database systems over the last years. Hence,
the subsequent discussions will bring up a number of
important aspects with regard to advanced data
processing that are of significant general importance,
as well as of general applicability to database
systems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "client\slash server architectures; Consistency
control; object-oriented modeling concepts; query
processing; run-time optimization",
}
@Article{Abiteboul:1998:LVS,
author = "Serge Abiteboul and Sophie Cluet and Tova Milo",
title = "A Logical View of Structured Files",
journal = j-VLDB-J,
volume = "7",
number = "2",
pages = "96--114",
month = may,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:45 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Abiteboul:Serge.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Cluet:Sophie.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Milo:Tova.html;
http://link.springer.de/link/service/journals/00778/bibs/8007002/80070096.htm;
http://link.springer.de/link/service/journals/00778/papers/8007002/80070096.pdf",
abstract = "Structured data stored in files can benefit from
standard database technology. In particular, we show
here how such data can be queried and updated using
declarative database languages. We introduce the notion
of {\em structuring schema}, which consists of a
grammar annotated with database programs. Based on a
structuring schema, a file can be viewed as a database
structure, queried and updated as such. For {\em
queries}, we show that almost standard database
optimization techniques can be used to answer queries
without having to construct the entire database. For
{\em updates}, we study in depth the propagation to the
file of an update specified on the database view of
this file. The problem is not feasible in general and
we present a number of negative results. The positive
results consist of techniques that allow to propagate
updates efficiently under some reasonable {\em
locality\/} conditions on the structuring schemas.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database; file system; query; query and update
optimization; textual data; update",
}
@Article{Ooi:1998:FIR,
author = "Beng Chin Ooi and Kian-Lee Tan and Tat Seng Chua and
Wynne Hsu",
title = "Fast Image Retrieval Using Color-Spatial Information",
journal = j-VLDB-J,
volume = "7",
number = "2",
pages = "115--128",
month = may,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:45 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Chua:Tat=Seng.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Hsu:Wynne.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/o/Ooi:Beng_Chin.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Tan:Kian=Lee.html;
http://link.springer.de/link/service/journals/00778/bibs/8007002/80070115.htm;
http://link.springer.de/link/service/journals/00778/papers/8007002/80070115.pdf",
abstract = "In this paper, we present an image retrieval system
that employs both the color and spatial information of
images to facilitate the retrieval process. The basic
unit used in our technique is a {\em single-colored
cluster}, which bounds a homogeneous region of that
color in an image. Two clusters from two images are
similar if they are of the same color and overlap in
the image space. The number of clusters that can be
extracted from an image can be very large, and it
affects the accuracy of retrieval. We study the effect
of the number of clusters on retrieval effectiveness to
determine an appropriate value for ``optimal''
performance. To facilitate efficient retrieval, we also
propose a multi-tier indexing mechanism called the {\em
Sequenced Multi-Attribute Tree\/} (SMAT). We
implemented a two-tier SMAT, where the first layer is
used to prune away clusters that are of different
colors, while the second layer discriminates clusters
of different spatial locality. We conducted an
experimental study on an image database consisting of
12,000 images. Our results show the effectiveness of
the proposed color-spatial approach, and the efficiency
of the proposed indexing mechanism.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "color-spatial information; content-based retrieval;
sequenced multi-attribute tree; single-colored
cluster",
}
@Article{Jarke:1998:GE,
author = "Matthias Jarke",
title = "Guest {Editorial}",
journal = j-VLDB-J,
volume = "7",
number = "3",
pages = "129--129",
month = aug,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:47 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t8007003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/8007003/80070129.htm;
http://link.springer.de/link/service/journals/00778/papers/8007003/80070129.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Seshadri:1998:EAD,
author = "Praveen Seshadri",
title = "Enhanced Abstract Data Types in Object-Relational
Databases",
journal = j-VLDB-J,
volume = "7",
number = "3",
pages = "130--140",
month = aug,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:47 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Special Issue on {VLDB 1997}. Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Seshadri:Praveen.html;
http://link.springer.de/link/service/journals/00778/bibs/8007003/80070130.htm;
http://link.springer.de/link/service/journals/00778/papers/8007003/80070130.pdf",
abstract = "The explosion in complex multimedia content makes it
crucial for database systems to support such data
efficiently. This paper argues that the ``blackbox''
ADTs used in current object-relational systems inhibit
their performance, thereby limiting their use in
emerging applications. Instead, the next generation of
object-relational database systems should be based on
enhanced abstract data type (E-ADT) technology. An
(E-ADT) can expose the {\em semantics\/} of its methods
to the database system, thereby permitting advanced
query optimizations. Fundamental architectural changes
are required to build a database system with E-ADTs;
the added functionality should not compromise the
modularity of data types and the extensibility of the
type system. The implementation issues have been
explored through the development of E-ADTs in {\em
Predator}. Initial performance results demonstrate an
order of magnitude in performance improvements.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database types; extensibility; object-relational
database; query optimization",
}
@Article{Kraiss:1998:IDC,
author = "Achim Kraiss and Gerhard Weikum",
title = "Integrated Document Caching and Prefetching in Storage
Hierarchies Based on {Markov}-Chain Predictions",
journal = j-VLDB-J,
volume = "7",
number = "3",
pages = "141--162",
month = aug,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:47 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kraiss:Achim.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/w/Weikum:Gerhard.html;
http://link.springer.de/link/service/journals/00778/bibs/8007003/80070141.htm;
http://link.springer.de/link/service/journals/00778/papers/8007003/80070141.pdf",
abstract = "Large multimedia document archives may hold a major
fraction of their data in tertiary storage libraries
for cost reasons. This paper develops an integrated
approach to the vertical data migration between the
tertiary, secondary, and primary storage in that it
reconciles speculative prefetching, to mask the high
latency of the tertiary storage, with the replacement
policy of the document caches at the secondary and
primary storage level, and also considers the
interaction of these policies with the tertiary and
secondary storage request scheduling. The integrated
migration policy is based on a continuous-time Markov
chain model for predicting the expected number of
accesses to a document within a specified time horizon.
Prefetching is initiated only if that expectation is
higher than those of the documents that need to be
dropped from secondary storage to free up the necessary
space. In addition, the possible resource contention at
the tertiary and secondary storage is taken into
account by dynamically assessing the response-time
benefit of prefetching a document versus the penalty
that it would incur on the response time of the pending
document requests. The parameters of the
continuous-time Markov chain model, the probabilities
of co-accessing certain documents and the interaction
times between successive accesses, are dynamically
estimated and adjusted to evolving workload patterns by
keeping online statistics. The integrated policy for
vertical data migration has been implemented in a
prototype system. The system makes profitable use of
the Markov chain model also for the scheduling of
volume exchanges in the tertiary storage library.
Detailed simulation experiments with Web-server-like
synthetic workloads indicate significant gains in terms
of client response time. The experiments also show that
the overhead of the statistical bookkeeping and the
computations for the access predictions is
affordable.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "caching; Markov chains; performance; prefetching;
scheduling; stochastic modeling; tertiary storage",
}
@Article{Chakrabarti:1998:SFS,
author = "Soumen Chakrabarti and Byron Dom and Rakesh Agrawal
and Prabhakar Raghavan",
title = "Scalable Feature Selection, Classification and
Signature Generation for Organizing Large Text
Databases into Hierarchical Topic Taxonomies",
journal = j-VLDB-J,
volume = "7",
number = "3",
pages = "163--178",
month = aug,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:47 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Agrawal:Rakesh.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Chakrabarti:Soumen.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/Dom:Byron.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Raghavan:Prabhakar.html;
http://link.springer.de/link/service/journals/00778/bibs/8007003/80070163.htm;
http://link.springer.de/link/service/journals/00778/papers/8007003/80070163.pdf",
abstract = "We explore how to organize large text databases
hierarchically by topic to aid better searching,
browsing and filtering. Many corpora, such as Internet
directories, digital libraries, and patent databases
are manually organized into topic hierarchies, also
called {\em taxonomies}. Similar to indices for
relational data, taxonomies make search and access more
efficient. However, the exponential growth in the
volume of on-line textual information makes it nearly
impossible to maintain such taxonomic organization for
large, fast-changing corpora by hand. We describe an
automatic system that starts with a small sample of the
corpus in which topics have been assigned by hand, and
then updates the database with new documents as the
corpus grows, assigning topics to these new documents
with high speed and accuracy. To do this, we use
techniques from statistical pattern recognition to
efficiently separate the {\em feature\/} words, or {\em
discriminants}, from the {\em noise\/} words at each
node of the taxonomy. Using these, we build a
multilevel classifier. At each node, this classifier
can ignore the large number of ``noise'' words in a
document. Thus, the classifier has a small model size
and is very fast. Owing to the use of context-sensitive
features, the classifier is very accurate. As a
by-product, we can compute for each document a set of
terms that occur significantly more often in it than in
the classes to which it belongs. We describe the design
and implementation of our system, stressing how to
exploit standard, efficient relational operations like
sorts and joins. We report on experiences with the
Reuters newswire benchmark, the US patent database, and
web document samples from Yahoo!. We discuss
applications where our system can improve searching and
filtering capabilities.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Roy:1998:GCO,
author = "Prasan Roy and S. Seshadri and Abraham Silberschatz
and S. Sudarshan and S. Ashwin",
title = "Garbage Collection in Object-Oriented Databases Using
Transactional Cyclic Reference Counting",
journal = j-VLDB-J,
volume = "7",
number = "3",
pages = "179--193",
month = aug,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:47 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Ashwin:S=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Roy:Prasan.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Seshadri:S=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Silberschatz:Abraham.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sudarshan:S=.html;
http://link.springer.de/link/service/journals/00778/bibs/8007003/80070179.htm;
http://link.springer.de/link/service/journals/00778/papers/8007003/80070179.pdf",
abstract = "Garbage collection is important in object-oriented
databases to free the programmer from explicitly
deallocating memory. In this paper, we present a
garbage collection algorithm, called Transactional
Cyclic Reference Counting (TCRC), for object-oriented
databases. The algorithm is based on a variant of a
reference-counting algorithm proposed for functional
programming languages The algorithm keeps track of
auxiliary reference count information to detect and
collect cyclic garbage. The algorithm works correctly
in the presence of concurrently running transactions,
and system failures. It does not obtain any long-term
locks, thereby minimizing interference with transaction
processing. It uses recovery subsystem logs to detect
pointer updates; thus, existing code need not be
rewritten. Finally, it exploits schema information, if
available, to reduce costs. We have implemented the
TCRC algorithm and present results of a performance
study of the implementation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ng:1998:IRM,
author = "Wee Teck Ng and Peter M. Chen",
title = "Integrating Reliable Memory in Databases",
journal = j-VLDB-J,
volume = "7",
number = "3",
pages = "194--204",
month = aug,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:47 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Chen:Peter_M=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/n/Ng:Wee_Teck.html;
http://link.springer.de/link/service/journals/00778/bibs/8007003/80070194.htm;
http://link.springer.de/link/service/journals/00778/papers/8007003/80070194.pdf",
abstract = "Recent results in the Rio project at the University of
Michigan show that it is possible to create an area of
main memory that is as safe as disk from operating
system crashes. This paper explores how to integrate
the reliable memory provided by the Rio file cache into
a database system. Prior studies have analyzed the
performance benefits of reliable memory; we focus
instead on how different designs affect reliability. We
propose three designs for integrating reliable memory
into databases: non-persistent database buffer cache,
persistent database buffer cache, and persistent
database buffer cache with protection. Non-persistent
buffer caches use an I/O interface to reliable memory
and require the fewest modifications to existing
databases. However, they waste memory capacity and
bandwidth due to double buffering. Persistent buffer
caches use a memory interface to reliable memory by
mapping it into the database address space. This places
reliable memory under complete database control and
eliminates double buffering, but it may expose the
buffer cache to database errors. Our third design
reduces this exposure by write protecting the buffer
pages. Extensive fault tests show that mapping reliable
memory into the database address space does not
significantly hurt reliability. This is because wild
stores rarely touch dirty, committed pages written by
previous transactions. As a result, we believe that
databases should use a memory interface to reliable
memory.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "main memory database system (MMDB); recovery;
reliability",
}
@Article{Ozsu:1998:I,
author = "M. Tamer {\"O}zsu and Stavros Christodoulakis",
title = "Introduction",
journal = j-VLDB-J,
volume = "7",
number = "4",
pages = "205--205",
month = dec,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:48 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ozsu:1998:SIM,
author = "M. Tamer {\"O}zsu and Stavros Christodoulakis",
title = "Special Issue on Multimedia Databases: Introduction",
journal = j-VLDB-J,
volume = "7",
number = "4",
pages = "205--205",
month = dec,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 27 10:11:57 MDT 2000",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007004.htm;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/=/=Ouml=zsu:M=_Tamer.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Christodoulakis:Stavros.html;
http://link.springer.de/link/service/journals/00778/bibs/8007004/80070205.htm;
http://link.springer.de/link/service/journals/00778/papers/8007004/80070205.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Garofalakis:1998:PRS,
author = "Minos N. Garofalakis and Banu {\"O}zden and Avi
Silberschatz",
title = "On Periodic Resource scheduling for Continuous-Media
Databases",
journal = j-VLDB-J,
volume = "7",
number = "4",
pages = "206--225",
month = dec,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 27 10:11:57 MDT 2000",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007004.htm;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/=/=Ouml=zden:Banu.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Garofalakis:Minos_N=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Silberschatz:Abraham.html;
http://link.springer.de/link/service/journals/00778/bibs/8007004/80070206.htm;
http://link.springer.de/link/service/journals/00778/papers/8007004/80070206.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
xxauthor = "Minos N. Garofalakis and Banu {\"O}zden and Abraham
Silberschatz",
}
@Article{Jiang:1998:STC,
author = "Haitao Jiang and Ahmed K. Elmagarmid",
title = "Spatial and Temporal Content-Based Access to
Hypervideo Databases",
journal = j-VLDB-J,
volume = "7",
number = "4",
pages = "226--238",
month = dec,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:48 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/e/Elmagarmid:Ahmed_K=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/j/Jiang:Haitao.html;
http://link.springer.de/link/service/journals/00778/bibs/8007004/80070226.htm;
http://link.springer.de/link/service/journals/00778/papers/8007004/80070226.pdf",
abstract = "Providing content-based video query, retrieval and
browsing is the most important goal of a video database
management system (VDBMS). Video data is unique not
only in terms of its spatial and temporal
characteristics, but also in the semantic associations
manifested by the entities present in the video. This
paper introduces a novel video data model called {\em
Logical Hypervideo Data Model}. In addition to
multilevel video abstractions, the model is capable of
representing video entities that users are interested
in (defined as {\em hot objects\/}) and their semantic
associations with other logical video abstractions,
including hot objects themselves. The semantic
associations are modeled as {\em video hyperlinks\/}
and video data with such property are called {\em
hypervideo}. Video hyperlinks provide a flexible and
effective way of browsing video data. Based on the
proposed model, video queries can be specified with
both temporal and spatial constraints, as well as with
semantic descriptions of the video data. The
characteristics of hot objects' spatial and temporal
relations and efficient evaluation of them are also
discussed. Some query examples are given to demonstrate
the expressiveness of the video data model and query
language. Finally, we describe a modular video database
system architecture that our web-based prototype is
based on.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "content-based query; hot object; hypervideo; spatial
and temporal constraint; video database",
}
@Article{Ng:1998:OCO,
author = "Raymond T. Ng and Paul Shum",
title = "Optimal Clip Ordering for Multi-Clip Queries",
journal = j-VLDB-J,
volume = "7",
number = "4",
pages = "239--252",
month = dec,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:48 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/n/Ng:Raymond_T=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Shum:Paul.html;
http://link.springer.de/link/service/journals/00778/bibs/8007004/80070239.htm;
http://link.springer.de/link/service/journals/00778/papers/8007004/80070239.pdf",
abstract = "A multi-clip query requests multiple video clips be
returned as the answer of the query. In many
applications and situations, the order in which these
clips are to be delivered does not matter that much to
the user. This allows the system ample opportunities to
optimize system throughput by using schedules that
maximize the effect of piggybacking. In this paper, we
study how to find such optimal schedules. In
particular, we consider two optimization criteria: (i)
one based on maximizing the number of piggybacked
clips, and (ii) the other based on maximizing the
impact on buffer space. We show that the optimal
schedule under the first criterion is equivalent to a
maximum matching in a suitably defined bipartite graph,
and that under the second criterion, the optimal
schedule is equivalent to a maximum matching in a
suitably defined weighted bipartite graph. Our
experimental results, which are based on realistic
distributions, indicate that both kinds of optimal
schedules can lead to a gain in throughput of over
300\%. And yet the time taken to compute such an
optimal schedule is negligible. Finally, we show how to
deal with clips that are variable in length.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "admission control; bipartite graph matching;
performance of multimedia systems",
}
@Article{Soffer:1998:ISI,
author = "Aya Soffer and Hanan Samet",
title = "Integrating Symbolic Images into a Multimedia Database
System Using Classification and Abstraction
Approaches",
journal = j-VLDB-J,
volume = "7",
number = "4",
pages = "253--274",
month = dec,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:48 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Samet:Hanan.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Soffer:Aya.html;
http://link.springer.de/link/service/journals/00778/bibs/8007004/80070253.htm;
http://link.springer.de/link/service/journals/00778/papers/8007004/80070253.pdf",
abstract = "Symbolic images are composed of a finite set of
symbols that have a semantic meaning. Examples of
symbolic images include maps (where the semantic
meaning of the symbols is given in the legend),
engineering drawings, and floor plans. Two approaches
for supporting queries on symbolic-image databases that
are based on image content are studied. The
classification approach preprocesses all symbolic
images and attaches a semantic classification and an
associated certainty factor to each object that it
finds in the image. The abstraction approach describes
each object in the symbolic image by using a vector
consisting of the values of some of its features (e.g.,
shape, genus, etc.). The approaches differ in the way
in which responses to queries are computed. In the
classification approach, images are retrieved on the
basis of whether or not they contain objects that have
the same classification as the objects in the query. On
the other hand, in the abstraction approach, retrieval
is on the basis of similarity of feature vector values
of these objects. Methods of integrating these two
approaches into a relational multimedia database
management system so that symbolic images can be stored
and retrieved based on their content are described.
Schema definitions and indices that support query
specifications involving spatial as well as contextual
constraints are presented. Spatial constraints may be
based on both locational information (e.g., distance)
and relational information (e.g., north of). Different
strategies for image retrieval for a number of typical
queries using these approaches are described. Estimated
costs are derived for these strategies. Results are
reported of a comparative study of the two approaches
in terms of image insertion time, storage space,
retrieval accuracy, and retrieval time.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "image indexing; multimedia databases; query
optimization; retrieval by content; spatial databases;
symbolic-image databases",
}
@Article{Zezula:1998:ASR,
author = "Pavel Zezula and Pasquale Savino and Giuseppe Amato
and Fausto Rabitti",
title = "Approximate Similarity Retrieval with {M}-Trees",
journal = j-VLDB-J,
volume = "7",
number = "4",
pages = "275--293",
month = dec,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:48 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Amato:Giuseppe.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Rabitti:Fausto.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Savino:Pasquale.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/z/Zezula:Pavel.html;
http://link.springer.de/link/service/journals/00778/bibs/8007004/80070275.htm;
http://link.springer.de/link/service/journals/00778/papers/8007004/80070275.pdf",
abstract = "Motivated by the urgent need to improve the efficiency
of similarity queries, approximate similarity retrieval
is investigated in the environment of a metric tree
index called the M-tree. Three different approximation
techniques are proposed, which show how to forsake
query precision for improved performance. Measures are
defined that can quantify the improvements in
performance efficiency and the quality of
approximations. The proposed approximation techniques
are then tested on various synthetic and real-life
files. The evidence obtained from the experiments
confirms our hypothesis that a high-quality
approximated similarity search can be performed at a
much lower cost than that needed to obtain the exact
results. The proposed approximation techniques are
scalable and appear to be independent of the metric
used. Extensions of these techniques to the
environments of other similarity search indexes are
also discussed.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access structures; approximation algorithms; distance
only data; performance evaluation; similarity search",
}
@Article{Balkir:1998:DPM,
author = "Nevzat Hurkan Balkir and Gultekin {\"O}zsoyoglu",
title = "Delivering Presentations from Multimedia Servers",
journal = j-VLDB-J,
volume = "7",
number = "4",
pages = "294--307",
month = dec,
year = "1998",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:48 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb7.html;
http://link.springer.de/link/service/journals/00778/tocs/t8007004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/=/=Ouml=zsoyoglu:Gultekin.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Balkir:Nevzat_Hurkan.html;
http://link.springer.de/link/service/journals/00778/bibs/8007004/80070294.htm;
http://link.springer.de/link/service/journals/00778/papers/8007004/80070294.pdf",
abstract = "Most multimedia servers reported in the literature are
designed to serve multiple and independent video/audio
streams. We think that, in future, multimedia servers
will also serve complete presentations. Multimedia
presentations provide unique opportunities to develop
algorithms for buffer management and admission control,
as execution-time consumption requirements of
presentations are known a priori. In this paper, we
examine presentations in three different domains
(heavyweight, middleweight, and lightweight) and
provide buffer management and admission control
algorithms for the three domains. We propose two
improvements (flattening and dynamic-adjustments) on
the schedules created for the heavyweight
presentations. Results from a simulation environment
are presented.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "admission control; buffer management; flattening;
multimedia presentations",
}
@Article{Li:1999:FJU,
author = "Zhe Li and Kenneth A. Ross",
title = "Fast Joins Using Join Indices",
journal = j-VLDB-J,
volume = "8",
number = "1",
pages = "1--24",
month = apr,
year = "1999",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:49 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t9008001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Li:Zhe.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Ross:Kenneth_A=.html;
http://link.springer.de/link/service/journals/00778/bibs/9008001/90080001.htm;
http://link.springer.de/link/service/journals/00778/papers/9008001/90080001.pdf",
abstract = "Two new algorithms, ``Jive join'' and ``Slam join,''
are proposed for computing the join of two relations
using a join index. The algorithms are duals: Jive join
range-partitions input relation tuple ids and then
processes each partition, while Slam join forms ordered
runs of input relation tuple ids and then merges the
results. Both algorithms make a single sequential pass
through each input relation, in addition to one pass
through the join index and two passes through a
temporary file, whose size is half that of the join
index. Both algorithms require only that the number of
blocks in main memory is of the order of the square
root of the number of blocks in the smaller relation.
By storing intermediate and final join results in a
vertically partitioned fashion, our algorithms need to
manipulate less data in memory at a given time than
other algorithms. The algorithms are resistant to data
skew and adaptive to memory fluctuations. Selection
conditions can be incorporated into the algorithms.
Using a detailed cost model, the algorithms are
analyzed and compared with competing algorithms. For
large input relations, our algorithms perform
significantly better than Valduriez's algorithm, the
TID join algorithm, and hash join algorithms. An
experimental study is also conducted to validate the
analytical results and to demonstrate the performance
characteristics of each algorithm in practice.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "decision support systems; query processing",
remark = "Check month: April or May??",
}
@Article{Harder:1999:IPS,
author = "Theo H{\"a}rder and G{\"u}nter Sauter and Joachim
Thomas",
title = "The Intrinsic Problems of Structural Heterogeneity and
an Approach to Their Solution",
journal = j-VLDB-J,
volume = "8",
number = "1",
pages = "25--43",
month = apr,
year = "1999",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:49 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t9008001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/H=auml=rder:Theo.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sauter:G=uuml=nter.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Thomas:Joachim.html;
http://link.springer.de/link/service/journals/00778/bibs/9008001/90080025.htm;
http://link.springer.de/link/service/journals/00778/papers/9008001/90080025.pdf",
abstract = "This paper focuses on the problems that arise when
integrating data from heterogeneous sources in a
single, unified database view. At first, we give a
detailed analysis of the kinds of structural
heterogeneity that occur when unified views are derived
from different database systems. We present the results
in a multiple tier architecture which distinguishes
different levels of heterogeneity and relates them to
their underlying causes as well as to the mapping
conflicts resulting from the view derivation process.
As the second essential contribution, the paper
presents our approach to a mapping language solving the
identified conflicts. The main characteristics of the
language are its descriptiveness, its capability to map
between schemas written in the relational,
object-oriented, ER, or EXPRESS data model, and its
facilities for specifying user-defined update
operations on the view that are to be propagated to the
data sources. Finally, we briefly discuss how this
mapping information is employed to convert queries
formulated with respect to the integrated view, into
database operations over the heterogeneous data
sources.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "heterogeneity; legacy systems; mapping language;
schema integration; schema mapping; updatable views",
remark = "Check month: April or May??",
}
@Article{Huang:1999:CTP,
author = "Yueh-Min Huang and Jen-Wen Ding and Shiao-Li Tsao",
title = "Constant Time Permutation: An Efficient Block
Allocation Strategy for Variable-Bit-Rate Continuous
Media Data",
journal = j-VLDB-J,
volume = "8",
number = "1",
pages = "44--54",
month = apr,
year = "1999",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:49 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t9008001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/Ding:Jen=Wen.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Huang:Yueh=Min.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Tsao:Shiao=Li.html;
http://link.springer.de/link/service/journals/00778/bibs/9008001/90080044.htm;
http://link.springer.de/link/service/journals/00778/papers/9008001/90080044.pdf",
abstract = "To provide high accessibility of continuous-media (CM)
data, CM servers generally stripe data across multiple
disks. Currently, the most widely used striping scheme
for CM data is round-robin permutation (RRP).
Unfortunately, when RRP is applied to variable-bit-rate
(VBR) CM data, load imbalance across multiple disks
occurs, thereby reducing overall system performance. In
this paper, the performance of a VBR CM server with RRP
is analyzed. In addition, we propose an efficient
striping scheme called constant time permutation (CTP),
which takes the VBR characteristic into account and
obtains a more balanced load than RRP. Analytic models
of both RRP and CTP are presented, and the models are
verified via trace-driven simulations. Analysis and
simulation results show that CTP can substantially
increase the number of clients supported, though it
might introduce a few seconds/minutes of initial
delay.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "continuous-media server; data placement; load
balancing; striping; video-on-demand (VOD)",
remark = "Check month: April or May??",
}
@Article{Kabra:1999:OOO,
author = "Navin Kabra and David J. DeWitt",
title = "{OPT++}: an object-oriented implementation for
extensible database query optimization",
journal = j-VLDB-J,
volume = "8",
number = "1",
pages = "55--78",
month = apr,
year = "1999",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:49 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t9008001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/d/DeWitt:David_J=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kabra:Navin.html;
http://link.springer.de/link/service/journals/00778/bibs/9008001/90080055.htm;
http://link.springer.de/link/service/journals/00778/papers/9008001/90080055.pdf",
abstract = "In this paper we describe the design and
implementation of OPT++, a tool for extensible database
query optimization that uses an object-oriented design
to simplify the task of implementing, extending, and
modifying an optimizer. Building an optimizer using
OPT++ makes it easy to extend the query algebra (to add
new query algebra operators and physical implementation
algorithms to the system), easy to change the search
space, and also to change the search strategy.
Furthermore, OPT++ comes equipped with a number of
search strategies that are available for use by an
optimizer-implementor. OPT++ considerably simplifies
both, the task of implementing an optimizer for a new
database system, and the task of evaluating alternative
optimization techniques and strategies to decide what
techniques are best suited for that database system. We
present the results of a series of performance studies.
These results validate our design and show that, in
spite of its flexibility, OPT++ can be used to build
efficient optimizers.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "extensibility; object-relational databases; query
optimization; software architecture",
remark = "Check month: April or May??",
}
@Article{Krivokapic:1999:DDD,
author = "Natalija Krivokapi{\'c} and Alfons Kemper and Ehud
Gudes",
title = "Deadlock Detection in Distributed Database Systems: a
New Algorithm and a Comparative Performance Analysis",
journal = j-VLDB-J,
volume = "8",
number = "2",
pages = "79--100",
month = apr,
year = "1999",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:50 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t9008002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Gudes:Ehud.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kemper:Alfons.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Krivokapic:Natalija.html;
http://link.springer.de/link/service/journals/00778/bibs/9008002/90080079.htm;
http://link.springer.de/link/service/journals/00778/papers/9008002/90080079.pdf",
abstract = "This paper attempts a comprehensive study of deadlock
detection in distributed database systems. First, the
two predominant deadlock models in these systems and
the four different distributed deadlock detection
approaches are discussed. Afterwards, a new deadlock
detection algorithm is presented. The algorithm is
based on dynamically creating {\em deadlock detection
agents\/} (DDAs), each being responsible for detecting
deadlocks in one connected component of the global
wait-for-graph (WFG). The DDA scheme is a
``self-tuning'' system: after an initial warm-up phase,
dedicated DDAs will be formed for ``centers of
locality'', i.e., parts of the system where many
conflicts occur. A dynamic shift in locality of the
distributed system will be responded to by
automatically creating new DDAs while the obsolete ones
terminate. In this paper, we also compare the most
competitive representative of each class of algorithms
suitable for distributed database systems based on a
simulation model, and point out their relative
strengths and weaknesses. The extensive experiments we
carried out indicate that our newly proposed deadlock
detection algorithm outperforms the other algorithms in
the vast majority of configurations and workloads and,
in contrast to all other algorithms, is very robust
with respect to differing load and access profiles.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "comparative performance analysis; deadlock detection;
distributed database systems; simulation study",
remark = "Check month: April or May??",
}
@Article{Boncz:1999:MPQ,
author = "Peter A. Boncz and Martin L. Kersten",
title = "{MIL} primitives for querying a fragmented world",
journal = j-VLDB-J,
volume = "8",
number = "2",
pages = "101--119",
month = apr,
year = "1999",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:50 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t9008002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Boncz:Peter_A=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kersten:Martin_L=.html;
http://link.springer.de/link/service/journals/00778/bibs/9008002/90080101.htm;
http://link.springer.de/link/service/journals/00778/papers/9008002/90080101.pdf",
abstract = "In query-intensive database application areas, like
decision support and data mining, systems that use
vertical fragmentation have a significant performance
advantage. In order to support relational or object
oriented applications on top of such a fragmented data
model, a flexible yet powerful intermediate language is
needed. This problem has been successfully tackled in
Monet, a modern extensible database kernel developed by
our group. We focus on the design choices made in the
Monet interpreter language (MIL), its algebraic query
language, and outline how its concept of tactical
optimization enhances and simplifies the optimization
of complex queries. Finally, we summarize the
experience gained in Monet by creating a highly
efficient implementation of MIL.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database systems; main-memory techniques; query
languages; query optimization; vertical fragmentation",
remark = "Check month: April or May??",
}
@Article{Aslan:1999:SHR,
author = "Goksel Aslan and Dennis McLeod",
title = "Semantic Heterogeneity Resolution in Federated
Databases by Metadata Implantation and Stepwise
Evolution",
journal = j-VLDB-J,
volume = "8",
number = "2",
pages = "120--132",
month = apr,
year = "1999",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:50 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t9008002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Aslan:Goksel.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/McLeod:Dennis.html;
http://link.springer.de/link/service/journals/00778/bibs/9008002/90080120.htm;
http://link.springer.de/link/service/journals/00778/papers/9008002/90080120.pdf",
abstract = "A key aspect of interoperation among data-intensive
systems involves the mediation of metadata and
ontologies across database boundaries. One way to
achieve such mediation between a local database and a
remote database is to fold remote metadata into the
local metadata, thereby creating a common platform
through which information sharing and exchange becomes
possible. Schema implantation and semantic evolution,
our approach to the metadata folding problem, is a
partial database integration scheme in which remote and
local (meta)data are integrated in a stepwise manner
over time. We introduce metadata implantation and
stepwise evolution techniques to interrelate database
elements in different databases, and to resolve
conflicts on the structure and semantics of database
elements (classes, attributes, and individual
instances). We employ a semantically rich canonical
data model, and an incremental integration and semantic
heterogeneity resolution scheme. In our approach,
relationships between local and remote information
units are determined whenever enough knowledge about
their semantics is acquired. The metadata folding
problem is solved by implanting remote database
elements into the local database, a process that
imports remote database elements into the local
database environment, hypothesizes the relevance of
local and remote classes, and customizes the
organization of remote metadata. We have implemented a
prototype system and demonstrated its use in an
experimental neuroscience environment.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database integration; database interoperability;
federated databases; schema evolution; semantic
heterogeneity resolution",
remark = "Check month: April or May??",
}
@Article{Law:1999:ESI,
author = "Kelvin K. W. Law and John C. S. Lui and Leana
Golubchik",
title = "Efficient Support for Interactive Service in
Multi-Resolution {VOD} Systems",
journal = j-VLDB-J,
volume = "8",
number = "2",
pages = "133--153",
month = apr,
year = "1999",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:50 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t9008002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Golubchik:Leana.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Law:Kelvin_K=_W=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Lui:John_C=_S=.html;
http://link.springer.de/link/service/journals/00778/bibs/9008002/90080133.htm;
http://link.springer.de/link/service/journals/00778/papers/9008002/90080133.pdf",
abstract = "Advances in high-speed networks and multimedia
technologies have made it feasible to provide
video-on-demand (VOD) services to users. However, it is
still a challenging task to design a cost-effective VOD
system that can support a large number of clients (who
may have different quality of service (QoS)
requirements) and, at the same time, provide different
types of VCR functionalities. Although it has been
recognized that VCR operations are important
functionalities in providing VOD service, techniques
proposed in the past for providing VCR operations may
require additional system resources, such as extra disk
I/O, additional buffer space, as well as network
bandwidth. In this paper, we consider the design of a
VOD storage server that has the following features: (1)
provision of different levels of display resolutions to
users who have different QoS requirements, (2)
provision of different types of VCR functionalities,
such as fast forward and rewind, without imposing
additional demand on the system buffer space, I/O
bandwidth, and network bandwidth, and (3) guarantees of
the load-balancing property across all disks during
normal and VCR display periods. The above-mentioned
features are especially important because they simplify
the design of the buffer space, I/O, and network
resource allocation policies of the VOD storage system.
The load-balancing property also ensures that no single
disk will be the bottleneck of the system. In this
paper, we propose data block placement, admission
control, and I/O-scheduling algorithms, as well as
determine the corresponding buffer space requirements
of the proposed VOD storage system. We show that the
proposed VOD system can provide VCR and
multi-resolution services to the viewing clients and at
the same time maintain the load-balancing property.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "interactive services; multi-resolution services;
multimedia servers; VOD systems",
remark = "Check month: April or May??",
}
@Article{Shmueli:2000:FVP,
author = "O. Shmueli and J. Widom",
title = "Foreword by the {VLDB} `98 {PC Chairmen}",
journal = j-VLDB-J,
volume = "8",
number = "3--4",
pages = "155--155",
month = feb,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:51 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Widom:2000:BPV,
author = "Jennifer Widom and Oded Shmueli",
title = "Best Papers of {VLDB `98, New York: Foreword by the
VLDB `98 PC Chairmen: Best Papers of VLDB `98}",
journal = j-VLDB-J,
volume = "8",
number = "3--4",
pages = "155--155",
month = feb,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 27 10:11:55 MDT 2000",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t0008003.htm;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Shmueli:Oded.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/w/Widom:Jennifer.html;
http://link.springer.de/link/service/journals/00778/bibs/0008003/00080155.htm;
http://link.springer.de/link/service/journals/00778/papers/0008003/00080155.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
xxauthor = "O. Shmueli and J. Widom",
}
@Article{Braumandl:2000:FJP,
author = "Reinhard Braumandl and Jens Clau{\ss}en and Alfons
Kemper and Donald Kossmann",
title = "Functional-Join Processing",
journal = j-VLDB-J,
volume = "8",
number = "3--4",
pages = "156--177",
month = feb,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:51 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t0008003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Braumandl:Reinhard.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Clau=szlig=en:Jens.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kemper:Alfons.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kossmann:Donald.html;
http://link.springer.de/link/service/journals/00778/bibs/0008003/00080156.htm;
http://link.springer.de/link/service/journals/00778/papers/0008003/00080156.pdf",
abstract = "Inter-object references are one of the key concepts of
object-relational and object-oriented database systems.
In this work, we investigate alternative techniques to
implement inter-object references and make the best use
of them in query processing, i.e., in evaluating
functional joins. We will give a comprehensive overview
and performance evaluation of all known techniques for
simple (single-valued) as well as multi-valued
functional joins. Furthermore, we will describe special
{\em order-preserving\/\/} functional-join techniques
that are particularly attractive for decision support
queries that require ordered results. While most of the
presentation of this paper is focused on
object-relational and object-oriented database systems,
some of the results can also be applied to plain
relational databases because {\em index nested-loop
joins\/\/} along key/foreign-key relationships, as they
are frequently found in relational databases, are just
one particular way to execute a functional join.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "functional join; logical OID; object identifier;
order-preserving join; physical OID; pointer join;
query processing",
}
@Article{George:2000:SBF,
author = "Binto George and Jayant R. Haritsa",
title = "Secure Buffering in Firm Real-Time Database Systems",
journal = j-VLDB-J,
volume = "8",
number = "3--4",
pages = "178--198",
month = feb,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:51 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t0008003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/George:Binto.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/h/Haritsa:Jayant_R=.html;
http://link.springer.de/link/service/journals/00778/bibs/0008003/00080178.htm;
http://link.springer.de/link/service/journals/00778/papers/0008003/00080178.pdf",
abstract = "Many real-time database applications arise in
electronic financial services, safety-critical
installations and military systems where enforcing is
crucial to the success of the enterprise. We
investigate here the performance implications, in terms
of killed transactions, of guaranteeing {\em
multi-level secrecy\/} in a real-time database system
supporting applications with {\em firm\/} deadlines. In
particular, we focus on the {\em buffer management\/}
aspects of this issue. Our main contributions are the
following. First, we identify the importance and
difficulties of providing secure buffer management in
the real-time database environment. Second, we present
, a novel buffer management algorithm that provides
{\em covert-channel-free\/} security. SABRE employs a
fully dynamic one-copy allocation policy for efficient
usage of buffer resources. It also incorporates several
optimizations for reducing the overall number of killed
transactions and for decreasing the unfairness in the
distribution of killed transactions across security
levels. Third, using a detailed simulation model, the
real-time performance of SABRE is evaluated against
unsecure conventional and real-time buffer management
policies for a variety of security-classified
transaction workloads and system configurations. Our
experiments show that SABRE provides security with only
a modest drop in real-time performance. Finally, we
evaluate SABRE's performance when augmented with the
GUARD adaptive admission control policy. Our
experiments show that this combination provides close
to ideal fairness for real-time applications that can
tolerate covert-channel bandwidths of up to one bit per
second (a limit specified in military standards).",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "buffer management; covert channels; firm deadlines;
real-time database",
}
@Article{Muth:2000:LLS,
author = "Peter Muth and Patrick E. O'Neil and Achim Pick and
Gerhard Weikum",
title = "The {LHAM} Log-Structured History Data Access Method",
journal = j-VLDB-J,
volume = "8",
number = "3--4",
pages = "199--221",
month = feb,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:51 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t0008003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Muth:Peter.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/o/O=Neil:Patrick_E=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Pick:Achim.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/w/Weikum:Gerhard.html;
http://link.springer.de/link/service/journals/00778/bibs/0008003/00080199.htm;
http://link.springer.de/link/service/journals/00778/papers/0008003/00080199.pdf",
abstract = "Numerous applications such as stock market or medical
information systems require that both historical and
current data be logically integrated into a temporal
database. The underlying access method must support
different forms of ``time-travel'' queries, the
migration of old record versions onto inexpensive
archive media, and high insertion and update rates.
This paper presents an access method for
transaction-time temporal data, called the
log-structured history data access method (LHAM) that
meets these demands. The basic principle of LHAM is to
partition the data into successive components based on
the timestamps of the record versions. Components are
assigned to different levels of a storage hierarchy,
and incoming data is continuously migrated through the
hierarchy. The paper discusses the LHAM concepts,
including concurrency control and recovery, our
full-fledged LHAM implementation, and experimental
performance results based on this implementation. A
detailed comparison with the TSB-tree, both
analytically and based on experiments with real
implementations, shows that LHAM is highly superior in
terms of insert performance, while query performance is
in almost all cases at least as good as for the
TSB-tree; in many cases it is much better.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data warehouses; index structures; performance;
storage systems; temporal databases",
}
@Article{Gibson:2000:CCD,
author = "David Gibson and Jon M. Kleinberg and Prabhakar
Raghavan",
title = "Clustering Categorical Data: An Approach Based on
Dynamical Systems",
journal = j-VLDB-J,
volume = "8",
number = "3--4",
pages = "222--236",
month = feb,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:51 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t0008003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Gibson:David.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kleinberg:Jon_M=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Raghavan:Prabhakar.html;
http://link.springer.de/link/service/journals/00778/bibs/0008003/00080222.htm;
http://link.springer.de/link/service/journals/00778/papers/0008003/00080222.pdf",
abstract = "We describe a novel approach for clustering
collections of sets, and its application to the
analysis and mining of categorical data. By
``categorical data,'' we mean tables with fields that
cannot be naturally ordered by a metric --- e.g., the
names of producers of automobiles, or the names of
products offered by a manufacturer. Our approach is
based on an iterative method for assigning and
propagating weights on the categorical values in a
table; this facilitates a type of similarity measure
arising from the co-occurrence of values in the
dataset. Our techniques can be studied analytically in
terms of certain types of non-linear dynamical
systems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "categorical data; clustering; data mining; dynamical
systems; hypergraphs",
}
@Article{Knorr:2000:DBO,
author = "Edwin M. Knorr and Raymond T. Ng and Vladimir
Tucakov",
title = "Distance-Based Outliers: Algorithms and Applications",
journal = j-VLDB-J,
volume = "8",
number = "3--4",
pages = "237--253",
month = feb,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:51 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t0008003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Knorr:Edwin_M=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/n/Ng:Raymond_T=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Tucakov:V=.html;
http://link.springer.de/link/service/journals/00778/bibs/0008003/00080237.htm;
http://link.springer.de/link/service/journals/00778/papers/0008003/00080237.pdf",
abstract = "This paper deals with finding outliers (exceptions) in
large, multidimensional datasets. The identification of
outliers can lead to the discovery of truly unexpected
knowledge in areas such as electronic commerce, credit
card fraud, and even the analysis of performance
statistics of professional athletes. Existing methods
that we have seen for finding outliers can only deal
efficiently with two dimensions/attributes of a
dataset. In this paper, we study the notion of {\em
DB\/} ({\em distance-based\/}) outliers. Specifically,
we show that (i) outlier detection can be done {\em
efficiently\/} for {\em large\/} datasets, and for
$k$-dimensional datasets with large values of $k$
(e.g., $ k \ge 5$); and (ii), outlier detection is a
{\em meaningful\/} and important knowledge discovery
task. First, we present two simple algorithms, both
having a complexity of $ O(k \: N^2)$, $k$ being the
dimensionality and $N$ being the number of objects in
the dataset. These algorithms readily support datasets
with many more than two attributes. Second, we present
an optimized cell-based algorithm that has a complexity
that is linear with respect to $N$, but exponential
with respect to $k$. We provide experimental results
indicating that this algorithm significantly
outperforms the two simple algorithms for $ k \leq 4$.
Third, for datasets that are mainly disk-resident, we
present another version of the cell-based algorithm
that guarantees at most three passes over a dataset.
Again, experimental results show that this algorithm is
by far the best for $ k \leq 4$. Finally, we discuss
our work on three real-life applications, including one
on spatio-temporal data (e.g., a video surveillance
application), in order to confirm the relevance and
broad applicability of {\em DB\/} outliers.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "algorithms; data mining; data mining applications;
outliers\slash exceptions",
}
@Article{Korn:2000:QDM,
author = "Flip Korn and Alexandros Labrinidis and Yannis Kotidis
and Christos Faloutsos",
title = "Quantifiable Data Mining Using Ratio Rules",
journal = j-VLDB-J,
volume = "8",
number = "3--4",
pages = "254--266",
month = feb,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:51 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t0008003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/f/Faloutsos:Christos.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Korn:Flip.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/k/Kotidis:Yannis.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Labrinidis:Alexandros.html;
http://link.springer.de/link/service/journals/00778/bibs/0008003/00080254.htm;
http://link.springer.de/link/service/journals/00778/papers/0008003/00080254.pdf",
abstract = "Association Rule Mining algorithms operate on a data
matrix (e.g., customers $ \times $ products) to derive
association rules [AIS93b, SA96]. We propose a new
paradigm, namely, {\em Ratio Rules}, which are
quantifiable in that we can measure the ``goodness'' of
a set of discovered rules. We also propose the
``guessing error'' as a measure of the ``goodness'',
that is, the root-mean-square error of the
reconstructed values of the cells of the given matrix,
when we pretend that they are unknown. Another
contribution is a novel method to guess missing/hidden
values from the Ratio Rules that our method derives.
For example, if somebody bought $ 10 o f m i l k a n d
$3 of bread, our rules can ``guess'' the amount spent
on butter. Thus, unlike association rules, Ratio Rules
can perform a variety of important tasks such as
forecasting, answering ``what-if'' scenarios, detecting
outliers, and visualizing the data. Moreover, we show
that we can compute Ratio Rules in a {\em single\/}
pass over the data set with small memory requirements
(a few small matrices), in contrast to association rule
mining methods which require multiple passes and/or
large memory. Experiments on several real data sets
(e.g., basketball and baseball statistics, biological
data) demonstrate that the proposed method: (a) leads
to rules that make sense; (b) can find large itemsets
in binary matrices, even in the presence of noise; and
(c) consistently achieves a ``guessing error'' of up to
5 times less than using straightforward column
averages.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data mining; forecasting; guessing error; knowledge
discovery",
}
@Article{Torp:2000:ETD,
author = "Kristian Torp and Christian S. Jensen and Richard
Thomas Snodgrass",
title = "Effective Timestamping in Databases",
journal = j-VLDB-J,
volume = "8",
number = "3--4",
pages = "267--288",
month = feb,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:51 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t0008003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/j/Jensen:Christian_S=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Snodgrass:Richard_T=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Torp:Kristian.html;
http://link.springer.de/link/service/journals/00778/bibs/0008003/00080267.htm;
http://link.springer.de/link/service/journals/00778/papers/0008003/00080267.pdf",
abstract = "Many existing database applications place various
timestamps on their data, rendering temporal values
such as dates and times prevalent in database tables.
During the past two decades, several dozen temporal
data models have appeared, all with timestamps being
integral components. The models have used timestamps
for encoding two specific temporal aspects of database
facts, namely transaction time, when the facts are
current in the database, and valid time, when the facts
are true in the modeled reality. However, with few
exceptions, the assignment of timestamp values has been
considered only in the context of individual
modification statements. This paper takes the next
logical step: It considers the use of timestamping for
capturing transaction and valid time in the context of
transactions. The paper initially identifies and
analyzes several problems with straightforward
timestamping, then proceeds to propose a variety of
techniques aimed at solving these problems.
Timestamping the results of a transaction with the
commit time of the transaction is a promising approach.
The paper studies how this timestamping may be done
using a spectrum of techniques. While many database
facts are valid until {\em now}, the current time, this
value is absent from the existing temporal types.
Techniques that address this problem using different
substitute values are presented. Using a stratum
architecture, the performance of the different proposed
techniques are studied. Although querying and modifying
time-varying data is accompanied by a number of subtle
problems, we present a comprehensive approach that
provides application programmers with simple,
consistent, and efficient support for modifying
bitemporal databases in the context of user
transactions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "timestamping; transactions",
}
@Article{Sheikholeslami:2000:WWB,
author = "Gholamhosein Sheikholeslami and Surojit Chatterjee and
Aidong Zhang",
title = "{WaveCluster}: a Wavelet Based Clustering Approach for
Spatial Data in Very Large Databases",
journal = j-VLDB-J,
volume = "8",
number = "3--4",
pages = "289--304",
month = feb,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:51 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t0008003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Chatterjee:Surojit.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sheikholeslami:Gholamhosein.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/z/Zhang:Aidong.html;
http://link.springer.de/link/service/journals/00778/bibs/0008003/00080289.htm;
http://link.springer.de/link/service/journals/00778/papers/0008003/00080289.pdf",
abstract = "Many applications require the management of spatial
data in a multidimensional feature space. Clustering
large spatial databases is an important problem, which
tries to find the densely populated regions in the
feature space to be used in data mining, knowledge
discovery, or efficient information retrieval. A good
clustering approach should be efficient and detect
clusters of arbitrary shape. It must be insensitive to
the noise (outliers) and the order of input data. We
propose {\em WaveCluster}, a novel clustering approach
based on wavelet transforms, which satisfies all the
above requirements. Using the multiresolution property
of wavelet transforms, we can effectively identify
arbitrarily shaped clusters at different degrees of
detail. We also demonstrate that {\em WaveCluster\/} is
highly efficient in terms of time complexity.
Experimental results on very large datasets are
presented, which show the efficiency and effectiveness
of the proposed approach compared to the other recent
clustering methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Pacitti:2000:UPS,
author = "Esther Pacitti and Eric Simon",
title = "Update Propagation Strategies to Improve Freshness in
Lazy Master Replicated Databases",
journal = j-VLDB-J,
volume = "8",
number = "3--4",
pages = "305--318",
month = feb,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:51 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t0008003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Pacitti:Esther.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Simon:Eric.html;
http://link.springer.de/link/service/journals/00778/bibs/0008003/00080305.htm;
http://link.springer.de/link/service/journals/00778/papers/0008003/00080305.pdf",
abstract = "Many distributed database applications need to
replicate data to improve data availability and query
response time. The two-phase commit protocol guarantees
mutual consistency of replicated data but does not
provide good performance. Lazy replication has been
used as an alternative solution in several types of
applications such as on-line financial transactions and
telecommunication systems. In this case, mutual
consistency is relaxed and the concept of freshness is
used to measure the deviation between replica copies.
In this paper, we propose two update propagation
strategies that improve freshness. Both of them use
immediate propagation: updates to a primary copy are
propagated towards a slave node as soon as they are
detected at the master node without waiting for the
commitment of the update transaction. Our performance
study shows that our strategies can improve data
freshness by up to five times compared with the
deferred approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data replication; distributed databases; performance
evaluation",
}
@Article{Liang:2000:OMD,
author = "Weifa Liang and Maria E. Orlowska and Jeffrey X. Yu",
title = "Optimizing Multiple Dimensional Queries Simultaneously
in Multidimensional Databases",
journal = j-VLDB-J,
volume = "8",
number = "3--4",
pages = "319--338",
month = feb,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:51 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb8.html;
http://link.springer.de/link/service/journals/00778/tocs/t0008003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Liang:Weifa.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/o/Orlowska:Maria_E=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/y/Yu:Jeffrey_X=.html;
http://link.springer.de/link/service/journals/00778/bibs/0008003/00080319.htm;
http://link.springer.de/link/service/journals/00778/papers/0008003/00080319.pdf",
abstract = "Some significant progress related to multidimensional
data analysis has been achieved in the past few years,
including the design of fast algorithms for computing
datacubes, selecting some precomputed group-bys to
materialize, and designing efficient storage structures
for multidimensional data. However, little work has
been carried out on multidimensional query optimization
issues. Particularly the response time (or evaluation
cost) for answering several related dimensional queries
simultaneously is crucial to the OLAP applications.
Recently, Zhao et al. first exploited this problem by
presenting three heuristic algorithms. In this paper we
first consider in detail two cases of the problem in
which all the queries are either hash-based star joins
or index-based star joins only. In the case of the
hash-based star join, we devise a polynomial
approximation algorithm which delivers a plan whose
evaluation cost is $ O(n^\epsilon) $ times the optimal,
where $n$ is the number of queries and $ \epsilon $ is
a fixed constant with $ 0 < \epsilon \leq 1$. We also
present an exponential algorithm which delivers a plan
with the optimal evaluation cost. In the case of the
index-based star join, we present a heuristic algorithm
which delivers a plan whose evaluation cost is $n$
times the optimal, and an exponential algorithm which
delivers a plan with the optimal evaluation cost. We
then consider a general case in which both hash-based
star-join and index-based star-join queries are
included. For this case, we give a possible improvement
on the work of Zhao et al., based on an analysis of
their solutions. We also develop another heuristic and
an exact algorithm for the problem. We finally conduct
a performance study by implementing our algorithms. The
experimental results demonstrate that the solutions
delivered for the restricted cases are always within
two times of the optimal, which confirms our
theoretical upper bounds. Actually these experiments
produce much better results than our theoretical
estimates. To the best of our knowledge, this is the
only development of polynomial algorithms for the first
two cases which are able to deliver plans with
deterministic performance guarantees in terms of the
qualities of the plans generated. The previous
approaches including that of [ZDNS98] may generate a
feasible plan for the problem in these two cases, but
they do not provide any performance guarantee, i.e.,
the plans generated by their algorithms can be
arbitrarily far from the optimal one.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data warehousing; MDDBs; multiple dimensional query
optimization; OLAP; query modeling",
}
@Article{Atzeni:2000:DWG,
author = "Paolo Atzeni and Alberto O. Mendelzon",
title = "Databases and the {Web}: Guest Editorial: Databases
and the {Web}",
journal = j-VLDB-J,
volume = "9",
number = "1",
pages = "1--1",
month = mar,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 27 10:11:55 MDT 2000",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb9.html;
http://link.springer.de/link/service/journals/00778/tocs/t0009001.htm;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/a/Atzeni:Paolo.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Mendelzon:Alberto_O=.html;
http://link.springer.de/link/service/journals/00778/bibs/0009001/00090001.htm;
http://link.springer.de/link/service/journals/00778/papers/0009001/00090001.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Atzeni:2000:GE,
author = "Paolo Atzeni and Alberto O. Mendelzon",
title = "Guest editorial",
journal = j-VLDB-J,
volume = "9",
number = "1",
pages = "1--1",
month = mar,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:52 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chidlovskii:2000:SCW,
author = "Boris Chidlovskii and Uwe M. Borghoff",
title = "Semantic caching of {Web} queries",
journal = j-VLDB-J,
volume = "9",
number = "1",
pages = "2--17",
month = mar,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:52 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb9.html;
http://link.springer.de/link/service/journals/00778/tocs/t0009001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Borghoff:Uwe_M=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Chidlovskii:Boris.html;
http://link.springer.de/link/service/journals/00778/bibs/0009001/00090002.htm;
http://link.springer.de/link/service/journals/00778/papers/0009001/00090002.pdf",
abstract = "In meta-searchers accessing distributed Web-based
information repositories, performance is a major issue.
Efficient query processing requires an appropriate
caching mechanism. Unfortunately, standard page-based
as well as tuple-based caching mechanisms designed for
conventional databases are not efficient on the Web,
where keyword-based querying is often the only way to
retrieve data. In this work, we study the problem of
semantic caching of Web queries and develop a caching
mechanism for conjunctive Web queries based on {\em
signature files}. Our algorithms cope with both
relations of semantic containment and intersection
between a query and the corresponding cache items. We
also develop the cache replacement strategy to treat
situations when cached items differ in size and
contribution when providing partial query answers. We
report results of experiments and show how the caching
mechanism is realized in the Knowledge Broker system.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "experiments; query algorithms; region containment;
semantic caching; signature files",
}
@Article{Gruser:2000:LRT,
author = "Jean-Robert Gruser and Louiqa Raschid and Vladimir
Zadorozhny and Tao Zhan",
title = "Learning response time for {WebSources} using query
feedback and application in query optimization",
journal = j-VLDB-J,
volume = "9",
number = "1",
pages = "18--37",
month = mar,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:52 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb9.html;
http://link.springer.de/link/service/journals/00778/tocs/t0009001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/g/Gruser:Jean=Robert.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/r/Raschid:Louiqa.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/z/Zadorozhny:Vladimir.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/z/Zhan:Tao.html;
http://link.springer.de/link/service/journals/00778/bibs/0009001/00090018.htm;
http://link.springer.de/link/service/journals/00778/papers/0009001/00090018.pdf",
abstract = "The rapid growth of the Internet and support for
interoperability protocols has increased the number of
Web accessible sources, WebSources. Current wrapper
mediator architectures need to be extended with a
wrapper cost model (WCM) for WebSources that can
estimate the response time (delays) to access sources
as well as other relevant statistics. In this paper, we
present a Web prediction tool (WebPT), a tool that is
based on learning using query feedback from WebSources.
The WebPT uses dimensions time of day, day, and
quantity of data, to learn response times from a
particular WebSource, and to predict the expected
response time (delay) for some query. Experiment data
was collected from several sources, and those
dimensions that were significant in estimating the
response time were determined. We then trained the
WebPT on the collected data, to use the three
dimensions mentioned above, and to predict the response
time, as well as a confidence in the prediction. We
describe the WebPT learning algorithms, and report on
the WebPT learning for WebSources. Our research shows
that we can improve the quality of learning by tuning
the WebPT features, e.g., training the WebPT using a
logarithm of the input training data; including
significant dimensions in the WebPT; or changing the
ordering of dimensions. A comparison of the WebPT with
more traditional neural network (NN) learning has been
performed, and we briefly report on the comparison. We
then demonstrate how the WebPT prediction of delay may
be used by a scrambling enabled optimizer. A scrambling
algorithm identifies some critical points of delay,
where it makes a decision to scramble (modify) a plan,
to attempt to hide the expected delay by computing some
other part of the plan that is unaffected by the delay.
We explore the space of real delay at a WebSource,
versus the WebPT prediction of this delay, with respect
to critical points of delay in specific plans. We
identify those cases where WebPT overestimation or
underestimation of the real delay results in a penalty
in the scrambling enabled optimizer, and those cases
where there is no penalty. Using the experimental data
and WebPT learning, we test how good the WebPT is in
minimizing these penalties.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data-intensive applications on the Web; query
languages and systems for Web data",
}
@Article{Fernandez:2000:DSW,
author = "Mary Fern{\'a}ndez and Daniela Florescu and Alon Levy
and Dan Suciu",
title = "Declarative specification of {Web} sites with {S}",
journal = j-VLDB-J,
volume = "9",
number = "1",
pages = "38--55",
month = mar,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:52 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb9.html;
http://link.springer.de/link/service/journals/00778/tocs/t0009001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/f/Fernandez:Mary_F=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/f/Florescu:Daniela.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Levy:Alon_Y=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Suciu:Dan.html;
http://link.springer.de/link/service/journals/00778/bibs/0009001/00090038.htm;
http://link.springer.de/link/service/journals/00778/papers/0009001/00090038.pdf",
abstract = "S is a system for implementing {\em data-intensive\/}
Web sites, which typically integrate information from
multiple data sources and have complex structure. S's
key idea is separating the management of a Web site's
data, the specification of its content and structure,
and the visual representation of its pages. S provides
a declarative {\em query language\/} for specifying a
site's content and structure, and a simple {\em
template language\/} for specifying a site's HTML
representation. This paper contains a comprehensive
description of the S system and details the benefits of
declarative site specification. We describe our
experiences using S in a production application and
describe three different, but complementary, systems
that extend and improve upon S's original ideas.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "declarative query languages; web-site management",
xxauthor = "Mary F. Fernandez and Daniela Florescu and Alon Y.
Levy and Dan Suciu",
xxtitle = "Declarative Specification of {Web} Sites with
{Strudel}",
}
@Article{Berendt:2000:ANB,
author = "Bettina Berendt and Myra Spiliopoulou",
title = "Analysis of navigation behaviour in {Web} sites
integrating multiple information systems",
journal = j-VLDB-J,
volume = "9",
number = "1",
pages = "56--75",
month = mar,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:52 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb9.html;
http://link.springer.de/link/service/journals/00778/tocs/t0009001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Berendt:Bettina.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Spiliopoulou:Myra.html;
http://link.springer.de/link/service/journals/00778/bibs/0009001/00090056.htm;
http://link.springer.de/link/service/journals/00778/papers/0009001/00090056.pdf",
abstract = "The analysis of web usage has mostly focused on sites
composed of conventional static pages. However, huge
amounts of information available in the web come from
databases or other data collections and are presented
to the users in the form of dynamically generated
pages. The query interfaces of such sites allow the
specification of many search criteria. Their generated
results support navigation to pages of results
combining cross-linked data from many sources. For the
analysis of visitor navigation behaviour in such web
sites, we propose the web usage miner (WUM), which
discovers navigation patterns subject to advanced
statistical and structural constraints. Since our
objective is the discovery of interesting navigation
patterns, we do not focus on accesses to individual
pages. Instead, we construct conceptual hierarchies
that reflect the query capabilities used in the
production of those pages. Our experiments with a real
web site that integrates data from multiple databases,
the German SchulWeb, demonstrate the appropriateness of
WUM in discovering navigation patterns and show how
those discoveries can help in assessing and improving
the quality of the site.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "conceptual hierarchies; data mining; query
capabilities; Web databases; Web query interfaces; Web
usage mining",
}
@Article{Buneman:2000:UQL,
author = "Peter Buneman and Mary F. Fernandez and Dan Suciu",
title = "{UnQL}: a query language and algebra for
semistructured data based on structural recursion",
journal = j-VLDB-J,
volume = "9",
number = "1",
pages = "76--110",
month = mar,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:52 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb9.html;
http://link.springer.de/link/service/journals/00778/tocs/t0009001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/b/Buneman:Peter.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/f/Fernandez:Mary_F=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Suciu:Dan.html;
http://link.springer.de/link/service/journals/00778/bibs/0009001/00090076.htm;
http://link.springer.de/link/service/journals/00778/papers/0009001/00090076.pdf",
abstract = "This paper presents structural recursion as the basis
of the syntax and semantics of query languages for
semistructured data and XML. We describe a simple and
powerful query language based on pattern matching and
show that it can be expressed using structural
recursion, which is introduced as a top-down, recursive
function, similar to the way XSL is defined on XML
trees. On cyclic data, structural recursion can be
defined in two equivalent ways: as a recursive function
which evaluates the data top-down and remembers all its
calls to avoid infinite loops, or as a bulk evaluation
which processes the entire data in parallel using only
traditional relational algebra operators. The latter
makes it possible for optimization techniques in
relational queries to be applied to structural
recursion. We show that the composition of two
structural recursion queries can be expressed as a
single such query, and this is used as the basis of an
optimization method for mediator systems. Several other
formal properties are established: structural recursion
can be expressed in first-order logic extended with
transitive closure; its data complexity is PTIME; and
over relational data it is a conservative extension of
the relational calculus. The underlying data model is
based on value equality, formally defined with
bisimulation. Structural recursion is shown to be
invariant with respect to value equality.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "optimization; query language; semistructured data;
structural recursion; XML; XSL",
}
@Article{Mirbel:2000:CTI,
author = "Isabelle Mirbel and Barbara Pernici and Timos K.
Sellis and S. Tserkezoglou and Michalis Vazirgiannis",
title = "Checking the Temporal Integrity of Interactive
Multimedia Documents",
journal = j-VLDB-J,
volume = "9",
number = "2",
pages = "111--130",
month = jul,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:53 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb9.html;
http://link.springer.de/link/service/journals/00778/tocs/t0009002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Mirbel:Isabelle.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/p/Pernici:Barbara.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Sellis:Timos_K=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/t/Tserkezoglou:S=.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/v/Vazirgiannis:Michalis.html;
http://link.springer.de/link/service/journals/00778/bibs/0009002/00090111.htm;
http://link.springer.de/link/service/journals/00778/papers/0009002/00090111.pdf",
abstract = "When authoring multimedia scenarios, and in particular
scenarios with user interaction, where the sequence and
time of occurrence of interactions is not predefined,
it is difficult to guarantee the consistency of the
resulting scenarios. As a consequence, the {\em
execution\/} of the scenario may result in unexpected
behavior or inconsistent use of media. The present
paper proposes a methodology for checking the temporal
integrity of interactive multimedia document (IMD)
scenarios at authoring time at various levels. The IMD
flow is mainly defined by the events occurring during
the IMD session. Integrity checking consists of a set
of discrete steps, during which we transform the
scenario into temporal constraint networks representing
the constraints linking the different possible events
in the scenario. Temporal constraint verification
techniques are applied to verify the integrity of the
scenario, deriving a minimal network, showing possible
temporal relationships between events given a set of
constraints.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "constraint networks; multimedia presentation; temporal
integrity",
}
@Article{Candan:2000:VMM,
author = "K. Sel{\c{c}}uk Candan and Eric Lemar and V. S.
Subrahmanian",
title = "View management in multimedia databases",
journal = j-VLDB-J,
volume = "9",
number = "2",
pages = "131--153",
month = jul,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:53 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb9.html;
http://link.springer.de/link/service/journals/00778/tocs/t0009002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Candan:K=_Sel=ccedil=uk.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/l/Lemar:Eric.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/s/Subrahmanian:V=_S=.html;
http://link.springer.de/link/service/journals/00778/bibs/0009002/00090131.htm;
http://link.springer.de/link/service/journals/00778/papers/0009002/00090131.pdf",
abstract = "Though there has been extensive work on multimedia
databases in the last few years, there is no prevailing
notion of a multimedia view, nor there are techniques
to create, manage, and maintain such views. Visualizing
the results of a dynamic multimedia query or
materializing a dynamic multimedia view corresponds to
assembling and delivering an interactive multimedia
presentation in accordance with the visualization
specifications. In this paper, we suggest that a
non-interactive multimedia presentation is a set of
{\em virtual objects\/} with associated spatial and
temporal presentation constraints. A virtual object is
either an object, or the result of a query. As queries
may have different answers at different points in time,
scheduling the presentation of such objects is
nontrivial. We then develop a probabilistic model of
interactive multimedia presentations, extending the
non-interactive model described earlier. We also
develop a probabilistic model of interactive
visualization where the probabilities reflect the user
profiles, or the likelihood of certain user
interactions. Based on this probabilistic model, we
develop three utility-theoretic based types of
prefetching algorithms that anticipate how users will
interact with the presentation. These prefetching
algorithms allow efficient visualization of the query
results in accordance with the underlying
specification. We have built a prototype system that
incorporates these algorithms. We report on the results
of experiments conducted on top of this
implementation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "interactivity; multimedia databases; prefetching;
result visualization\slash presentation; view
management",
}
@Article{Fu:2000:DVT,
author = "Ada Wai-chee Fu and Polly Mei-shuen Chan and Yin-Ling
Cheung and Yiu Sang Moon",
title = "Dynamic vp-Tree Indexing for $n$-Nearest Neighbor
Search Given Pair-Wise Distances",
journal = j-VLDB-J,
volume = "9",
number = "2",
pages = "154--173",
month = jul,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:53 MDT 2008",
bibsource = "http://ftp.informatik.rwth-aachen.de/dblp/db/journals/vldb/vldb9.html;
http://link.springer.de/link/service/journals/00778/tocs/t0009002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Electronic edition.",
URL = "http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Chan:Polly_Mei=shuen.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/c/Cheung:Yin=Ling.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/f/Fu:Ada_Wai=Chee.html;
http://ftp.informatik.rwth-aachen.de/dblp/db/indices/a-tree/m/Moon:Yiu_Sang.html;
http://link.springer.de/link/service/journals/00778/bibs/0009002/00090154.htm;
http://link.springer.de/link/service/journals/00778/papers/0009002/00090154.pdf",
abstract = "For some multimedia applications, it has been found
that domain objects cannot be represented as feature
vectors in a multidimensional space. Instead, pair-wise
distances between data objects are the only input. To
support content-based retrieval, one approach maps each
object to a $k$ dimensional ($k$ d) point and tries to
preserve the distances among the points. Then, existing
spatial access index methods such as the R-trees and
KD-trees can support fast searching on the resulting
$k$ d points. However, information loss is inevitable
with such an approach since the distances between data
objects can only be preserved to a certain extent. Here
we investigate the use of a distance-based indexing
method. In particular, we apply the vantage point tree
(vp-tree) method. There are two important problems for
the vp-tree method that warrant further investigation,
the $n$ nearest neighbors search and the updating
mechanisms. We study an $n$ nearest neighbors search
algorithm for the vp-tree, which is shown by
experiments to scale up well with the size of the
dataset and the desired number of nearest neighbors,
$n$. Experiments also show that the searching in the
vp-tree is more efficient than that for the $ R^*$-tree
and the $M$-tree. Next, we propose solutions for the
update problem for the vp-tree, and show by experiments
that the algorithms are efficient and effective.
Finally, we investigate the problem of selecting
vantage-point, propose a few alternative methods, and
study their impact on the number of distance
computation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "content-based retrieval; indexing; nearest neighbor
search; pair-wise distances; updating",
}
@Article{Atkinson:2000:GE,
author = "Malcolm P. Atkinson",
title = "Guest editorial",
journal = j-VLDB-J,
volume = "9",
number = "3",
pages = "175--176",
month = dec,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:54 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t0009003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/0009003/00090175.htm;
http://link.springer.de/link/service/journals/00778/papers/0009003/00090175.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bernstein:2000:CBP,
author = "Philip A. Bernstein and Shankar Pal and David Shutt",
title = "Context-based prefetch --- an optimization for
implementing objects on relations",
journal = j-VLDB-J,
volume = "9",
number = "3",
pages = "177--189",
month = dec,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:54 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t0009003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/0009003/00090177.htm;
http://link.springer.de/link/service/journals/00778/papers/0009003/00090177.pdf",
abstract = "When implementing persistent objects on a relational
database, a major performance issue is prefetching data
to minimize the number of round-trips to the database.
This is especially hard with navigational applications,
since future accesses are unpredictable. We propose the
use of the context in which an object is loaded as a
predictor of future accesses, where a context can be a
stored collection of relationships, a query result, or
a complex object. When an object O's state is loaded,
similar state for other objects in O's context is
prefetched. We present a design for maintaining context
and for using it to guide prefetch. We give performance
measurements of its implementation in Microsoft
Repository, showing up to a 70\% reduction in running
time. We describe several variations of the
optimization: selectively applying the technique based
on application and database characteristics, using
application-supplied performance hints, using
concurrent database queries to support asynchronous
prefetch, prefetching across relationship paths, and
delayed prefetch to save database round-trips.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "caching; object-oriented database; object-relational
mapping; prefetch",
}
@Article{Claussen:2000:EES,
author = "J. Claussen and A. Kemper and D. Kossmann and C.
Wiesner",
title = "Exploiting early sorting and early partitioning for
decision support query processing",
journal = j-VLDB-J,
volume = "9",
number = "3",
pages = "190--213",
month = dec,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:54 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t0009003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/0009003/00090190.htm;
http://link.springer.de/link/service/journals/00778/papers/0009003/00090190.pdf",
abstract = "Decision support queries typically involve several
joins, a grouping with aggregation, and/or sorting of
the result tuples. We propose two new classes of query
evaluation algorithms that can be used to speed up the
execution of such queries. The algorithms are based on
(1) {\em early sorting\/} and (2) {\em early
partitioning\/} --- or a combination of both. The idea
is to push the sorting and/or the partitioning to the
leaves, i.e., the base relations, of the query
evaluation plans (QEPs) and thereby avoid sorting or
partitioning large intermediate results generated by
the joins. Both early sorting and early partitioning
are used in combination with hash-based algorithms for
evaluating the join(s) and the grouping. To enable
early sorting, the sort order generated at an early
stage of the QEP is retained through an arbitrary
number of so-called {\em order-preserving hash joins}.
To make early partitioning applicable to a large class
of decision support queries, we generalize the
so-called hash teams proposed by Graefe et al. [GBC98].
Hash teams allow to perform several hash-based
operations (join and grouping) on the same attribute in
one pass without repartitioning intermediate results.
Our generalization consists of indirectly partitioning
the input data. Indirect partitioning means
partitioning the input data on an attribute that is not
directly needed for the next hash-based operation, and
it involves the construction of bitmaps to approximate
the partitioning for the attribute that is needed in
the next hash-based operation. Our performance
experiments show that such QEPs based on {\em early
sorting, early partitioning}, or both in combination
perform significantly better than conventional
strategies for many common classes of decision support
queries.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "decision support systems; early sorting and
partitioning; hash joins and hash teams; performance
evaluation; query processing and optimization",
}
@Article{Jagadish:2000:ODM,
author = "H. V. Jagadish and Olga Kapitskaia and Raymond T. Ng
and Divesh Srivastava",
title = "One-dimensional and multi-dimensional substring
selectivity estimation",
journal = j-VLDB-J,
volume = "9",
number = "3",
pages = "214--230",
month = dec,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:54 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t0009003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/0009003/00090214.htm;
http://link.springer.de/link/service/journals/00778/papers/0009003/00090214.pdf",
abstract = "With the increasing importance of XML, LDAP
directories, and text-based information sources on the
Internet, there is an ever-greater need to evaluate
queries involving (sub)string matching. In many cases,
matches need to be on multiple attributes/dimensions,
with correlations between the multiple dimensions.
Effective query optimization in this context requires
good selectivity estimates. In this paper, we use
pruned count-suffix trees (PSTs) as the basic data
structure for substring selectivity estimation. For the
1-D problem, we present a novel technique called MO
(Maximal Overlap). We then develop and analyze two 1-D
estimation algorithms, MOC and MOLC, based on MO and a
constraint-based characterization of all possible
completions of a given PST. For the $k$-D problem, we
first generalize PSTs to multiple dimensions and
develop a space- and time-efficient probabilistic
algorithm to construct $k$-D PSTs directly. We then
show how to extend MO to multiple dimensions. Finally,
we demonstrate, both analytically and experimentally,
that MO is both practical and substantially superior to
competing algorithms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "maximal overlap; pruned count-suffix tree; short
memory property; string selectivity",
}
@Article{Manegold:2000:ODA,
author = "Stefan Manegold and Peter A. Boncz and Martin L.
Kersten",
title = "Optimizing database architecture for the new
bottleneck: memory access",
journal = j-VLDB-J,
volume = "9",
number = "3",
pages = "231--246",
month = dec,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:54 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t0009003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/0009003/00090231.htm;
http://link.springer.de/link/service/journals/00778/papers/0009003/00090231.pdf",
abstract = "In the past decade, advances in the speed of commodity
CPUs have far out-paced advances in memory latency.
Main-memory access is therefore increasingly a
performance bottleneck for many computer applications,
including database systems. In this article, we use a
simple scan test to show the severe impact of this
bottleneck. The insights gained are translated into
guidelines for database architecture, in terms of both
data structures and algorithms. We discuss how
vertically fragmented data structures optimize cache
performance on sequential data access. We then focus on
equi-join, typically a random-access operation, and
introduce radix algorithms for partitioned hash-join.
The performance of these algorithms is quantified using
a detailed analytical model that incorporates memory
access cost. Experiments that validate this model were
performed on the Monet database system. We obtained
exact statistics on events such as TLB misses and L1
and L2 cache misses by using hardware performance
counters found in modern CPUs. Using our cost model, we
show how the carefully tuned memory access pattern of
our radix algorithms makes them perform well, which is
confirmed by experimental results.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "decomposed storage model; implementation techniques;
join algorithms; main-memory databases; memory access
optimization; query processing",
}
@Article{Raman:2000:ODR,
author = "Vijayshankar Raman and Bhaskaran Raman and Joseph M.
Hellerstein",
title = "Online dynamic reordering",
journal = j-VLDB-J,
volume = "9",
number = "3",
pages = "247--260",
month = dec,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:54 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t0009003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/0009003/00090247.htm;
http://link.springer.de/link/service/journals/00778/papers/0009003/00090247.pdf",
abstract = "We present a pipelining, dynamically tunable {\em
reorder\/} operator for providing user control during
long running, data-intensive operations. Users can see
partial results and accordingly direct the processing
by specifying preferences for various data items; data
of interest is prioritized for early processing. The
reordering mechanism is efficient and non-blocking and
can be used over arbitrary data streams from files and
indexes, as well as continuous data feeds. We also
investigate several policies for the reordering based
on the performance goals of various typical
applications. We present performance results for
reordering in the context of an online aggregation
implementation in Informix and in the context of
sorting and scrolling in a large-scale spreadsheet. Our
experiments demonstrate that for a variety of data
distributions and applications, reordering is
responsive to dynamic preference changes, imposes
minimal overheads in overall completion time, and
provides dramatic improvements in the quality of the
feedback over time. Surprisingly, preliminary
experiments indicate that online reordering can also be
useful in traditional batch query processing, because
it can serve as a form of pipelined, approximate
sorting.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Informix; interactive data processing; online
reordering; user control",
}
@Article{Tan:2000:PEN,
author = "Kian-Lee Tan and Cheng Hian Goh and Beng Chin Ooi",
title = "Progressive evaluation of nested aggregate queries",
journal = j-VLDB-J,
volume = "9",
number = "3",
pages = "261--278",
month = dec,
year = "2000",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:54 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t0009003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/0009003/00090261.htm;
http://link.springer.de/link/service/journals/00778/papers/0009003/00090261.pdf",
abstract = "In many decision-making scenarios, decision makers
require rapid feedback to their queries, which
typically involve aggregates. The traditional {\em
blocking execution model\/} can no longer meet the
demands of these users. One promising approach in the
literature, called {\em online aggregation}, evaluates
an aggregation query progressively as follows: as soon
as certain data have been evaluated, approximate
answers are produced with their respective running
confidence intervals; as more data are examined, the
answers and their corresponding running confidence
intervals are refined. In this paper, we extend this
approach to handle nested queries with aggregates
(i.e., at least one inner query block is an aggregate
query) by providing users with (approximate) answers
progressively as the inner aggregation query blocks are
evaluated. We address the new issues pose by nested
queries. In particular, the answer space begins with a
superset of the final answers and is refined as the
aggregates from the inner query blocks are refined. For
the intermediary answers to be meaningful, they have to
be interpreted with the aggregates from the inner
queries. We also propose a {\em multi-threaded model\/}
in evaluating such queries: each query block is
assigned to a thread, and the threads can be evaluated
concurrently and independently. The time slice across
the threads is {\em nondeterministic\/} in the sense
that the user controls the relative rate at which these
subqueries are being evaluated. For {\em enumerative\/}
nested queries, we propose a priority-based evaluation
strategy to present answers that are certainly in the
final answer space first, before presenting those whose
validity may be affected as the inner query aggregates
are refined. We implemented a prototype system using
Java and evaluated our system. Results for nested
queries with a level and multiple levels of nesting are
reported. Our results show the effectiveness of the
proposed mechanisms in providing progressive feedback
that reduces the initial waiting time of users
significantly without sacrificing the quality of the
answers.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "approximate answers; multi-threading; nested aggregate
queries; online aggregation; progressive query
processing",
}
@Article{Ngu:2001:CMV,
author = "Anne H. H. Ngu and Quan Z. Sheng and Du Q. Huynh and
Ron Lei",
title = "Combining multi-visual features for efficient indexing
in a large image database",
journal = j-VLDB-J,
volume = "9",
number = "4",
pages = "279--293",
month = apr,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100028",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:55 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1009004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1009004/10090279.htm;
http://link.springer.de/link/service/journals/00778/papers/1009004/10090279.pdf",
abstract = "The optimized distance-based access methods currently
available for multidimensional indexing in multimedia
databases have been developed based on two major
assumptions: a suitable distance function is known a
priori and the dimensionality of the image features is
low. It is not trivial to define a distance function
that best mimics human visual perception regarding
image similarity measurements. Reducing
high-dimensional features in images using the popular
principle component analysis (PCA) might not always be
possible due to the non-linear correlations that may be
present in the feature vectors. We propose in this
paper a fast and robust hybrid method for non-linear
dimensions reduction of composite image features for
indexing in large image database. This method
incorporates both the PCA and non-linear neural network
techniques to reduce the dimensions of feature vectors
so that an optimized access method can be applied. To
incorporate human visual perception into our system, we
also conducted experiments that involved a number of
subjects classifying images into different classes for
neural network training. We demonstrate that not only
can our neural network system reduce the dimensions of
the feature vectors, but that the reduced dimensional
feature vectors can also be mapped to an optimized
access method for fast and accurate indexing.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "high-dimensional indexing; image retrieval; neural
network",
}
@Article{Combi:2001:HTD,
author = "Carlo Combi and Giuseppe Pozzi",
title = "{{\em HMAP\/}} --- a temporal data model managing
intervals with different granularities and
indeterminacy from natural language sentences",
journal = j-VLDB-J,
volume = "9",
number = "4",
pages = "294--311",
month = apr,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100033",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:55 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1009004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1009004/10090294.htm;
http://link.springer.de/link/service/journals/00778/papers/1009004/10090294.pdf",
abstract = "The {\em granularity\/} of given temporal information
is the level of abstraction at which information is
expressed. Different units of measure allow one to
represent different granularities. Indeterminacy is
often present in temporal information given at
different granularities: temporal {\em indeterminacy\/}
is related to incomplete knowledge of when the
considered fact happened. Focusing on temporal
databases, different granularities and indeterminacy
have to be considered in expressing valid time, i.e.,
the time at which the information is true in the
modeled reality. In this paper, we propose {\em HMAP\/}
(The term is the transliteration of an ancient Greek
poetical word meaning ``day''.), a temporal data model
extending the capability of defining valid times with
different granularity and/or with indeterminacy. In
{\em HMAP}, absolute intervals are explicitly
represented by their {\em start}, {\em end}, and {\em
duration\/}: in this way, we can represent valid times
as ``in December 1998 for five hours'', ``from July
1995, for 15 days'', ``from March 1997 to October 15,
1997, between 6 and 6:30 p.m.''. {\em HMAP\/} is based
on a three-valued logic, for managing uncertainty in
temporal relationships. Formulas involving different
temporal relationships between intervals, instants, and
durations can be defined, allowing one to query the
database with different granularities, not necessarily
related to that of data. In this paper, we also discuss
the complexity of algorithms, allowing us to evaluate
{\em HMAP\/} formulas, and show that the formulas can
be expressed as constraint networks falling into the
class of simple temporal problems, which can be solved
in polynomial time.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "temporal databases; three-valued logic; time
granularity; time indeterminacy",
}
@Article{Li:2001:SEM,
author = "Wen-Syan Li and K. Sel{\c{c}}uk Candan and Kyoji
Hirata and Yoshinori Hara",
title = "Supporting efficient multimedia database exploration",
journal = j-VLDB-J,
volume = "9",
number = "4",
pages = "312--326",
month = apr,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100040",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:55 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1009004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1009004/10090312.htm;
http://link.springer.de/link/service/journals/00778/papers/1009004/10090312.pdf",
abstract = "Due to the fuzziness of query specification and media
matching, multimedia retrieval is conducted by way of
exploration. It is essential to provide feedback so
that users can visualize query reformulation
alternatives and database content distribution. Since
media matching is an expensive task, another issue is
how to efficiently support exploration so that the
system is not overloaded by perpetual query
reformulation. In this paper, we present a uniform
framework to represent statistical information of both
semantics and visual metadata for images in the
databases. We propose the concept of {\em query
verification}, which evaluates queries using
statistics, and provides users with feedback, including
the strictness and reformulation alternatives of each
query condition as well as estimated numbers of
matches. With query verification, the system increases
the efficiency of the multimedia database exploration
for both users and the system. Such statistical
information is also utilized to support progressive
query processing and query relaxation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "exploration; human computer interaction; multimedia
database; progressive processing; query relaxation;
selectivity statistics",
}
@Article{Lee:2001:GTM,
author = "Chiang Lee and Chi-Sheng Shih and Yaw-Huei Chen",
title = "A graph-theoretic model for optimizing queries
involving methods",
journal = j-VLDB-J,
volume = "9",
number = "4",
pages = "327--343",
month = apr,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100035",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:55 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1009004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1009004/10090327.htm;
http://link.springer.de/link/service/journals/00778/papers/1009004/10090327.pdf",
abstract = "Traditional algorithms for optimizing the execution
order of joins are no more valid when selections and
projections involve methods and become very expensive
operations. Selections and projections could be even
more costly than joins such that they are pulled above
joins, rather than pushed down in a query tree. In this
paper, we take a fundamental look at how to approach
query optimization from a top-down design perspective,
rather than trying to force one model to fit into
another. We present a graph model which is designed to
characterize execution plans. Each edge and each vertex
of the graph is assigned a weight to model execution
plans. We also design algorithms that use these weights
to optimize the execution order of operations. A cost
model of these algorithms is developed. Experiments are
conducted on the basis of this cost model. The results
show that our algorithms are superior to similar work
proposed in the literature.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "graph model; method query; object-oriented databases;
query optimization; spanning tree",
}
@Article{Wang:2001:IVH,
author = "Changzhou Wang and X. Sean Wang",
title = "Indexing very high-dimensional sparse and quasi-sparse
vectors for similarity searches",
journal = j-VLDB-J,
volume = "9",
number = "4",
pages = "344--361",
month = apr,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100036",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:55 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1009004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1009004/10090344.htm;
http://link.springer.de/link/service/journals/00778/papers/1009004/10090344.pdf",
abstract = "Similarity queries on complex objects are usually
translated into searches among their feature vectors.
This paper studies indexing techniques for very
high-dimensional (e.g., in hundreds) vectors that are
sparse or quasi-sparse, i.e., vectors {\em each\/}
having only a small number (e.g., ten) of non-zero or
significant values. Based on the R-tree, the paper
introduces the xS-tree that uses lossy compression of
bounding regions to guarantee a reasonable minimum
fan-out within the allocated storage space for each
node. In addition, the paper studies the performance
and scalability of the xS-tree via experiments.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "high-dimensional indexing structure; lossy
compression; quasi-sparse vector; similarity search;
sparse vector",
}
@Article{Casati:2001:GE,
author = "Fabio Casati and Ming-Chien Shan and Dimitrios
Georgakopoulos",
title = "Guest editorial",
journal = j-VLDB-J,
volume = "10",
number = "1",
pages = "1--1",
month = aug,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100041",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:56 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010001/10100001.htm;
http://link.springer.de/link/service/journals/00778/papers/1010001/10100001.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mecella:2001:DWC,
author = "Massimo Mecella and Barbara Pernici",
title = "Designing wrapper components for e-services in
integrating heterogeneous systems",
journal = j-VLDB-J,
volume = "10",
number = "1",
pages = "2--15",
month = aug,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100044",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:56 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010001/10100002.htm;
http://link.springer.de/link/service/journals/00778/papers/1010001/10100002.pdf",
abstract = "Component-based approaches are becoming more and more
popular to support Internet-based application
development. Different component modeling approaches,
however, can be adopted, obtaining different
abstraction levels (either conceptual or operational).
In this paper we present a component-based architecture
for the design of e-applications, and discuss the
concept of wrapper components as building blocks for
the development of e-services, where these services are
based on legacy systems. We discuss their
characteristics and their applicability in
Internet-based application development.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "component; cooperation; e-application; e-service;
integration; legacy system; wrapper",
}
@Article{Eyal:2001:ICH,
author = "Anat Eyal and Tova Milo",
title = "Integrating and customizing heterogeneous e-commerce
applications",
journal = j-VLDB-J,
volume = "10",
number = "1",
pages = "16--38",
month = aug,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100045",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:56 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010001/10100016.htm;
http://link.springer.de/link/service/journals/00778/papers/1010001/10100016.pdf",
abstract = "A broad spectrum of electronic commerce applications
is currently available on the Web, providing services
in almost any area one can think of. As the number and
variety of such applications grow, more business
opportunities emerge for providing new services based
on the integration and customization of existing
applications. (Web shopping malls and support for
comparative shopping are just a couple of examples.)
Unfortunately, the diversity of applications in each
specific domain and the disparity of interfaces,
application flows, actor roles in the business
transaction, and data formats, renders the integration
and manipulation of applications a rather difficult
task. In this paper we present the {\em Application
Manifold\/} system, aimed at simplifying the intricate
task of integration and customization of e-commerce
applications. The scope of the work in this paper is
limited to web-enabled e-commerce applications. We do
not support the integration/customization of
proprietary/legacy applications. The wrapping of such
applications as web services is complementary to our
work. Based on the emerging Web data standard, XML, and
application modeling standard, UML, the system offers a
novel declarative specification language for describing
the integration/customization task, supporting a
modular approach where new applications can be added
and integrated at will with minimal effort. Then,
acting as an application generator, the system
generates a full integrated/customized e-commerce
application, with the declarativity of the
specification allowing for the optimization and
verification of the generated application. The
integration here deals with the full profile of the
given e-commerce applications: the various services
offered by the applications, the activities and roles
of the different actors participating in the
application (e.g., customers, vendors), the application
flow, as well as with the data involved in the process.
This is in contrast to previous works on Web data
integration that focused primarily on querying the data
available in the applications, mostly ignoring the
additional aspects mentioned above.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "application integration; data integration; electronic
commerce",
}
@Article{Bonifati:2001:ARX,
author = "Angela Bonifati and Stefano Ceri and Stefano
Paraboschi",
title = "Active rules for {XML}: a new paradigm for
{E}-services",
journal = j-VLDB-J,
volume = "10",
number = "1",
pages = "39--47",
month = aug,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100039",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:56 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010001/10100039.htm;
http://link.springer.de/link/service/journals/00778/papers/1010001/10100039.pdf",
abstract = "XML is rapidly becoming one of the most widely adopted
technologies for information exchange and
representation. As the use of XML becomes more
widespread, we foresee the development of active XML
rules, i.e., rules explicitly designed for the
management of XML information. In particular, we argue
that active rules for XML offer a natural paradigm for
the rapid development of innovative e-services. In the
paper, we show how active rules can be specified in the
context of XSLT, a pattern-based language for
publishing XML documents (promoted by the W3C) which is
receiving strong commercial support, and Lorel, a query
language for XML documents that is quite popular in the
research world. We demonstrate, through simple examples
of active rules for XSLT and Lorel, that active rules
can be effective for the implementation of e-commerce
services. We also discuss the various issues that need
to be considered in adapting the notion of relational
triggers to the XML context.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "active databases; document management; query languages
for XML; XML; XSLT",
}
@Article{Braumandl:2001:OUQ,
author = "R. Braumandl and M. Keidl and A. Kemper and D.
Kossmann and A. Kreutz and S. Seltzsam and K. Stocker",
title = "{ObjectGlobe}: {Ubiquitous} query processing on the
{Internet}",
journal = j-VLDB-J,
volume = "10",
number = "1",
pages = "48--71",
month = aug,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100043",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:56 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010001/10100048.htm;
http://link.springer.de/link/service/journals/00778/papers/1010001/10100048.pdf",
abstract = "We present the design of ObjectGlobe, a distributed
and open query processor for Internet data sources.
Today, data is published on the Internet via Web
servers which have, if at all, very localized query
processing capabilities. The goal of the ObjectGlobe
project is to establish an open marketplace in which
{\em data\/} and {\em query processing capabilities\/}
can be distributed and used by any kind of Internet
application. Furthermore, ObjectGlobe integrates {\em
cycle providers\/} (i.e., machines) which carry out
query processing operators. The overall picture is to
make it possible to execute a query with --- in
principle --- unrelated query operators, cycle
providers, and data sources. Such an infrastructure can
serve as enabling technology for scalable e-commerce
applications, e.g., B2B and B2C market places, to be
able to integrate data and data processing operations
of a large number of participants. One of the main
challenges in the design of such an open system is to
ensure privacy and security. We discuss the ObjectGlobe
security requirements, show how basic components such
as the optimizer and runtime system need to be
extended, and present the results of performance
experiments that assess the additional cost for secure
distributed query processing. Another challenge is
quality of service management so that users can
constrain the costs and running times of their
queries.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "cycle-; distributed query processing; function- and
data provider; open systems; privacy; quality of
service; query optimization; security",
}
@Article{Su:2001:IBN,
author = "Stanley Y. W. Su and Chunbo Huang and Joachim Hammer
and Yihua Huang and Haifei Li and Liu Wang and Youzhong
Liu and Charnyote Pluempitiwiriyawej and Minsoo Lee and
Herman Lam",
title = "An {Internet}-based negotiation server for
e-commerce",
journal = j-VLDB-J,
volume = "10",
number = "1",
pages = "72--90",
month = aug,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100051",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:56 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010001/10100072.htm;
http://link.springer.de/link/service/journals/00778/papers/1010001/10100072.pdf",
abstract = "This paper describes the design and implementation of
a replicable, Internet-based negotiation server for
conducting bargaining-type negotiations between
enterprises involved in e-commerce and e-business.
Enterprises can be buyers and sellers of
products/services or participants of a complex supply
chain engaged in purchasing, planning, and scheduling.
Multiple copies of our server can be installed to
complement the services of Web servers. Each enterprise
can install or select a trusted negotiation server to
represent his/her interests. Web-based GUI tools are
used during the build-time registration process to
specify the requirements, constraints, and rules that
represent negotiation policies and strategies,
preference scoring of different data conditions, and
aggregation methods for deriving a global cost-benefit
score for the item(s) under negotiation. The
registration information is used by the negotiation
servers to automatically conduct bargaining type
negotiations on behalf of their clients. In this paper,
we present the architecture of our implementation as
well as a framework for automated negotiations, and
describe a number of communication primitives which are
used in the underlying negotiation protocol. A
constraint satisfaction processor (CSP) is used to
evaluate a negotiation proposal or counterproposal
against the registered requirements and constraints of
a client company. In case of a constraint violation, an
event is posted to trigger the execution of negotiation
strategic rules, which either automatically relax the
violated constraint, ask for human intervention, invoke
an application, or perform other remedial operations.
An Event-Trigger-Rule (ETR) server is used to manage
events, triggers, and rules. Negotiation strategic
rules can be added or modified at run-time. A
cost-benefit analysis component is used to perform
quantitative analysis of alternatives. The use of
negotiation servers to conduct automated negotiation
has been demonstrated in the context of an integrated
supply chain scenario.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "constraint evaluation; cost-benefit analysis;
database; e-commerce; negotiation policy and strategy;
negotiation protocol",
}
@Article{Shegalov:2001:XEW,
author = "German Shegalov and Michael Gillmann and Gerhard
Weikum",
title = "{XML}-enabled workflow management for e-services
across heterogeneous platforms",
journal = j-VLDB-J,
volume = "10",
number = "1",
pages = "91--103",
month = aug,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100038",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:56 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010001/10100091.htm;
http://link.springer.de/link/service/journals/00778/papers/1010001/10100091.pdf",
abstract = "Advanced e-services require efficient, flexible, and
easy-to-use workflow technology that integrates well
with mainstream Internet technologies such as XML and
Web servers. This paper discusses an XML-enabled
architecture for distributed workflow management that
is implemented in the latest version of our Mentor-lite
prototype system. The key asset of this architecture is
an XML mediator that handles the exchange of business
and flow control data between workflow and
business-object servers on the one hand and client
activities on the other via XML messages over http. Our
implementation of the mediator has made use of Oracle's
XSQL servlet. The major benefit of the advocated
architecture is that it provides seamless integration
of client applications into e-service workflows with
scalable efficiency and very little explicit coding, in
contrast to an earlier, Java-based, version of our
Mentor-lite prototype that required much more code and
exhibited potential performance problems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "business processes; information system
interoperability; Internet e-services; workflow
management; XML/XSL",
}
@Article{Datta:2001:ASS,
author = "Anindya Datta and Kaushik Dutta and Debra VanderMeer
and Krithi Ramamritham and Shamkant B. Navathe",
title = "An architecture to support scalable online
personalization on the {Web}",
journal = j-VLDB-J,
volume = "10",
number = "1",
pages = "104--117",
month = aug,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100037",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:56 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010001/10100104.htm;
http://link.springer.de/link/service/journals/00778/papers/1010001/10100104.pdf",
abstract = "Online personalization is of great interest to
e-companies. Virtually all personalization technologies
are based on the idea of storing as much historical
customer session data as possible, and then querying
the data store as customers navigate through a web
site. The holy grail of online personalization is an
environment where fine-grained, detailed historical
session data can be queried based on current online
navigation patterns for use in formulating real-time
responses. Unfortunately, as more consumers become
e-shoppers, the user load and the amount of historical
data continue to increase, causing scalability-related
problems for almost all current personalization
technologies. This paper chronicles the development of
a real-time interaction management system through the
integration of historical data and online visitation
patterns of e-commerce site visitors. It describes the
scientific underpinnings of the system as well as its
architecture. Experimental evaluation of the system
shows that the caching and storage techniques built
into the system deliver performance that is orders of
magnitude better than those derived from off-the-shelf
database components.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "behavior-based personalization; dynamic lookahead
profile; profile caching; scalable online
personalization; Web site and interaction model",
}
@Article{ElAbbadi:2001:GE,
author = "Amr {El Abbadi} and Gunter Schlageter and Kyu-Young
Whang",
title = "Guest editorial",
journal = j-VLDB-J,
volume = "10",
number = "2--3",
pages = "119--119",
month = sep,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100053",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:58 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010002/10100119.htm;
http://link.springer.de/link/service/journals/00778/papers/1010002/10100119.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Pucheral:2001:PSD,
author = "Philippe Pucheral and Luc Bouganim and Patrick
Valduriez and Christophe Bobineau",
title = "{PicoDBMS}: {Scaling} down database techniques for the
smartcard",
journal = j-VLDB-J,
volume = "10",
number = "2--3",
pages = "120--132",
month = sep,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100047",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:58 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010002/10100120.htm;
http://link.springer.de/link/service/journals/00778/papers/1010002/10100120.pdf",
abstract = "Smartcards are the most secure portable computing
device today. They have been used successfully in
applications involving money, and proprietary and
personal data (such as banking, healthcare, insurance,
etc.). As smartcards get more powerful (with 32-bit CPU
and more than 1 MB of stable memory in the next
versions) and become multi-application, the need for
database management arises. However, smartcards have
severe hardware limitations (very slow write, very
little RAM, constrained stable memory, no autonomy,
etc.) which make traditional database technology
irrelevant. The major problem is scaling down database
techniques so they perform well under these
limitations. In this paper, we give an in-depth
analysis of this problem and propose a PicoDBMS
solution based on highly compact data structures, query
execution without RAM, and specific techniques for
atomicity and durability. We show the effectiveness of
our techniques through performance evaluation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "atomicity; durability; execution model; PicoDBMS;
query optimization; smartcard applications; storage
model",
}
@Article{Shanmugasundaram:2001:EPR,
author = "Jayavel Shanmugasundaram and Eugene Shekita and Rimon
Barr and Michael Carey and Bruce Lindsay and Hamid
Pirahesh and Berthold Reinwald",
title = "Efficiently publishing relational data as {XML}
documents",
journal = j-VLDB-J,
volume = "10",
number = "2--3",
pages = "133--154",
month = sep,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100052",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:58 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010002/10100133.htm;
http://link.springer.de/link/service/journals/00778/papers/1010002/10100133.pdf",
abstract = "XML is rapidly emerging as a standard for exchanging
business data on the World Wide Web. For the
foreseeable future, however, most business data will
continue to be stored in relational database systems.
Consequently, if XML is to fulfill its potential, some
mechanism is needed to publish relational data as XML
documents. Towards that goal, one of the major
challenges is finding a way to efficiently structure
and tag data from one or more tables as a hierarchical
XML document. Different alternatives are possible
depending on when this processing takes place and how
much of it is done inside the relational engine. In
this paper, we characterize and study the performance
of these alternatives. Among other things, we explore
the use of new scalar and aggregate functions in SQL
for constructing complex XML documents directly in the
relational engine. We also explore different execution
plans for generating the content of an XML document.
The results of an experimental study show that
constructing XML documents inside the relational engine
can have a significant performance benefit. Our results
also show the superiority of having the relational
engine use what we call an ``outer union plan'' to
generate the content of an XML document.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "publishing; relational databases; XML",
}
@Article{Chang:2001:AQM,
author = "Kevin Chen-Chuan Chang and H{\'e}ctor
Garc{\'\i}a-Molina",
title = "Approximate query mapping: {Accounting} for
translation closeness",
journal = j-VLDB-J,
volume = "10",
number = "2--3",
pages = "155--181",
month = sep,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100042",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:58 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010002/10100155.htm;
http://link.springer.de/link/service/journals/00778/papers/1010002/10100155.pdf",
abstract = "In this paper we present a mechanism for approximately
translating Boolean query constraints across
heterogeneous information sources. Achieving the best
translation is challenging because sources support
different constraints for formulating queries, and
often these constraints cannot be precisely translated.
For instance, a query [score>8] might be ``perfectly''
translated as [rating>0.8] at some site, but can only
be approximated as [grade=A] at another. Unlike other
work, our general framework adopts a customizable
``closeness'' metric for the translation that combines
both precision and recall. Our results show that for
query translation we need to handle interdependencies
among both query conjuncts as well as disjuncts. As the
basis, we identify the essential requirements of a rule
system for users to encode the mappings for atomic
semantic units. Our algorithm then translates complex
queries by rewriting them in terms of the semantic
units. We show that, under practical assumptions, our
algorithm generates the best approximate translations
with respect to the closeness metric of choice. We also
present a case study to show how our technique may be
applied in practice.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "approximate query translation; closeness;
constraint-mapping; information integration;
mediators",
}
@Article{Pottinger:2001:MSA,
author = "Rachel Pottinger and Alon Halevy",
title = "{MiniCon}: a scalable algorithm for answering queries
using views",
journal = j-VLDB-J,
volume = "10",
number = "2--3",
pages = "182--198",
month = sep,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100048",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:58 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010002/10100182.htm;
http://link.springer.de/link/service/journals/00778/papers/1010002/10100182.pdf",
abstract = "The problem of answering queries using views is to
find efficient methods of answering a query using a set
of previously materialized views over the database,
rather than accessing the database relations. The
problem has received significant attention because of
its relevance to a wide variety of data management
problems, such as data integration, query optimization,
and the maintenance of physical data independence. To
date, the performance of proposed algorithms has
received very little attention, and in particular,
their scale up in the presence of a large number of
views is unknown. We first analyze two previous
algorithms, the bucket algorithm and the inverse-rules,
and show their deficiencies. We then describe the
MiniCon, a novel algorithm for finding the
maximally-contained rewriting of a conjunctive query
using a set of conjunctive views. We present the first
experimental study of algorithms for answering queries
using views. The study shows that the MiniCon scales up
well and significantly outperforms the previous
algorithms. We describe an extension of the MiniCon to
handle comparison predicates, and show its performance
experimentally. Finally, we describe how the MiniCon
can be extended to the context of query optimization.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data integration; materialized views; query
optimization; Web and databases",
}
@Article{Chakrabarti:2001:AQP,
author = "Kaushik Chakrabarti and Minos Garofalakis and Rajeev
Rastogi and Kyuseok Shim",
title = "Approximate query processing using wavelets",
journal = j-VLDB-J,
volume = "10",
number = "2--3",
pages = "199--223",
month = sep,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100049",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:58 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010002/10100199.htm;
http://link.springer.de/link/service/journals/00778/papers/1010002/10100199.pdf",
abstract = "Approximate query processing has emerged as a
cost-effective approach for dealing with the huge data
volumes and stringent response-time requirements of
today's decision support systems (DSS). Most work in
this area, however, has so far been limited in its
query processing scope, typically focusing on specific
forms of aggregate queries. Furthermore, conventional
approaches based on sampling or histograms appear to be
inherently limited when it comes to approximating the
results of complex queries over high-dimensional DSS
data sets. In this paper, we propose the use of
multi-dimensional wavelets as an effective tool for
general-purpose approximate query processing in modern,
high-dimensional applications. Our approach is based on
building {\em wavelet-coefficient synopses\/} of the
data and using these synopses to provide approximate
answers to queries. We develop novel query processing
algorithms that operate directly on the
wavelet-coefficient synopses of relational tables,
allowing us to process arbitrarily complex queries {\em
entirely\/} in the wavelet-coefficient domain. This
guarantees extremely fast response times since our
approximate query execution engine can do the bulk of
its processing over compact sets of wavelet
coefficients, essentially postponing the expansion into
relational tuples until the end-result of the query. We
also propose a novel wavelet decomposition algorithm
that can build these synopses in an I/O-efficient
manner. Finally, we conduct an extensive experimental
study with synthetic as well as real-life data sets to
determine the effectiveness of our wavelet-based
approach compared to sampling and histograms. Our
results demonstrate that our techniques: (1) provide
approximate answers of better quality than either
sampling or histograms; (2) offer query execution-time
speedups of more than two orders of magnitude; and (3)
guarantee extremely fast synopsis construction times
that scale linearly with the size of the data.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "approximate query answers; data synopses; query
processing; wavelet decomposition",
}
@Article{Sarawagi:2001:UCM,
author = "Sunita Sarawagi",
title = "User-cognizant multidimensional analysis",
journal = j-VLDB-J,
volume = "10",
number = "2--3",
pages = "224--239",
month = sep,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100046",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:58 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010002/10100224.htm;
http://link.springer.de/link/service/journals/00778/papers/1010002/10100224.pdf",
abstract = "Our goal is to enhance multidimensional database
systems with a suite of advanced operators to automate
data analysis tasks that are currently handled through
manual exploration. In this paper, we present a key
component of our system that characterizes the
information content of a cell based on a user's prior
familiarity with the cube and provides a
context-sensitive exploration of the cube. There are
three main modules of this component. A Tracker, that
continuously tracks the parts of the cube that a user
has visited. A Modeler, that pieces together the
information in the visited parts to model the user's
expected values in the unvisited parts. An Informer,
that processes user's queries about the most
informative unvisited parts of the cube. The
mathematical basis for the expected value modeling is
provided by the classical maximum entropy principle.
Accordingly, the expected values are computed so as to
agree with every value that is already visited while
reducing assumptions about unvisited values to the
minimum by maximizing their entropy. The most
informative values are defined as those that bring the
new expected values closest to the actual values. We
believe and prove through experiments that such a
user-in-the-loop exploration will enable much faster
assimilation of all significant information in the data
compared to existing manual explorations.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "maximum entropy; multidimensional data exploration;
OLAP; personalized mining; user-sensitive interest
measure",
}
@Article{Turker:2001:SIS,
author = "Can T{\"u}rker and Michael Gertz",
title = "Semantic integrity support in {SQL:1999} and
commercial (object-)relational database management
systems",
journal = j-VLDB-J,
volume = "10",
number = "4",
pages = "241--269",
month = dec,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100050",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:59 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010004/10100241.htm;
http://link.springer.de/link/service/journals/00778/papers/1010004/10100241.pdf",
abstract = "The correctness of the data managed by database
systems is vital to any application that utilizes data
for business, research, and decision-making purposes.
To guard databases against erroneous data not
reflecting real-world data or business rules, semantic
integrity constraints can be specified during database
design. Current commercial database management systems
provide various means to implement mechanisms to
enforce semantic integrity constraints at database
run-time. In this paper, we give an overview of the
semantic integrity support in the most recent
SQL-standard SQL:1999, and we show to what extent the
different concepts and language constructs proposed in
this standard can be found in major commercial
(object-)relational database management systems. In
addition, we discuss general design guidelines that
point out how the semantic integrity features provided
by these systems should be utilized in order to
implement an effective integrity enforcing subsystem
for a database.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "constraint enforcement; object-relational databases;
semantic integrity constraints; SQL:1999",
}
@Article{Halevy:2001:AQU,
author = "Alon Y. Halevy",
title = "Answering queries using views: a survey",
journal = j-VLDB-J,
volume = "10",
number = "4",
pages = "270--294",
month = dec,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100054",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:59 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010004/10100270.htm;
http://link.springer.de/link/service/journals/00778/papers/1010004/10100270.pdf",
abstract = "The problem of answering queries using views is to
find efficient methods of answering a query using a set
of previously defined materialized views over the
database, rather than accessing the database relations.
The problem has recently received significant attention
because of its relevance to a wide variety of data
management problems. In query optimization, finding a
rewriting of a query using a set of materialized views
can yield a more efficient query execution plan. To
support the separation of the logical and physical
views of data, a storage schema can be described using
views over the logical schema. As a result, finding a
query execution plan that accesses the storage amounts
to solving the problem of answering queries using
views. Finally, the problem arises in data integration
systems, where data sources can be described as
precomputed views over a mediated schema. This article
surveys the state of the art on the problem of
answering queries using views, and synthesizes the
disparate works into a coherent framework. We describe
the different applications of the problem, the
algorithms proposed to solve it and the relevant
theoretical results.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data integration; date warehousing; materialized
views; query optimization; survey; Web-site
management",
}
@Article{Laurent:2001:MCI,
author = "D. Laurent and J. Lechtenb{\"o}rger and N. Spyratos
and G. Vossen",
title = "Monotonic complements for independent data
warehouses",
journal = j-VLDB-J,
volume = "10",
number = "4",
pages = "295--315",
month = dec,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100055",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:59 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010004/10100295.htm;
http://link.springer.de/link/service/journals/00778/papers/1010004/10100295.pdf",
abstract = "Views over databases have regained attention in the
context of data warehouses, which are seen as {\em
materialized\/} views. In this setting, efficient view
maintenance is an important issue, for which the notion
of {\em self-maintainability\/} has been identified as
desirable. In this paper, we extend the concept of
self-maintainability to (query and update) {\em
independence\/} within a formal framework, where
independence with respect to arbitrary given sets of
queries and updates over the sources can be guaranteed.
To this end we establish an intuitively appealing
connection between warehouse independence and {\em view
complements}. Moreover, we study special kinds of
complements, namely {\em monotonic complements}, and
show how to compute minimal ones in the presence of
keys and foreign keys in the underlying databases.
Taking advantage of these complements, an algorithmic
approach is proposed for the specification of
independent warehouses with respect to given sets of
queries and updates.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data warehouse; independence; materialized view;
self-maintainability; view complement",
}
@Article{Grefen:2001:GTS,
author = "Paul Grefen and Jochem Vonk and Peter Apers",
title = "Global transaction support for workflow management
systems: from formal specification to practical
implementation",
journal = j-VLDB-J,
volume = "10",
number = "4",
pages = "316--333",
month = dec,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100056",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:59 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010004/10100316.htm;
http://link.springer.de/link/service/journals/00778/papers/1010004/10100316.pdf",
abstract = "In this paper, we present an approach to global
transaction management in workflow environments. The
transaction mechanism is based on the well-known notion
of compensation, but extended to deal with both
arbitrary process structures to allow cycles in
processes and safepoints to allow partial compensation
of processes. We present a formal specification of the
transaction model and transaction management algorithms
in set and graph theory, providing clear, unambiguous
transaction semantics. The specification is
straightforwardly mapped to a modular architecture, the
implementation of which is first applied in a testing
environment, then in the prototype of a commercial
workflow management system. The modular nature of the
resulting system allows easy distribution using
middleware technology. The path from abstract semantics
specification to concrete, real-world implementation of
a workflow transaction mechanism is thus covered in a
complete and coherent fashion. As such, this paper
provides a complete framework for the application of
well-founded transactional workflows.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "compensation; long-running transaction; transaction
management; workflow management",
}
@Article{Rahm:2001:SAA,
author = "Erhard Rahm and Philip A. Bernstein",
title = "A survey of approaches to automatic schema matching",
journal = j-VLDB-J,
volume = "10",
number = "4",
pages = "334--350",
month = dec,
year = "2001",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100057",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:50:59 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t1010004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/1010004/10100334.htm;
http://link.springer.de/link/service/journals/00778/papers/1010004/10100334.pdf",
abstract = "Schema matching is a basic problem in many database
application domains, such as data integration,
E-business, data warehousing, and semantic query
processing. In current implementations, schema matching
is typically performed manually, which has significant
limitations. On the other hand, previous research
papers have proposed many techniques to achieve a
partial automation of the match operation for specific
application domains. We present a taxonomy that covers
many of these existing approaches, and we describe the
approaches in some detail. In particular, we
distinguish between schema-level and instance-level,
element-level and structure-level, and language-based
and constraint-based matchers. Based on our
classification we review some previous match
implementations thereby indicating which part of the
solution space they cover. We intend our taxonomy and
review of past work to be useful when comparing
different approaches to schema matching, when
developing a new match algorithm, and when implementing
a schema matching component.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "graph matching; machine learning; model management;
schema integration; schema matching",
}
@Article{Saltenis:2002:INR,
author = "Simonas {\v{S}}altenis and Christian S. Jensen",
title = "Indexing of now-relative spatio-bitemporal data",
journal = j-VLDB-J,
volume = "11",
number = "1",
pages = "1--16",
month = aug,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100058",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:00 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011001/20110001.htm;
http://link.springer.de/link/service/journals/00778/papers/2011001/20110001.pdf",
abstract = "Real-world entities are inherently spatially and
temporally referenced, and database applications
increasingly exploit databases that record the past,
present, and anticipated future locations of entities,
e.g., the residences of customers obtained by the
geo-coding of addresses. Indices that efficiently
support queries on the spatio-temporal extents of such
entities are needed. However, past indexing research
has progressed in largely separate spatial and temporal
streams. Adding time dimensions to spatial indices, as
if time were a spatial dimension, neither supports nor
exploits the special properties of time. On the other
hand, temporal indices are generally not amenable to
extension with spatial dimensions. This paper proposes
the first efficient and versatile index for a general
class of spatio-temporal data: the discretely changing
spatial aspect of an object may be a point or may have
an extent; both transaction time and valid time are
supported, and a generalized notion of the current
time, {\em now}, is accommodated for both temporal
dimensions. The index is based on the R$^*$-tree and
provides means of prioritizing space versus time, which
enables it to adapt to spatially and temporally
restrictive queries. Performance experiments are
reported that evaluate pertinent aspects of the
index.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access method; bitemporal data; multidimensional
indexing; R-tree; spatio-temporal data; transaction
time; valid time",
}
@Article{Rafiei:2002:ERS,
author = "Davood Rafiei and Alberto O. Mendelzon",
title = "Efficient retrieval of similar shapes",
journal = j-VLDB-J,
volume = "11",
number = "1",
pages = "17--27",
month = aug,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780100059",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:00 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011001/20110017.htm;
http://link.springer.de/link/service/journals/00778/papers/2011001/20110017.pdf",
abstract = "We propose an indexing technique for the fast
retrieval of objects in 2D images based on similarity
between their boundary shapes. Our technique is robust
in the presence of noise and supports several important
notions of similarity including optimal matches
irrespective of variations in orientation and/or
position. Our method can also handle size-invariant
matches using a normalization technique, although
optimality is not guaranteed here. We implemented our
method and performed experiments on real (hand-written
digits) data. Our experimental results showed the
superiority of our method compared to search based on
sequential scanning, which is the only obvious
competitor. The performance gain of our method
increases with any increase in the number or the size
of shapes.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Fourier descriptors; image databases; shape retrieval;
similarity queries; similarity retrieval",
}
@Article{Navarro:2002:SMS,
author = "Gonzalo Navarro",
title = "Searching in metric spaces by spatial approximation",
journal = j-VLDB-J,
volume = "11",
number = "1",
pages = "28--46",
month = aug,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780200060",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:00 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011001/20110028.htm;
http://link.springer.de/link/service/journals/00778/papers/2011001/20110028.pdf",
abstract = "We propose a new data structure to search in metric
spaces. A {\em metric space\/} is formed by a
collection of objects and a {\em distance function\/}
defined among them which satisfies the triangle
inequality. The goal is, given a set of objects and a
query, retrieve those objects close enough to the
query. The complexity measure is the number of
distances computed to achieve this goal. Our data
structure, called {\em sa-tree\/} (``spatial
approximation tree''), is based on approaching the
searched objects spatially, that is, getting closer and
closer to them, rather than the classic
divide-and-conquer approach of other data structures.
We analyze our method and show that the number of
distance evaluations to search among $n$ objects is
sublinear. We show experimentally that the {\em
sa-tree\/} is the best existing technique when the
metric space is hard to search or the query has low
selectivity. These are the most important unsolved
cases in real applications. As a practical advantage,
our data structure is one of the few that does not need
to tune parameters, which makes it appealing for use by
non-experts.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "multimedia databases; similarity or proximity search;
spatial and multidimensional search; spatial
approximation tree",
}
@Article{Mihaila:2002:LAD,
author = "George A. Mihaila and Louiqa Raschid and Anthony
Tomasic",
title = "Locating and accessing data repositories with
{WebSemantics}",
journal = j-VLDB-J,
volume = "11",
number = "1",
pages = "47--57",
month = aug,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780200061",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:00 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011001/20110047.htm;
http://link.springer.de/link/service/journals/00778/papers/2011001/20110047.pdf",
abstract = "Many collections of scientific data in particular
disciplines are available today on the World Wide Web.
Most of these data sources are compliant with some
standard for interoperable access. In addition, sources
may support a common semantics, i.e., a shared meaning
for the data types and their domains. However, sharing
data among a global community of users is still
difficult because of the following reasons: (i) data
providers need a mechanism for describing and
publishing available sources of data; (ii) data
administrators need a mechanism for discovering the
location of published sources and obtaining metadata
from these sources; and (iii) users need a mechanism
for browsing and selecting sources. This paper
describes a system, WebSemantics, that accomplishes the
above tasks. We describe an architecture for the
publication and discovery of scientific data sources,
which is an extension of the World Wide Web
architecture and protocols. We support catalogs
containing metadata about data sources for some
application domain. We define a language for
discovering sources and querying their metadata. We
then describe the WebSemantics prototype.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data discovery; data integration; mediators; query
languages; World Wide Web; XML",
}
@Article{Ferrari:2002:ASD,
author = "E. Ferrari and N. R. Adam and V. Atluri and E. Bertino
and U. Capuozzo",
title = "An authorization system for digital libraries",
journal = j-VLDB-J,
volume = "11",
number = "1",
pages = "58--67",
month = aug,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780200063",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:00 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011001/20110058.htm;
http://link.springer.de/link/service/journals/00778/papers/2011001/20110058.pdf",
abstract = "Digital Libraries (DLs) introduce several challenging
requirements with respect to the formulation,
specification, and enforcement of adequate data
protection policies. Unlike conventional database
environments, a DL environment typically is
characterized by a dynamic subject population, often
making accesses from remote locations, and by an
extraordinarily large amount of multimedia information,
stored in a variety of formats. Moreover, in a DL
environment, access policies are often specified based
on subject qualifications and characteristics, rather
than subject identity. Traditional authorization models
are not adequate to meet access control requirements of
DLs. In this paper, we present a {\em Digital Library
Authorization System\/} (DLAS). DLAS employs a
content-based authorization model, called a {\em
Digital Library Authorization Model\/} (DLAM) which was
proposed in previous work [1].",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access control; credentials; digital libraries",
}
@Article{Marathe:2002:QPT,
author = "Arunprasad P. Marathe and Kenneth Salem",
title = "Query processing techniques for arrays",
journal = j-VLDB-J,
volume = "11",
number = "1",
pages = "68--91",
month = aug,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780200062",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:00 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011001/20110068.htm;
http://link.springer.de/link/service/journals/00778/papers/2011001/20110068.pdf",
abstract = "Arrays are a common and important class of data. At
present, database systems do not provide adequate array
support: arrays can neither be easily defined nor
conveniently manipulated. Further, array manipulations
are not optimized. This paper describes a language
called the {\em Array Manipulation Language\/} (AML),
for expressing array manipulations, and a collection of
optimization techniques for AML expressions. In the AML
framework for array manipulation, arbitrary
externally-defined functions can be applied to arrays
in a structured manner. AML can be adapted to different
application domains by choosing appropriate external
function definitions. This paper concentrates on arrays
occurring in databases of digital images such as
satellite or medical images. AML queries can be treated
declaratively and subjected to rewrite optimizations.
Rewriting minimizes the number of applications of
potentially costly external functions required to
compute a query result. AML queries can also be
optimized for space. Query results are generated a
piece at a time by pipelined execution plans, and the
amount of memory required by a plan depends on the
order in which pieces are generated. An optimizer can
consider generating the pieces of the query result in a
variety of orders, and can efficiently choose orders
that require less space. An AML-based prototype array
database system called {\em ArrayDB\/} has been built,
and it is used to show the effectiveness of these
optimization techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "array manipulation language; array query optimization;
declarative query language; memory-usage optimization;
pipelined evaluation; user-defined functions",
}
@Article{Sakurai:2002:SIH,
author = "Yasushi Sakurai and Masatoshi Yoshikawa and Shunsuke
Uemura and Haruhiko Kojima",
title = "Spatial indexing of high-dimensional data based on
relative approximation",
journal = j-VLDB-J,
volume = "11",
number = "2",
pages = "93--108",
month = oct,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0066-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:01 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011002/20110093.htm;
http://link.springer.de/link/service/journals/00778/papers/2011002/20110093.pdf",
abstract = "We propose a novel index structure, the A-tree
(approximation tree), for similarity searches in
high-dimensional data. The basic idea of the A-tree is
the introduction of virtual bounding rectangles (VBRs)
which contain and approximate MBRs or data objects.
VBRs can be represented quite compactly and thus affect
the tree configuration both quantitatively and
qualitatively. First, since tree nodes can contain a
large number of VBR entries, fanout becomes large,
which increases search speed. More importantly, we have
a free hand in arranging MBRs and VBRs in the tree
nodes. Each A-tree node contains an MBR and its
children VBRs. Therefore, by fetching an A-tree node,
we can obtain information on the exact position of a
parent MBR and the approximate position of its
children. We have performed experiments using both
synthetic and real data sets. For the real data sets,
the A-tree outperforms the SR-tree and the VA-file in
all dimensionalities up to 64 dimensions, which is the
highest dimension in our experiments. Additionally, we
propose a cost model for the A-tree. We verify the
validity of the cost model for synthetic and real data
sets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "high-dimensional data; relative approximation;
similarity search",
}
@Article{Hjaltason:2002:SCP,
author = "Gisli R. Hjaltason and Hanan Samet",
title = "Speeding up construction of {PMR} quadtree-based
spatial indexes",
journal = j-VLDB-J,
volume = "11",
number = "2",
pages = "109--137",
month = oct,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0067-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:01 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011002/20110109.htm;
http://link.springer.de/link/service/journals/00778/papers/2011002/20110109.pdf",
abstract = "Spatial indexes, such as those based on the quadtree,
are important in spatial databases for efficient
execution of queries involving spatial constraints,
especially when the queries involve spatial joins. In
this paper we present a number of techniques for
speeding up the construction of quadtree-based spatial
indexes, specifically the PMR quadtree, which can index
arbitrary spatial data. We assume a quadtree
implementation using the ``linear quadtree'', a
disk-resident representation that stores objects
contained in the leaf nodes of the quadtree in a linear
index (e.g., a B-tree) ordered based on a space-filling
curve. We present two complementary techniques: an
improved insertion algorithm and a bulk-loading method.
The bulk-loading method can be extended to handle
bulk-insertions into an existing PMR quadtree. We make
some analytical observations about the I/O cost and CPU
cost of our PMR quadtree bulk-loading algorithm, and
conduct an extensive empirical study of the techniques
presented in the paper. Our techniques are found to
yield significant speedup compared to traditional
quadtree building methods, even when the size of a main
memory buffer is very small compared to the size of the
resulting quadtrees.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "bulk-loading; I/O; spatial indexing",
}
@Article{Nanopoulos:2002:ESS,
author = "Alexandros Nanopoulos and Yannis Manolopoulos",
title = "Efficient similarity search for market basket data",
journal = j-VLDB-J,
volume = "11",
number = "2",
pages = "138--152",
month = oct,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0068-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:01 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011002/20110138.htm;
http://link.springer.de/link/service/journals/00778/papers/2011002/20110138.pdf",
abstract = "Several organizations have developed very large market
basket databases for the maintenance of customer
transactions. New applications, e.g., Web
recommendation systems, present the requirement for
processing similarity queries in market basket
databases. In this paper, we propose a novel scheme for
similarity search queries in basket data. We develop a
new representation method, which, in contrast to
existing approaches, is proven to provide correct
results. New algorithms are proposed for the processing
of similarity queries. Extensive experimental results,
for a variety of factors, illustrate the superiority of
the proposed scheme over the state-of-the-art method.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data mining; market basket data; nearest-neighbor;
similarity search",
}
@Article{Feng:2002:TMM,
author = "Ling Feng and Jeffrey Xu Yu and Hongjun Lu and Jiawei
Han",
title = "A template model for multidimensional
inter-transactional association rules",
journal = j-VLDB-J,
volume = "11",
number = "2",
pages = "153--175",
month = oct,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0069-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:01 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011002.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011002/20110153.htm;
http://link.springer.de/link/service/journals/00778/papers/2011002/20110153.pdf",
abstract = "Multidimensional inter-transactional association rules
extend the traditional association rules to describe
more general associations among items with multiple
properties across transactions. ``{\em After McDonald
and Burger King open branches, KFC will open a branch
two months later and one mile away}'' is an example of
such rules. Since the number of potential
inter-transactional association rules tends to be
extremely large, mining inter-transactional
associations poses more challenges on efficient
processing than mining traditional intra-transactional
associations. In order to make such association rule
mining truly practical and computationally tractable,
in this study we present a template model to help users
declare the interesting {\em multidimensional
inter-transactional associations\/} to be mined. With
the guidance of templates, several optimization
techniques, i.e., joining, converging, and speeding,
are devised to speed up the discovery of
inter-transactional association rules. We show, through
a series of experiments on both synthetic and real-life
data sets, that these optimization techniques can yield
significant performance benefits.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "intra-transactional/inter-transactional association
rules; multidimensional context; template model",
}
@Article{Apers:2002:E,
author = "Peter Apers and Stefano Ceri and Richard Snodgrass",
title = "Editorial",
journal = j-VLDB-J,
volume = "11",
number = "3",
pages = "177--178",
month = nov,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0075-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:02 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Special issue VLDB best papers 2001.",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011003/20110177.htm;
http://link.springer.de/link/service/journals/00778/papers/2011003/20110177.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{An:2002:EPT,
author = "Ning An and Sudhanva Gurumurthi and Anand
Sivasubramaniam and Narayanan Vijaykrishnan and Mahmut
Kandemir and Mary Jane Irwin",
title = "Energy-performance trade-offs for spatial access
methods on memory-resident data",
journal = j-VLDB-J,
volume = "11",
number = "3",
pages = "179--197",
month = nov,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0073-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:02 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Special issue VLDB best papers 2001.",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011003/20110179.htm;
http://link.springer.de/link/service/journals/00778/papers/2011003/20110179.pdf",
abstract = "The proliferation of mobile and pervasive computing
devices has brought energy constraints into the
limelight. Energy-conscious design is important at all
levels of system architecture, and the software has a
key role to play in conserving battery energy on these
devices. With the increasing popularity of spatial
database applications, and their anticipated deployment
on mobile devices (such as road atlases and GPS-based
applications), it is critical to examine the energy
implications of spatial data storage and access methods
for memory resident datasets. While there has been
extensive prior research on spatial access methods on
resource-rich environments, this is, perhaps, the first
study to examine their suitability for
resource-constrained environments. Using a detailed
cycle-accurate energy estimation framework and four
different datasets, this paper examines the pros and
cons of three previously proposed spatial indexing
alternatives from both the energy and performance
angles. Specifically, the Quadtree, Packed R-tree, and
Buddy-Tree structures are evaluated and compared with a
brute-force approach that does not use an index. The
results show that there are both performance and energy
trade-offs between the indexing schemes for the
different queries. The nature of the query also plays
an important role in determining the energy-performance
trade-offs. Further, technological trends and
architectural enhancements are influencing factors on
the relative behavior of the index structures. The work
in the query has a bearing on how and where (on a
mobile client or/and on a server) it should be
performed for performance and energy savings. The
results from this study will be beneficial for the
design and implementation of embedded spatial
databases, accelerating their deployment on numerous
mobile devices.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "energy optimization; multidimensional indexing;
resource-constrained computing; spatial data",
}
@Article{Ailamaki:2002:DPL,
author = "Anastassia Ailamaki and David J. DeWitt and Mark D.
Hill",
title = "Data page layouts for relational databases on deep
memory hierarchies",
journal = j-VLDB-J,
volume = "11",
number = "3",
pages = "198--215",
month = nov,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0074-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:02 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Special issue VLDB best papers 2001.",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011003/20110198.htm;
http://link.springer.de/link/service/journals/00778/papers/2011003/20110198.pdf",
abstract = "Relational database systems have traditionally
optimized for I/O performance and organized records
sequentially on disk pages using the N-ary Storage
Model (NSM) (a.k.a., slotted pages). Recent research,
however, indicates that cache utilization and
performance is becoming increasingly important on
modern platforms. In this paper, we first demonstrate
that in-page data placement is the key to high cache
performance and that NSM exhibits low cache utilization
on modern platforms. Next, we propose a new data
organization model called PAX (Partition Attributes
Across), that significantly improves cache performance
by grouping together all values of each attribute
within each page. Because PAX only affects layout
inside the pages, it incurs no storage penalty and does
not affect I/O behavior. According to our experimental
results (which were obtained without using any indices
on the participating relations), when compared to NSM:
(a) PAX exhibits superior cache and memory bandwidth
utilization, saving at least 75\% of NSM's stall time
due to data cache accesses; (b) range selection queries
and updates on memory-resident relations execute 1725\%
faster; and (c) TPC-H queries involving I/O execute
1148\% faster. Finally, we show that PAX performs well
across different memory system designs.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "cache-conscious database systems; disk page layout;
relational data placement",
}
@Article{Chirkova:2002:FPV,
author = "Rada Chirkova and Alon Y. Halevy and Dan Suciu",
title = "A formal perspective on the view selection problem",
journal = j-VLDB-J,
volume = "11",
number = "3",
pages = "216--237",
month = nov,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0070-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:02 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Special issue VLDB best papers 2001.",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011003/20110216.htm;
http://link.springer.de/link/service/journals/00778/papers/2011003/20110216.pdf",
abstract = "The view selection problem is to choose a set of views
to materialize over a database schema, such that the
cost of evaluating a set of workload queries is
minimized and such that the views fit into a
prespecified storage constraint. The two main
applications of the view selection problem are
materializing views in a database to speed up query
processing, and selecting views to materialize in a
data warehouse to answer decision support queries. In
addition, view selection is a core problem for
intelligent data placement over a wide-area network for
data integration applications and data management for
ubiquitous computing. We describe several fundamental
results concerning the view selection problem. We
consider the problem for views and workloads that
consist of equality-selection, project and join
queries, and show that the complexity of the problem
depends crucially on the quality of the estimates that
a query optimizer has on the size of the views it is
considering to materialize. When a query optimizer has
good estimates of the sizes of the views, we show a
somewhat surprising result, namely, that an optimal
choice of views may involve a number of views that is
exponential in the size of the database schema. On the
other hand, when an optimizer uses standard estimation
heuristics, we show that the number of necessary views
and the expression size of each view are polynomially
bounded.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "materialized views; view selection",
}
@Article{Aguilera:2002:VLS,
author = "Vincent Aguilera and Sophie Cluet and Tova Milo and
Pierangelo Veltri and Dan Vodislav",
title = "Views in a large-scale {XML} repository",
journal = j-VLDB-J,
volume = "11",
number = "3",
pages = "238--255",
month = nov,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0065-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:02 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Special issue VLDB best papers 2001.",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011003/20110238.htm;
http://link.springer.de/link/service/journals/00778/papers/2011003/20110238.pdf",
abstract = "We are interested in defining and querying views in a
huge and highly heterogeneous XML repository (Web
scale). In this context, view definitions are very
large, involving lots of sources, and there is no
apparent limitation to their size. This raises
interesting problems that we address in the paper: (i)
how to distribute views over several machines without
having a negative impact on the query translation
process; (ii) how to quickly select the relevant part
of a view given a query; (iii) how to minimize the cost
of communicating potentially large queries to the
machines where they will be evaluated. The solution
that we propose is based on a simple view definition
language that allows for automatic generation of views.
The language maps paths in the view abstract DTD to
paths in the concrete source DTDs. It enables a
distributed implementation of the view system that is
scalable both in terms of data and load. In particular,
the query translation algorithm is shown to have a good
(linear) complexity.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "query evaluation; semantic integration; views;
warehouse; XML",
}
@Article{Hunt:2002:DIL,
author = "Ela Hunt and Malcolm P. Atkinson and Robert W.
Irving",
title = "Database indexing for large {DNA} and protein sequence
collections",
journal = j-VLDB-J,
volume = "11",
number = "3",
pages = "256--271",
month = nov,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s007780200064",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:02 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011003.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "Special issue VLDB best papers 2001.",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011003/20110256.htm;
http://link.springer.de/link/service/journals/00778/papers/2011003/20110256.pdf",
abstract = "Our aim is to develop new database technologies for
the approximate matching of unstructured string data
using indexes. We explore the potential of the suffix
tree data structure in this context. We present a new
method of building suffix trees, allowing us to build
trees in excess of RAM size, which has hitherto not
been possible. We show that this method performs in
practice as well as the $ O(n) $ method of Ukkonen
[70]. Using this method we build indexes for 200 Mb of
protein and 300 Mbp of DNA, whose disk-image exceeds
the available RAM. We show experimentally that suffix
trees can be effectively used in approximate string
matching with biological data. For a range of query
lengths and error bounds the suffix tree reduces the
size of the unoptimised $ O(m n) $ dynamic programming
calculation required in the evaluation of string
similarity, and the gain from indexing increases with
index size. In the indexes we built this reduction is
significant, and less than 0.3\% of the expected matrix
is evaluated. We detail the requirements for further
database and algorithmic research to support efficient
use of large suffix indexes in biological
applications.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "approximate matching; biological sequence; database
index; suffix tree",
}
@Article{Halevy:2002:GE,
author = "Alon Y. Halevy",
title = "Guest Editorial",
journal = j-VLDB-J,
volume = "11",
number = "4",
pages = "273--273",
month = dec,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0082-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:03 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011004/20110273.htm;
http://link.springer.de/link/service/journals/00778/papers/2011004/20110273.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Jagadish:2002:TNX,
author = "H. V. Jagadish and S. Al-Khalifa and A. Chapman and L.
V. S. Lakshmanan and A. Nierman and S. Paparizos and J.
M. Patel and D. Srivastava and N. Wiwatwattana and Y.
Wu and C. Yu",
title = "{TIMBER}: a native {XML} database",
journal = j-VLDB-J,
volume = "11",
number = "4",
pages = "274--291",
month = dec,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0081-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:03 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011004/20110274.htm;
http://link.springer.de/link/service/journals/00778/papers/2011004/20110274.pdf",
abstract = "This paper describes the overall design and
architecture of the Timber XML database system
currently being implemented at the University of
Michigan. The system is based upon a bulk algebra for
manipulating trees, and natively stores XML. New access
methods have been developed to evaluate queries in the
XML context, and new cost estimation and query
optimization techniques have also been developed. We
present performance numbers to support some of our
design decisions. We believe that the key intellectual
contribution of this system is a comprehensive
set-at-a-time query processing ability in a native XML
store, with all the standard components of relational
query processing, including algebraic rewriting and a
cost-based optimizer.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "algebra; document management; hierarchical; query
processing; semi-structured",
}
@Article{Fiebig:2002:ANX,
author = "T. Fiebig and S. Helmer and C.-C. Kanne and G.
Moerkotte and J. Neumann and R. Schiele and T.
Westmann",
title = "Anatomy of a native {XML} base management system",
journal = j-VLDB-J,
volume = "11",
number = "4",
pages = "292--314",
month = dec,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0080-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:03 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011004/20110292.htm;
http://link.springer.de/link/service/journals/00778/papers/2011004/20110292.pdf",
abstract = "Several alternatives to manage large XML document
collections exist, ranging from file systems over
relational or other database systems to specifically
tailored XML base management systems. In this paper we
give a tour of Natix, a database management system
designed from scratch for storing and processing XML
data. Contrary to the common belief that management of
XML data is just another application for traditional
databases like relational systems, we illustrate how
almost every component in a database system is affected
in terms of adequacy and performance. We show how to
design and optimize areas such as storage, transaction
management --- comprising recovery and multi-user
synchronization --- as well as query processing for
XML.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database; XML",
}
@Article{Amer-Yahia:2002:TPQ,
author = "S. Amer-Yahia and S. Cho and L. V. S. Lakshmanan and
D. Srivastava",
title = "Tree pattern query minimization",
journal = j-VLDB-J,
volume = "11",
number = "4",
pages = "315--331",
month = dec,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0076-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:03 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011004/20110315.htm;
http://link.springer.de/link/service/journals/00778/papers/2011004/20110315.pdf",
abstract = "Tree patterns form a natural basis to query
tree-structured data such as XML and LDAP. To improve
the efficiency of tree pattern matching, it is
essential to quickly identify and eliminate redundant
nodes in the pattern. In this paper, we study tree
pattern minimization both in the absence and in the
presence of integrity constraints (ICs) on the
underlying tree-structured database. In the absence of
ICs, we develop a polynomial-time query minimization
algorithm called CIM, whose efficiency stems from two
key properties: (i) a node cannot be redundant unless
its children are; and (ii) the order of elimination of
redundant nodes is immaterial. When ICs are considered
for minimization, we develop a technique for query
minimization based on three fundamental operations:
augmentation (an adaptation of the well-known chase
procedure), minimization (based on homomorphism
techniques), and reduction. We show the surprising
result that the algorithm, referred to as ACIM,
obtained by first augmenting the tree pattern using
ICs, and then applying CIM, always finds the unique
minimal equivalent query. While ACIM is polynomial
time, it can be expensive in practice because of its
inherent non-locality. We then present a fast
algorithm, CDM, that identifies and eliminates local
redundancies due to ICs, based on propagating
``information labels'' up the tree pattern. CDM can be
applied prior to ACIM for improving the minimization
efficiency. We complement our analytical results with
an experimental study that shows the effectiveness of
our tree pattern minimization techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "query minimization; tree patterns; XML",
}
@Article{Chien:2002:ESM,
author = "S.-Y. Chien and V. J. Tsotras and C. Zaniolo",
title = "Efficient schemes for managing multiversion {XML}
documents",
journal = j-VLDB-J,
volume = "11",
number = "4",
pages = "332--353",
month = dec,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0079-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:03 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011004/20110332.htm;
http://link.springer.de/link/service/journals/00778/papers/2011004/20110332.pdf",
abstract = "Multiversion support for XML documents is needed in
many critical applications, such as software
configuration control, cooperative authoring, web
information warehouses, and ``e-permanence'' of web
documents. In this paper, we introduce efficient and
robust techniques for: (i) storing and retrieving; (ii)
viewing and exchanging; and (iii) querying multiversion
XML documents. We first discuss the limitations of
traditional version control methods, such as RCS and
SCCS, and then propose novel techniques that overcome
their limitations. Initially, we focus on the problem
of managing secondary storage efficiently, and
introduce an {\em edit-based\/} versioning scheme that
enhances RCS with an effective clustering policy based
on the concept of page-usefulness. The new scheme
drastically improves version retrieval at the expense
of a small (linear) space overhead. However, the
edit-based approach falls short of achieving objectives
(ii) and (iii). Therefore, we introduce and investigate
a second scheme, which is reference-based and preserves
the structure of the original document. In the
reference-based approach, a multiversion document can
be represented as yet another XML document, which can
be easily exchanged and viewed on the web; furthermore,
simple queries are also expressed and supported well
under this representation. To achieve objective (i), we
extend the page-usefulness clustering technique to the
reference-based scheme. After characterizing the
asymptotic behavior of the new techniques proposed, the
paper presents the results of an experimental study
evaluating and comparing their performance.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "historical queries; temporal clustering; temporal
indexing; version management; XML database",
}
@Article{Chan:2002:EFX,
author = "C.-Y. Chan and P. Felber and M. Garofalakis and R.
Rastogi",
title = "Efficient filtering of {XML} documents with {XPath}
expressions",
journal = j-VLDB-J,
volume = "11",
number = "4",
pages = "354--379",
month = dec,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0077-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:03 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011004/20110354.htm;
http://link.springer.de/link/service/journals/00778/papers/2011004/20110354.pdf",
abstract = "The publish/subscribe paradigm is a popular model for
allowing publishers (i.e., data generators) to
selectively disseminate data to a large number of
widely dispersed subscribers (i.e., data consumers) who
have registered their interest in specific information
items. Early publish/subscribe systems have typically
relied on simple subscription mechanisms, such as
keyword or ``bag of words'' matching, or simple
comparison predicates on attribute values. The
emergence of XML as a standard for information exchange
on the Internet has led to an increased interest in
using more expressive subscription mechanisms (e.g.,
based on XPath expressions) that exploit both the
structure and the content of published XML documents.
Given the increased complexity of these new
data-filtering mechanisms, the problem of effectively
identifying the subscription profiles that match an
incoming XML document poses a difficult and important
research challenge. In this paper, we propose a novel
index structure, termed XTrie, that supports the
efficient filtering of XML documents based on XPath
expressions. Our XTrie index structure offers several
novel features that, we believe, make it especially
attractive for large-scale publish/subscribe systems.
First, XTrie is designed to support effective filtering
based on complex XPath expressions (as opposed to
simple, single-path specifications). Second, our XTrie
structure and algorithms are designed to support both
ordered and unordered matching of XML data. Third, by
indexing on sequences of elements organized in a trie
structure and using a sophisticated matching algorithm,
XTrie is able to both reduce the number of unnecessary
index probes as well as avoid redundant matchings,
thereby providing extremely efficient filtering. Our
experimental results over a wide range of XML document
and XPath expression workloads demonstrate that our
XTrie index structure outperforms earlier approaches by
wide margins.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data dissemination; document filtering; index
structure; XML; XPath",
}
@Article{Ives:2002:XQE,
author = "Zachary G. Ives and A. Y. Halevy and D. S. Weld",
title = "An {XML} query engine for network-bound data",
journal = j-VLDB-J,
volume = "11",
number = "4",
pages = "380--402",
month = dec,
year = "2002",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0078-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:03 MDT 2008",
bibsource = "http://link.springer.de/link/service/journals/00778/tocs/t2011004.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/2011004/20110380.htm;
http://link.springer.de/link/service/journals/00778/papers/2011004/20110380.pdf",
abstract = "XML has become the lingua franca for data exchange and
integration across administrative and enterprise
boundaries. Nearly all data providers are adding XML
import or export capabilities, and standard XML Schemas
and DTDs are being promoted for all types of data
sharing. The ubiquity of XML has removed one of the
major obstacles to integrating data from widely
disparate sources --- namely, the heterogeneity of data
formats. However, general-purpose integration of data
across the wide are a also requires a query processor
that can query data sources on demand, receive streamed
XML data from them, and combine and restructure the
data into new XML output --- while providing good
performance for both batch-oriented and ad hoc,
interactive queries. This is the goal of the Tukwila
data integration system, the first system that focuses
on network-bound, dynamic XML data sources. In contrast
to previous approaches, which must read, parse, and
often store entire XML objects before querying them,
Tukwila can return query results even as the data is
streaming into the system. Tukwila is built with a new
system architecture that extends adaptive query
processing and relational-engine techniques into the
XML realm, as facilitated by a pair of operators that
incrementally evaluate a query's input path expressions
as data is read. In this paper, we describe the Tukwila
architecture and its novel aspects, and we
experimentally demonstrate that Tukwila provides better
overall query performance and faster initial answers
than existing systems, and has excellent scalability.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data integration; data streams; query processing; web
and databases; XML",
}
@Article{Ozsu:2003:NPA,
author = "M. Tamer {\"O}zsu",
title = "New partnership with {ACM} and update on the journal",
journal = j-VLDB-J,
volume = "12",
number = "1",
pages = "1--1",
month = may,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0089-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:05 MDT 2008",
bibsource = "http://link.springer-ny.com/link/service/journals/UNKNOWN/tocs/t3012001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/3012001/30120001.htm;
http://link.springer.de/link/service/journals/00778/papers/3012001/30120001.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sheth:2003:CRK,
author = "A. Sheth and S. Thacker and S. Patel",
title = "Complex relationships and knowledge discovery support
in the {InfoQuilt} system",
journal = j-VLDB-J,
volume = "12",
number = "1",
pages = "2--27",
month = may,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0071-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:05 MDT 2008",
bibsource = "http://link.springer-ny.com/link/service/journals/UNKNOWN/tocs/t3012001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/3012001/30120002.htm;
http://link.springer.de/link/service/journals/00778/papers/3012001/30120002.pdf",
abstract = "Support for semantic content is becoming more common
in Web-accessible information systems. We see this
support emerging with the use of ontologies and
machine-readable, annotated documents. The practice of
domain modeling coupled with the extraction of
domain-specific, contextually relevant metadata also
supports the use of semantics. These advancements
enable knowledge discovery approaches that define
complex relationships between data that is autonomously
collected and managed. The InfoQuilt (One of the
incarnations of the InfoQuilt system, as applied to the
geographic information as part of the NSF Digital
Library II initiative is the ADEPT-UGA system [Ade].
This research was funded in part by National Science
Foundation grant IIS-9817432.) system supports one such
knowledge discovery approach. This paper presents
(parts of) the InfoQuilt system with the focus on its
use for modeling and utilizing complex semantic
inter-domain relationships to enable human-assisted
knowledge discovery over Web-accessible heterogeneous
data. This includes the specification and execution of
Information Scale (IScapes), a semantically rich
information request and correlation mechanism.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Verykios:2003:BDM,
author = "V. S. Verykios and G. V. Moustakides and M. G.
Elfeky",
title = "A {Bayesian} decision model for cost optimal record
matching",
journal = j-VLDB-J,
volume = "12",
number = "1",
pages = "28--40",
month = may,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0072-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:05 MDT 2008",
bibsource = "http://link.springer-ny.com/link/service/journals/UNKNOWN/tocs/t3012001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/3012001/30120028.htm;
http://link.springer.de/link/service/journals/00778/papers/3012001/30120028.pdf",
abstract = "In an error-free system with perfectly clean data, the
construction of a global view of the data consists of
linking --- in relational terms, joining --- two or
more tables on their key fields. Unfortunately, most of
the time, these data are neither carefully controlled
for quality nor necessarily defined commonly across
different data sources. As a result, the creation of
such a global data view resorts to approximate joins.
In this paper, an optimal solution is proposed for the
matching or the linking of database record pairs in the
presence of inconsistencies, errors or missing values
in the data. Existing models for record matching rely
on decision rules that minimize the probability of
error, that is the probability that a sample (a
measurement vector) is assigned to the wrong class. In
practice though, minimizing the probability of error is
not the best criterion to design a decision rule
because the misclassifications of different samples may
have different consequences. In this paper we present a
decision model that minimizes the cost of making a
decision. In particular: (a) we present a decision
rule: (b) we prove that this rule is optimal with
respect to the cost of a decision: and (c) we compute
the probabilities of the two types of errors (Type I
and Type II) that incur when this rule is applied. We
also present a closed form decision model for a certain
class of record comparison pairs along with an example,
and results from comparing the proposed cost-based
model to the error-based model, for large record
comparison spaces.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "cost optimal statistical model; data cleaning; record
linkage",
}
@Article{Cui:2003:LTG,
author = "Y. Cui and J. Widom",
title = "Lineage tracing for general data warehouse
transformations",
journal = j-VLDB-J,
volume = "12",
number = "1",
pages = "41--58",
month = may,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0083-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:05 MDT 2008",
bibsource = "http://link.springer-ny.com/link/service/journals/UNKNOWN/tocs/t3012001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/3012001/30120041.htm;
http://link.springer.de/link/service/journals/00778/papers/3012001/30120041.pdf",
abstract = "Data warehousing systems integrate information from
operational data sources into a central repository to
enable analysis and mining of the integrated
information. During the integration process, source
data typically undergoes a series of {\em
transformations}, which may vary from simple algebraic
operations or aggregations to complex ``data
cleansing'' procedures. In a warehousing environment,
the {\em data lineage\/} problem is that of tracing
warehouse data items back to the original source items
from which they were derived. We formally define the
lineage tracing problem in the presence of general data
warehouse transformations, and we present algorithms
for lineage tracing in this environment. Our tracing
procedures take advantage of known structure or
properties of transformations when present, but also
work in the absence of such information. Our results
can be used as the basis for a lineage tracing tool in
a general warehousing setting, and also can guide the
design of data warehouses that enable efficient lineage
tracing.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data lineage; data warehouse; inverse; lineage
tracing; transformation",
}
@Article{Medjahed:2003:BBI,
author = "B. Medjahed and B. Benatallah and A. Bouguettaya and
A. H. H. Ngu and A. K. Elmagarmid",
title = "Business-to-business interactions: issues and enabling
technologies",
journal = j-VLDB-J,
volume = "12",
number = "1",
pages = "59--85",
month = may,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0087-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:05 MDT 2008",
bibsource = "http://link.springer-ny.com/link/service/journals/UNKNOWN/tocs/t3012001.htm;
http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.de/link/service/journals/00778/bibs/3012001/30120059.htm;
http://link.springer.de/link/service/journals/00778/papers/3012001/30120059.pdf",
abstract = "Business-to-Business (B2B) technologies pre-date the
Web. They have existed for at least as long as the
Internet. B2B applications were among the first to take
advantage of advances in computer networking. The
Electronic Data Interchange (EDI) business standard is
an illustration of such an early adoption of the
advances in computer networking. The ubiquity and the
affordability of the Web has made it possible for the
masses of businesses to automate their B2B
interactions. However, several issues related to scale,
content exchange, autonomy, heterogeneity, and other
issues still need to be addressed. In this paper, we
survey the main techniques, systems, products, and
standards for B2B interactions. We propose a set of
criteria for assessing the different B2B interaction
techniques, standards, and products.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "B2B interactions; components; e-commerce; EDI; Web
services; workflows; XML",
}
@Article{Bernstein:2003:GE,
author = "Philip A. Bernstein and Yannis Ioannidis and Raghu
Ramakrishnan",
title = "Guest editorial",
journal = j-VLDB-J,
volume = "12",
number = "2",
pages = "87--88",
month = aug,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0092-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:06 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ramamurthy:2003:CFM,
author = "Ravishankar Ramamurthy and David J. DeWitt and Qi Su",
title = "A case for fractured mirrors",
journal = j-VLDB-J,
volume = "12",
number = "2",
pages = "89--101",
month = aug,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0093-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:06 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The decomposition storage model (DSM) vertically
partitions all attributes of a table and has excellent
I/O behavior when the number of attributes accessed by
a query is small. It also has a better cache footprint
than the standard storage model (NSM) used by most
database systems. However, DSM incurs a high cost in
reconstructing the original tuple from its partitions.
We first revisit some of the performance problems
associated with DSM and suggest a simple indexing
strategy and compare different reconstruction
algorithms. Then we propose a new mirroring scheme,
termed fractured mirrors, using both NSM and DSM
models. This scheme combines the best aspects of both
models, along with the added benefit of mirroring to
better serve an ad hoc query workload. A prototype
system has been built using the Shore storage manager,
and performance is evaluated using queries from the
TPC-H workload.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data placement; disk mirroring; vertical
partitioning",
}
@Article{Chan:2003:RTE,
author = "Chee-Yong Chan and Minos Garofalakis and Rajeev
Rastogi",
title = "{RE}-tree: an efficient index structure for regular
expressions",
journal = j-VLDB-J,
volume = "12",
number = "2",
pages = "102--119",
month = aug,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0094-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:06 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Due to their expressive power, regular expressions
(REs) are quickly becoming an integral part of language
specifications for several important application
scenarios. Many of these applications have to manage
huge databases of RE specifications and need to provide
an effective matching mechanism that, given an input
string, quickly identifies the REs in the database that
match it. In this paper, we propose the RE-tree, a
novel index structure for large databases of RE
specifications. Given an input query string, the
RE-tree speeds up the retrieval of matching REs by
focusing the search and comparing the input string with
only a small fraction of REs in the database. Even
though the RE-tree is similar in spirit to other
tree-based structures that have been proposed for
indexing multidimensional data, RE indexing is
significantly more challenging since REs typically
represent infinite sets of strings with no well-defined
notion of spatial locality. To address these new
challenges, our RE-tree index structure relies on novel
measures for comparing the relative sizes of infinite
regular languages. We also propose innovative solutions
for the various RE-tree operations including the
effective splitting of RE-tree nodes and computing a
`tight' bounding RE for a collection of REs. Finally,
we demonstrate how sampling-based approximation
algorithms can be used to significantly speed up the
performance of RE-tree operations. Preliminary
experimental results with moderately large synthetic
data sets indicate that the RE-tree is effective in
pruning the search space and easily outperforms naive
sequential search approaches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "index structure; regular expressions; sampling-based
approximations; size measures",
}
@Article{Abadi:2003:ANM,
author = "Daniel J. Abadi and Don Carney and Ugur
{\c{C}}etintemel and Mitch Cherniack and Christian
Convey and Sangdon Lee and Michael Stonebraker and
Nesime Tatbul and Stan Zdonik",
title = "{Aurora}: a new model and architecture for data stream
management",
journal = j-VLDB-J,
volume = "12",
number = "2",
pages = "120--139",
month = aug,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0095-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:06 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper describes the basic processing model and
architecture of Aurora, a new system to manage data
streams for monitoring applications. Monitoring
applications differ substantially from conventional
business data processing. The fact that a software
system must process and react to continual inputs from
many sources (e.g., sensors) rather than from human
operators requires one to rethink the fundamental
architecture of a DBMS for this application area. In
this paper, we present Aurora, a new DBMS currently
under construction at Brandeis University, Brown
University, and M.I.T. We first provide an overview of
the basic Aurora model and architecture and then
describe in detail a stream-oriented set of
operators.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "continuous queries; data stream management; database
triggers; quality-of-service; real-time systems",
}
@Article{Chandrasekaran:2003:PSS,
author = "Sirish Chandrasekaran and Michael J. Franklin",
title = "{PSoup}: a system for streaming queries over streaming
data",
journal = j-VLDB-J,
volume = "12",
number = "2",
pages = "140--156",
month = aug,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0096-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:06 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Recent work on querying data streams has focused on
systems where newly arriving data is processed and
continuously streamed to the user in real time. In many
emerging applications, however, ad hoc queries and/or
intermittent connectivity also require the processing
of data that arrives prior to query submission or
during a period of disconnection. For such
applications, we have developed PSoup, a system that
combines the processing of ad hoc and continuous
queries by treating data and queries symmetrically,
allowing new queries to be applied to old data and new
data to be applied to old queries. PSoup also supports
intermittent connectivity by separating the computation
of query results from the delivery of those results.
PSoup builds on adaptive query-processing techniques
developed in the Telegraph project at UC Berkeley. In
this paper, we describe PSoup and present experiments
that demonstrate the effectiveness of our approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "disconnected operation; query-data duality; stream
query processing",
}
@Article{Agrawal:2003:WRD,
author = "Rakesh Agrawal and Peter J. Haas and Jerry Kiernan",
title = "Watermarking relational data: framework, algorithms
and analysis",
journal = j-VLDB-J,
volume = "12",
number = "2",
pages = "157--169",
month = aug,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0097-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:06 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We enunciate the need for watermarking database
relations to deter data piracy, identify the
characteristics of relational data that pose unique
challenges for watermarking, and delineate desirable
properties of a watermarking system for relational
data. We then present an effective watermarking
technique geared for relational data. This technique
ensures that some bit positions of some of the
attributes of some of the tuples contain specific
values. The specific bit locations and values are
algorithmically determined under the control of a
secret key known only to the owner of the data. This
bit pattern constitutes the watermark. Only if one has
access to the secret key can the watermark be detected
with high probability. Detecting the watermark requires
access neither to the original data nor the watermark,
and the watermark can be easily and efficiently
maintained in the presence of insertions, updates, and
deletions. Our analysis shows that the proposed
technique is robust against various forms of malicious
attacks as well as benign updates to the data. Using an
implementation running on DB2, we also show that the
algorithms perform well enough to be used in real-world
applications.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database; information hiding; steganography;
watermarking",
}
@Article{Chakrabarti:2003:FAT,
author = "Soumen Chakrabarti and Shourya Roy and Mahesh V.
Soundalgekar",
title = "Fast and accurate text classification via multiple
linear discriminant projections",
journal = j-VLDB-J,
volume = "12",
number = "2",
pages = "170--185",
month = aug,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0098-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:06 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Support vector machines (SVMs) have shown superb
performance for text classification tasks. They are
accurate, robust, and quick to apply to test instances.
Their only potential drawback is their training time
and memory requirement. For $n$ training instances held
in memory, the best-known SVM implementations take time
proportional to $ n^a$, where $a$ is typically between
1.8 and 2.1. SVMs have been trained on data sets with
several thousand instances, but Web directories today
contain millions of instances that are valuable for
mapping billions of Web pages into Yahoo!-like
directories. We present SIMPL, a nearly linear-time
classification algorithm that mimics the strengths of
SVMs while avoiding the training bottleneck. It uses
Fisher's linear discriminant, a classical tool from
statistical pattern recognition, to project training
instances to a carefully selected low-dimensional
subspace before inducing a decision tree on the
projected instances. SIMPL uses efficient sequential
scans and sorts and is comparable in speed and memory
scalability to widely used naive Bayes (NB)
classifiers, but it beats NB accuracy decisively. It
not only approaches and sometimes exceeds SVM accuracy,
but also beats the running time of a popular SVM
implementation by orders of magnitude. While describing
SIMPL, we make a detailed experimental comparison of
SVM-generated discriminants with Fisher's
discriminants, and we also report on an analysis of the
cache performance of a popular SVM implementation. Our
analysis shows that SIMPL has the potential to be the
method of choice for practitioners who want the
accuracy of SVMs and the simplicity and speed of naive
Bayes classifiers.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "discriminative learning; linear discriminants; text
classification",
}
@Article{Fung:2003:CDV,
author = "Chi-Wai Fung and Kamalakar Karlapalem and Qing Li",
title = "Cost-driven vertical class partitioning for methods in
object oriented databases",
journal = j-VLDB-J,
volume = "12",
number = "3",
pages = "187--210",
month = oct,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0084-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:07 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In object-oriented databases (OODBs), a method
encapsulated in a class typically accesses a few, but
not all the instance variables defined in the class. It
may thus be preferable to vertically partition the
class for reducing irrelevant data (instance variables)
accessed by the methods. Our prior work has shown that
vertical class partitioning can result in a substantial
decrease in the total number of disk accesses incurred
for executing a set of applications, but coming up with
an optimal vertical class partitioning scheme is a hard
problem. In this paper, we present two algorithms for
deriving optimal and near-optimal vertical class
partitioning schemes. The cost-driven algorithm
provides the optimal vertical class partitioning
schemes by enumerating, exhaustively, all the schemes
and calculating the number of disk accesses required to
execute a given set of applications. For this, a cost
model for executing a set of methods in an OODB system
is developed. Since exhaustive enumeration is costly
and only works for classes with a small number of
instance variables, a hill-climbing heuristic algorithm
(HCHA) is developed, which takes the solution provided
by the affinity-based algorithm and improves it,
thereby further reducing the total number of disk
accesses incurred. We show that the HCHA algorithm
provides a reasonable near-optimal vertical class
partitioning scheme for executing a given set of
applications.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "affinity-based; analytical cost model; cost-driven;
hill-climbing heuristic algorithm; method-induced;
object-oriented databases; vertical class
partitioning",
}
@Article{Li:2003:CCA,
author = "Chen Li",
title = "Computing complete answers to queries in the presence
of limited access patterns",
journal = j-VLDB-J,
volume = "12",
number = "3",
pages = "211--227",
month = oct,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-002-0085-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:07 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In data applications such as information integration,
there can be limited access patterns to relations,
i.e., binding patterns require values to be specified
for certain attributes in order to retrieve data from a
relation. As a consequence, we cannot retrieve all
tuples from these relations. In this article we study
the problem of computing the {\em complete\/} answer to
a query, i.e., the answer that could be computed if all
the tuples could be retrieved. A query is {\em
stable\/} if for any instance of the relations in the
query, its complete answer can be computed using the
access patterns permitted by the relations. We study
the problem of testing stability of various classes of
queries, including conjunctive queries, unions of
conjunctive queries, and conjunctive queries with
arithmetic comparisons. We give algorithms and
complexity results for these classes of queries. We
show that stability of datalog programs is undecidable,
and give a sufficient condition for stability of
datalog queries. Finally, we study data-dependent
computability of the complete answer to a nonstable
query, and propose a decision tree for guiding the
process to compute the complete answer.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "complete answers to queries; limited access patterns
to relations; query stability",
}
@Article{Chua:2003:IBA,
author = "Cecil Eng H. Chua and Roger H. L. Chiang and Ee-Peng
Lim",
title = "Instance-based attribute identification in database
integration",
journal = j-VLDB-J,
volume = "12",
number = "3",
pages = "228--243",
month = oct,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0088-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:07 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Most research on attribute identification in database
integration has focused on integrating attributes using
schema and summary information derived from the
attribute values. No research has attempted to fully
explore the use of attribute values to perform
attribute identification. We propose an attribute
identification method that employs schema and summary
instance information as well as properties of
attributes derived from their instances. Unlike other
attribute identification methods that match only single
attributes, our method matches attribute groups for
integration. Because our attribute identification
method fully explores data instances, it can identify
corresponding attributes to be integrated even when
schema information is misleading. Three experiments
were performed to validate our attribute identification
method. In the first experiment, the heuristic rules
derived for attribute classification were evaluated on
119 attributes from nine public domain data sets. The
second was a controlled experiment validating the
robustness of the proposed attribute identification
method by introducing erroneous data. The third
experiment evaluated the proposed attribute
identification method on five data sets extracted from
online music stores. The results demonstrated the
viability of the proposed method.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "attribute identification; database integration;
measures of association",
}
@Article{Helmer:2003:PSF,
author = "Sven Helmer and Guido Moerkotte",
title = "A performance study of four index structures for
set-valued attributes of low cardinality",
journal = j-VLDB-J,
volume = "12",
number = "3",
pages = "244--261",
month = oct,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0106-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:07 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The efficient retrieval of data items on set-valued
attributes is an important research topic that has
attracted little attention so far. We studied and
modified four index structures (sequential signature
files, signature trees, extendible signature hashing,
and inverted files) for a fast retrieval of sets with
low cardinality. We compared the index structures by
implementing them and subjecting them to extensive
experiments, investigating the influence of query set
size, database size, domain size, and data distribution
(synthetic and real). The results of the experiments
clearly indicate that inverted files exhibit the best
overall behavior of all tested index structures.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access methods; database management systems; index
structures; physical design; set-valued attributes",
}
@Article{Yang:2003:ICM,
author = "Jun Yang and Jennifer Widom",
title = "Incremental computation and maintenance of temporal
aggregates",
journal = j-VLDB-J,
volume = "12",
number = "3",
pages = "262--283",
month = oct,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0107-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:07 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We consider the problems of computing aggregation
queries in temporal databases and of maintaining
materialized temporal aggregate views efficiently. The
latter problem is particularly challenging since a
single data update can cause aggregate results to
change over the entire time line. We introduce a new
index structure called the {\em SB-tree}, which
incorporates features from both {\em segment-trees\/}
and {\em B-trees}. SB-trees support fast lookup of
aggregate results based on time and can be maintained
efficiently when the data change. We extend the basic
SB-tree index to handle {\em cumulative\/} (also called
{\em moving-window\/}) aggregates, considering
separatelycases when the window size is or is not fixed
in advance. For materialized aggregate views in a
temporal database or warehouse, we propose building and
maintaining SB-tree indices instead of the views
themselves.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access methods; aggregation; B-tree; segment tree;
temporal database; view maintenance",
}
@Article{Atluri:2003:GE,
author = "Vijay Atluri and Anupam Joshi and Yelena Yesha",
title = "Guest editorial",
journal = j-VLDB-J,
volume = "12",
number = "4",
pages = "285--285",
month = nov,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0109-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Maedche:2003:MMD,
author = "A. Maedche and B. Motik and L. Stojanovic",
title = "Managing multiple and distributed ontologies on the
{Semantic Web}",
journal = j-VLDB-J,
volume = "12",
number = "4",
pages = "286--302",
month = nov,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0102-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In traditional software systems, significant attention
is devoted to keeping modules well separated and
coherent with respect to functionality, thus ensuring
that changes in the system are localized to a handful
of modules. Reuse is seen as the key method in reaching
that goal. Ontology-based systems on the Semantic Web
are just a special class of software systems, so the
same principles apply. In this article, we present an
integrated framework for managing multiple and
distributed ontologies on the Semantic Web. It is based
on the representation model for ontologies, trading off
between expressivity and tractability. In our
framework, we provide features for reusing existing
ontologies and for evolving them while retaining the
consistency. The approach is implemented within KAON,
the Karlsruhe Ontology and Semantic Web tool suite.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "multiple and distributed ontologies; ontology
evolution",
}
@Article{Doan:2003:LMO,
author = "AnHai Doan and Jayant Madhavan and Robin Dhamankar and
Pedro Domingos and Alon Halevy",
title = "Learning to match ontologies on the {Semantic Web}",
journal = j-VLDB-J,
volume = "12",
number = "4",
pages = "303--319",
month = nov,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0104-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "On the Semantic Web, data will inevitably come from
many different ontologies, and information processing
across ontologies is not possible without knowing the
semantic mappings between them. Manually finding such
mappings is tedious, error-prone, and clearly not
possible on the Web scale. Hence the development of
tools to assist in the ontology mapping process is
crucial to the success of the Semantic Web. We describe
{\em GLUE}, a system that employs machine learning
techniques to find such mappings. Given two ontologies,
for each concept in one ontology {\em GLUE\/} finds the
most similar concept in the other ontology. We give
well-founded probabilistic definitions to several
practical similarity measures and show that {\em
GLUE\/} can work with all of them. Another key feature
of {\em GLUE\/} is that it uses multiple learning
strategies, each of which exploits well a different
type of information either in the data instances or in
the taxonomic structure of the ontologies. To further
improve matching accuracy, we extend {\em GLUE\/} to
incorporate common-sense knowledge and domain
constraints into the matching process. Our approach is
thus distinguished in that it works with a variety of
well-defined similarity notions and that it efficiently
incorporates multiple types of knowledge. We describe a
set of experiments on several real-world domains and
show that {\em GLUE\/} proposes highly accurate
semantic mappings. Finally, we extend {\em GLUE\/} to
find complex mappings between ontologies and describe
experiments that show the promise of the approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "machine learning; ontology matching; relaxation
labeling; Semantic Web",
}
@Article{Halkidi:2003:TOW,
author = "Maria Halkidi and Benjamin Nguyen and Iraklis Varlamis
and Michalis Vazirgiannis",
title = "{THESUS}: Organizing {Web} document collections based
on link semantics",
journal = j-VLDB-J,
volume = "12",
number = "4",
pages = "320--332",
month = nov,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0100-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The requirements for effective search and management
of the WWW are stronger than ever. Currently Web
documents are classified based on their content not
taking into account the fact that these documents are
connected to each other by links. We claim that a
page's classification is enriched by the detection of
its incoming links' semantics. This would enable
effective browsing and enhance the validity of search
results in the WWW context. Another aspect that is
underaddressed and strictly related to the tasks of
browsing and searching is the similarity of documents
at the semantic level. The above observations lead us
to the adoption of a hierarchy of concepts (ontology)
and a thesaurus to exploit links and provide a better
characterization of Web documents. The enhancement of
document characterization makes operations such as
clustering and labeling very interesting. To this end,
we devised a system called THESUS. The system deals
with an initial sets of Web documents, extracts
keywords from all pages' incoming links, and converts
them to semantics by mapping them to a domain's
ontology. Then a clustering algorithm is applied to
discover groups of Web documents. The effectiveness of
the clustering process is based on the use of a novel
similarity measure between documents characterized by
sets of terms. Web documents are organized into
thematic subsets based on their semantics. The subsets
are then labeled, thereby enabling easier management
(browsing, searching, querying) of the Web. In this
article, we detail the process of this system and give
an experimental analysis of its results.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "document clustering; link analysis; link management;
semantics; similarity measure; World Wide Web",
}
@Article{Medjahed:2003:CWS,
author = "Brahim Medjahed and Athman Bouguettaya and Ahmed K.
Elmagarmid",
title = "Composing {Web} services on the {Semantic Web}",
journal = j-VLDB-J,
volume = "12",
number = "4",
pages = "333--351",
month = nov,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0101-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Service composition is gaining momentum as the
potential {\em silver bullet\/} for the envisioned {\em
Semantic Web}. It purports to take the Web to
unexplored efficiencies and provide a flexible approach
for promoting all types of activities in tomorrow's
Web. Applications expected to heavily take advantage of
Web service composition include B2B E-commerce and
E-government. To date, enabling composite services has
largely been an ad hoc, time-consuming, and error-prone
process involving repetitive low-level programming. In
this paper, we propose an {\em ontology\/}-based
framework for the automatic composition of Web
services. We present a technique to generate composite
services from high-level declarative descriptions. We
define formal safeguards for meaningful composition
through the use of {\em composability\/} rules. These
rules compare the {\em syntactic\/} and {\em
semantic\/} features of Web services to determine
whether two services are composable. We provide an
implementation using an E-government application
offering customized services to indigent citizens.
Finally, we present an exhaustive performance
experiment to assess the scalability of our approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "ontology; Semantic Web; service composition; Web
services",
}
@Article{Fileto:2003:POW,
author = "Renato Fileto and Ling Liu and Calton Pu and Eduardo
Delgado Assad and Claudia Bauzer Medeiros",
title = "{POESIA}: an ontological workflow approach for
composing {Web} services in agriculture",
journal = j-VLDB-J,
volume = "12",
number = "4",
pages = "352--367",
month = nov,
year = "2003",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0103-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper describes the POESIA approach to systematic
composition of Web services. This pragmatic approach is
strongly centered in the use of domain-specific
multidimensional ontologies. Inspired by applications
needs and founded on ontologies, workflows, and
activity models, POESIA provides well-defined
operations (aggregation, specialization, and
instantiation) to support the composition of Web
services. POESIA complements current proposals for Web
services definition and composition by providing a
higher degree of abstraction with verifiable
consistency properties. We illustrate the POESIA
approach using a concrete application scenario in
agroenvironmental planning.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "composition of Web services; data integration;
ontologies; Semantic Web; semantics of data and
processes",
}
@Article{Jensen:2004:MDM,
author = "Christian S. Jensen and Augustas Kligys and Torben
Bach Pedersen and Igor Timko",
title = "Multidimensional data modeling for location-based
services",
journal = j-VLDB-J,
volume = "13",
number = "1",
pages = "1--21",
month = jan,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0091-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:09 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "With the recent and continuing advances in areas such
as wireless communications and positioning
technologies, mobile, location-based services are
becoming possible. Such services deliver
location-dependent content to their users. More
specifically, these services may capture the movements
and requests of their users in multidimensional
databases, i.e., data warehouses, and content delivery
may be based on the results of complex queries on these
data warehouses. Such queries aggregate detailed data
in order to find useful patterns, e.g., in the
interaction of a particular user with the services. The
application of multidimensional technology in this
context poses a range of new challenges. The specific
challenge addressed here concerns the provision of an
appropriate multidimensional data model. In particular,
the paper extends an existing multidimensional data
model and algebraic query language to accommodate
spatial values that exhibit partial containment
relationships instead of the total containment
relationships normally assumed in multidimensional data
models. Partial containment introduces imprecision in
aggregation paths. The paper proposes a method for
evaluating the imprecision of such paths. The paper
also offers transformations of dimension hierarchies
with partial containment relationships to simple
hierarchies, to which existing precomputation
techniques are applicable.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data modeling; location-based services;
multidimensional data; partial containment",
}
@Article{Zhang:2004:PMV,
author = "Xin Zhang and Lingli Ding and Elke A. Rundensteiner",
title = "Parallel multisource view maintenance",
journal = j-VLDB-J,
volume = "13",
number = "1",
pages = "22--48",
month = jan,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0086-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:09 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In a distributed environment, materialized views are
used to integrate data from different information
sources and then store them in some centralized
location. In order to maintain such materialized views,
maintenance queries need to be sent to information
sources by the data warehouse management system. Due to
the independence of the information sources and the
data warehouse, concurrency issues are raised between
the maintenance queries and the local update
transactions at each information source. Recent
solutions such as ECA and Strobe tackle such concurrent
maintenance, however with the requirement of quiescence
of the information sources. SWEEP and POSSE overcome
this limitation by decomposing the global maintenance
query into smaller subqueries to be sent to every
information source and then performing conflict
correction locally at the data warehouse. Note that all
these previous approaches handle the data updates {\em
one at a time}. Hence either some of the information
sources or the data warehouse is likely to be idle
during most of the maintenance process. In this paper,
we propose that a set of updates should be maintained
in parallel by several concurrent maintenance processes
so that both the information sources as well as the
warehouse would be utilized more fully throughout the
maintenance process. This parallelism should then
improve the overall maintenance performance. For this
we have developed a parallel view maintenance
algorithm, called PVM, that substantially improves upon
the performance of previous maintenance approaches by
handling a set of data updates at the same time. The
parallel handling of a set of updates is orthogonal to
the particular maintenance algorithm applied to the
handling of each individual update. In order to perform
parallel view maintenance, we have identified two
critical issues that must be overcome: (1) detecting
maintenance-concurrent data updates in a parallel mode
and (2) correcting the problem that the data warehouse
commit order may not correspond to the data warehouse
update processing order due to parallel maintenance
handling. In this work, we provide solutions to both
issues. For the former, we insert a middle-layer
timestamp assignment module for detecting
maintenance-concurrent data updates without requiring
any global clock synchronization. For the latter, we
introduce the negative counter concept to solve the
problem of variant orders of committing effects of data
updates to the data warehouse. We provide a proof of
the correctness of PVM that guarantees that our
strategy indeed generates the correct final data
warehouse state. We have implemented both SWEEP and PVM
in our EVE data warehousing system. Our performance
study demonstrates that a manyfold performance
improvement is achieved by PVM over SWEEP.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "concurrent data updates; data warehousing; parallel
view maintenance; performance evaluation",
}
@Article{Hristidis:2004:AAA,
author = "Vagelis Hristidis and Yannis Papakonstantinou",
title = "Algorithms and applications for answering ranked
queries using ranked views",
journal = j-VLDB-J,
volume = "13",
number = "1",
pages = "49--70",
month = jan,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0099-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:09 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Ranked queries return the top objects of a database
according to a preference function. We present and
evaluate (experimentally and theoretically) a core
algorithm that answers ranked queries in an efficient
pipelined manner using materialized ranked views. We
use and extend the core algorithm in the described
PREFER and MERGE systems. PREFER precomputes a set of
materialized views that provide guaranteed query
performance. We present an algorithm that selects a
near optimal set of views under space constraints. We
also describe multiple optimizations and implementation
aspects of the downloadable version of PREFER. Then we
discuss MERGE, which operates at a metabroker and
answers ranked queries by retrieving a minimal number
of objects from sources that offer ranked queries. A
speculative version of the pipelining algorithm is
described.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "materialization; merge ranked views; ranked queries",
}
@Article{Khan:2004:REO,
author = "Latifur Khan and Dennis McLeod and Eduard Hovy",
title = "Retrieval effectiveness of an ontology-based model for
information selection",
journal = j-VLDB-J,
volume = "13",
number = "1",
pages = "71--85",
month = jan,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0105-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:09 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Technology in the field of digital media generates
huge amounts of nontextual information, audio, video,
and images, along with more familiar textual
information. The potential for exchange and retrieval
of information is vast and daunting. The key problem in
achieving efficient and user-friendly retrieval is the
development of a search mechanism to guarantee delivery
of minimal irrelevant information (high precision)
while insuring relevant information is not overlooked
(high recall). The traditional solution employs
keyword-based search. The only documents retrieved are
those containing user-specified keywords. But many
documents convey desired semantic information without
containing these keywords. This limitation is
frequently addressed through query expansion mechanisms
based on the statistical co-occurrence of terms. Recall
is increased, but at the expense of deteriorating
precision. One can overcome this problem by indexing
documents according to context and meaning rather than
keywords, although this requires a method of converting
words to meanings and the creation of a meaning-based
index structure. We have solved the problem of an index
structure through the design and implementation of a
concept-based model using domain-dependent ontologies.
An ontology is a collection of concepts and their
interrelationships that provide an abstract view of an
application domain. With regard to converting words to
meaning, the key issue is to identify appropriate
concepts that both describe and identify documents as
well as language employed in user requests. This paper
describes an automatic mechanism for selecting these
concepts. An important novelty is a scalable
disambiguation algorithm that prunes irrelevant
concepts and allows relevant ones to associate with
documents and participate in query generation. We also
propose an automatic query expansion mechanism that
deals with user requests expressed in natural language.
This mechanism generates database queries with
appropriate and relevant expansion through knowledge
encoded in ontology form. Focusing on audio data, we
have constructed a demonstration prototype. We have
experimentally and analytically shown that our model,
compared to keyword search, achieves a significantly
higher degree of precision and recall. The techniques
employed can be applied to the problem of information
selection in all media types.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "audio; metadata; ontology; precision; recall; SQL",
}
@Article{Donderler:2004:RBS,
author = "Mehmet Emin D{\"o}nderler and {\"O}zg{\"u}r Ulusoy and
Ugur G{\"u}d{\"u}kbay",
title = "Rule-based spatiotemporal query processing for video
databases",
journal = j-VLDB-J,
volume = "13",
number = "1",
pages = "86--103",
month = jan,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0114-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:09 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In our earlier work, we proposed an architecture for a
Web-based video database management system (VDBMS)
providing an integrated support for spatiotemporal and
semantic queries. In this paper, we focus on the task
of spatiotemporal query processing and also propose an
SQL-like video query language that has the capability
to handle a broad range of spatiotemporal queries. The
language is rule-based in that it allows users to
express spatial conditions in terms of Prolog-type
predicates. Spatiotemporal query processing is carried
out in three main stages: query recognition, query
decomposition, and query execution.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "content-based retrieval; inference rules; multimedia
databases; spatiotemporal query processing; video
databases",
}
@Article{Yu:2004:QHD,
author = "Cui Yu and St{\'e}phane Bressan and Beng Chin Ooi and
Kian-Lee Tan",
title = "Querying high-dimensional data in single-dimensional
space",
journal = j-VLDB-J,
volume = "13",
number = "2",
pages = "105--119",
month = may,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-004-0121-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:10 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we propose a new tunable index scheme,
called iMinMax($ \theta $), that maps points in
high-dimensional spaces to single-dimensional values
determined by their maximum or minimum values among all
dimensions. By varying the tuning ``knob'', $ \theta $,
we can obtain different families of iMinMax structures
that are optimized for different distributions of data
sets. The transformed data can then be indexed using
existing single-dimensional indexing structures such as
the B$^+$-trees. Queries in the high-dimensional space
have to be transformed into queries in the
single-dimensional space and evaluated there. We
present efficient algorithms for evaluating window
queries as range queries on the single-dimensional
space. We conducted an extensive performance study to
evaluate the effectiveness of the proposed schemes. Our
results show that iMinMax($ \theta $) outperforms
existing techniques, including the Pyramid scheme and
VA-file, by a wide margin. We then describe how iMinMax
could be used in approximate K-nearest neighbor (KNN)
search, and we present a comparative study against the
recently proposed iDistance, a specialized KNN indexing
method.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "edge; high-dimensional data; iMinMax($\theta$);
single-dimensional space; window and KNN queries",
}
@Article{Dori:2004:VVS,
author = "Dov Dori",
title = "{ViSWeb} --- the {Visual Semantic Web}: unifying human
and machine knowledge representations with
{Object-Process Methodology}",
journal = j-VLDB-J,
volume = "13",
number = "2",
pages = "120--147",
month = may,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-004-0120-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:10 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The Visual Semantic Web (ViSWeb) is a new paradigm for
enhancing the current Semantic Web technology. Based on
Object-Process Methodology (OPM), which enables
modeling of systems in a single graphic and textual
model, ViSWeb provides for representation of knowledge
over the Web in a unified way that caters to human
perceptions while also being machine processable. The
advantages of the ViSWeb approach include equivalent
graphic-text knowledge representation, visual
navigability, semantic sentence interpretation,
specification of system dynamics, and complexity
management. Arguing against the claim that humans and
machines need to look at different knowledge
representation formats, the principles and basics of
various graphic and textual knowledge representations
are presented and examined as candidates for ViSWeb
foundation. Since OPM is shown to be most adequate for
the task, ViSWeb is developed as an OPM-based layer on
top of XML/RDF/OWL to express knowledge visually and in
natural language. Both the graphic and the textual
representations are strictly equivalent. Being
intuitive yet formal, they are not only understandable
to humans but are also amenable to computer processing.
The ability to use such bimodal knowledge
representation is potentially a major step forward in
the evolution of the Semantic Web.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "conceptual graphs; knowledge representation;
object-process methodology; Semantic Web; Visual
Semantic Web",
}
@Article{Fu:2004:EHA,
author = "Lixin Fu and Sanguthevar Rajasekaran",
title = "Evaluating holistic aggregators efficiently for very
large datasets",
journal = j-VLDB-J,
volume = "13",
number = "2",
pages = "148--161",
month = may,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0112-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:10 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In data warehousing applications, numerous OLAP
queries involve the processing of holistic aggregators
such as computing the ``top $n$,'' median, quantiles,
etc. In this paper, we present a novel approach called
dynamic bucketing to efficiently evaluate these
aggregators. We partition data into equiwidth buckets
and further partition dense buckets into subbuckets as
needed by allocating and reclaiming memory space. The
bucketing process dynamically adapts to the input order
and distribution of input datasets. The histograms of
the buckets and subbuckets are stored in our new data
structure called structure trees. A recent selection
algorithm based on regular sampling is generalized and
its analysis extended. We have also compared our new
algorithms with this generalized algorithm and several
other recent algorithms. Experimental results show that
our new algorithms significantly outperform prior ones
not only in the runtime but also in accuracy.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "aggregation; dynamic bucketing; quantiles",
}
@Article{Rahal:2004:ETU,
author = "Amira Rahal and Qiang Zhu and Per-{\AA}ke Larson",
title = "Evolutionary techniques for updating query cost models
in a dynamic multidatabase environment",
journal = j-VLDB-J,
volume = "13",
number = "2",
pages = "162--176",
month = may,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0110-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:10 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Deriving local cost models for query optimization in a
dynamic multidatabase system (MDBS) is a challenging
issue. In this paper, we study how to evolve a query
cost model to capture a slowly-changing dynamic MDBS
environment so that the cost model is kept up-to-date
all the time. Two novel evolutionary techniques, i.e.,
the shifting method and the block-moving method, are
proposed. The former updates a cost model by taking
up-to-date information from a new sample query into
consideration at each step, while the latter considers
a block (batch) of new sample queries at each step. The
relevant issues, including derivation of recurrence
updating formulas, development of efficient algorithms,
analysis and comparison of complexities, and design of
an integrated scheme to apply the two methods
adaptively, are studied. Our theoretical and
experimental results demonstrate that the proposed
techniques are quite promising in maintaining accurate
cost models efficiently for a slowly changing dynamic
MDBS environment. Besides the application to MDBSs, the
proposed techniques can also be applied to the
automatic maintenance of cost models in self-managing
database systems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "cost model; evolutionary technique; multidatabase;
query optimization; self-managing database",
}
@Article{Adi:2004:ASM,
author = "Asaf Adi and Opher Etzion",
title = "{Amit} --- the situation manager",
journal = j-VLDB-J,
volume = "13",
number = "2",
pages = "177--203",
month = may,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-003-0108-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:10 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper presents the ``situation manager'', a tool
that includes both a language and an efficient runtime
execution mechanism aimed at reducing the complexity of
active applications. This tool follows the observation
that in many cases there is a gap between current tools
that enable one to react to a single event (following
the ECA: event-condition-action paradigm) and the
reality in which a single event may not require any
reaction; however, the reaction should be given to
patterns over the event history. The concept of
presented in this paper extends the concept of in its
expressive power, flexibility, and usability. This
paper motivates the work, surveys other efforts in this
area, and discusses both the language and the execution
model.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "active databases; active technology; composite events;
high-level languages",
}
@Article{Freytag:2004:BPV,
author = "Johann-Christoph Freytag and Serge Abiteboul and Mike
Carey",
title = "Best papers of {VLDB} 2003",
journal = j-VLDB-J,
volume = "13",
number = "3",
pages = "205--206",
month = sep,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-004-0129-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ilyas:2004:STJ,
author = "Ihab F. Ilyas and Walid G. Aref and Ahmed K.
Elmagarmid",
title = "Supporting top-$k$ join queries in relational
databases",
journal = j-VLDB-J,
volume = "13",
number = "3",
pages = "207--221",
month = sep,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-004-0128-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Ranking queries, also known as top-$k$ queries,
produce results that are ordered on some computed
score. Typically, these queries involve joins, where
users are usually interested only in the top-$k$ join
results. Top-$k$ queries are dominant in many emerging
applications, e.g., multimedia retrieval by content,
Web databases, data mining, middlewares, and most
information retrieval applications. Current relational
query processors do not handle ranking queries
efficiently, especially when joins are involved. In
this paper, we address supporting top-$k$ join queries
in relational query processors. We introduce a new
rank-join algorithm that makes use of the individual
orders of its inputs to produce join results ordered on
a user-specified scoring function. The idea is to rank
the join results progressively during the join
operation. We introduce two physical query operators
based on variants of ripple join that implement the
rank-join algorithm. The operators are nonblocking and
can be integrated into pipelined execution plans. We
also propose an efficient heuristic designed to
optimize a top-$k$ join query by choosing the best join
order. We address several practical issues and
optimization heuristics to integrate the new join
operators in practical query processors. We implement
the new operators inside a prototype database engine
based on PREDATOR. The experimental evaluation of our
approach compares recent algorithms for joining ranked
inputs and shows superior performance.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "query operators; rank aggregarion; ranking; top-$k$
queries",
}
@Article{Papadimitriou:2004:AUS,
author = "Spiros Papadimitriou and Anthony Brockwell and
Christos Faloutsos",
title = "Adaptive, unsupervised stream mining",
journal = j-VLDB-J,
volume = "13",
number = "3",
pages = "222--239",
month = sep,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-004-0130-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Sensor devices and embedded processors are becoming
widespread, especially in measurement/monitoring
applications. Their limited resources (CPU, memory
and/or communication bandwidth, and power) pose some
interesting challenges. We need concise, expressive
models to represent the important features of the data
and that lend themselves to efficient estimation. In
particular, under these severe constraints, we want
models and estimation methods that (a) require little
memory and a single pass over the data, (b) can adapt
and handle arbitrary periodic components, and (c) can
deal with various types of noise. We propose $ {\mathrm
{AWSOM}} $ (Arbitrary Window Stream mOdeling Method),
which allows sensors in remote or hostile environments
to efficiently and effectively discover interesting
patterns and trends. This can be done automatically,
i.e., with no prior inspection of the data or any user
intervention and expert tuning before or during data
gathering. Our algorithms require limited resources and
can thus be incorporated into sensors --- possibly
alongside a distributed query processing engine
[10,6,27]. Updates are performed in constant time with
respect to stream size using logarithmic space.
Existing forecasting methods (SARIMA, GARCH, etc.) and
``traditional'' Fourier and wavelet analysis fall short
on one or more of these requirements. To the best of
our knowledge, $ {\mathrm {AWSOM}} $ is the first
framework that combines all of the above
characteristics. Experiments on real and synthetic
datasets demonstrate that $ {\mathrm {AWSOM}} $
discovers meaningful patterns over long time periods.
Thus, the patterns can also be used to make long-range
forecasts, which are notoriously difficult to perform.
In fact, $ {\mathrm {AWSOM}} $ outperforms manually set
up autoregressive models, both in terms of long-term
pattern detection and modeling and by at least $ 10
\times $ in resource consumption.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Labrinidis:2004:ETB,
author = "Alexandros Labrinidis and Nick Roussopoulos",
title = "Exploring the tradeoff between performance and data
freshness in database-driven {Web} servers",
journal = j-VLDB-J,
volume = "13",
number = "3",
pages = "240--255",
month = sep,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-004-0131-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Personalization, advertising, and the sheer volume of
online data generate a staggering amount of dynamic Web
content. In addition to Web caching, view
materialization has been shown to accelerate the
generation of dynamic Web content. View materialization
is an attractive solution as it decouples the serving
of access requests from the handling of updates. In the
context of the Web, selecting which views to
materialize must be decided online and needs to
consider both performance and data freshness, which we
refer to as the online view selection problem. In this
paper, we define data freshness metrics, provide an
adaptive algorithm for the online view selection
problem that is based on user-specified data freshness
requirements, and present experimental results.
Furthermore, we examine alternative metrics for data
freshness and extend our proposed algorithm to handle
multiple users and alternative definitions of data
freshness.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{He:2004:AIW,
author = "Hai He and Weiyi Meng and Clement Yu and Zonghuan Wu",
title = "Automatic integration of {Web} search interfaces with
{WISE}-Integrator",
journal = j-VLDB-J,
volume = "13",
number = "3",
pages = "256--273",
month = sep,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-004-0126-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "An increasing number of databases are becoming Web
accessible through form-based search interfaces, and
many of these sources are database-driven e-commerce
sites. It is a daunting task for users to access
numerous Web sites individually to get the desired
information. Hence, providing a unified access to
multiple e-commerce search engines selling similar
products is of great importance in allowing users to
search and compare products from multiple sites with
ease. One key task for providing such a capability is
to integrate the Web search interfaces of these
e-commerce search engines so that user queries can be
submitted against the integrated interface. Currently,
integrating such search interfaces is carried out
either manually or semiautomatically, which is
inefficient and difficult to maintain. In this paper,
we present WISE-Integrator --- a tool that performs
automatic integration of Web Interfaces of Search
Engines. WISE-Integrator explores a rich set of special
metainformation that exists in Web search interfaces
and uses the information to identify matching
attributes from different search interfaces for
integration. It also resolves domain differences of
matching attributes. In this paper, we also discuss how
to automatically extract information from search
interfaces that is needed by WISE-Integrator to perform
automatic interface integration. Our experimental
results, based on 143 real-world search interfaces in
four different domains, indicate that WISE-Integrator
can achieve high attribute matching accuracy and can
produce high-quality integrated search interfaces
without human interactions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "attribute matching; interface extraction; metasearch;
schema integration; Web search interface integration",
}
@Article{Velegrakis:2004:PMC,
author = "Yannis Velegrakis and Ren{\'e} J. Miller and Lucian
Popa",
title = "Preserving mapping consistency under schema changes",
journal = j-VLDB-J,
volume = "13",
number = "3",
pages = "274--293",
month = sep,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-004-0136-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In dynamic environments like the Web, data sources may
change not only their data but also their schemas,
their semantics, and their query capabilities. When a
mapping is left inconsistent by a schema change, it has
to be detected and updated. We present a novel
framework and a tool (ToMAS) for automatically adapting
(rewriting) mappings as schemas evolve. Our approach
considers not only local changes to a schema but also
changes that may affect and transform many components
of a schema. Our algorithm detects mappings affected by
structural or constraint changes and generates all the
rewritings that are consistent with the semantics of
the changed schemas. Our approach explicitly models
mapping choices made by a user and maintains these
choices, whenever possible, as the schemas and mappings
evolve. When there is more than one candidate
rewriting, the algorithm may rank them based on how
close they are to the semantics of the existing
mappings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Florescu:2004:BSX,
author = "Daniela Florescu and Chris Hillery and Donald Kossmann
and Paul Lucas and Fabio Riccardi and Till Westmann and
J. Carey and Arvind Sundararajan",
title = "The {BEA} streaming {XQuery} processor",
journal = j-VLDB-J,
volume = "13",
number = "3",
pages = "294--315",
month = sep,
year = "2004",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-004-0137-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper describes the design, implementation, and
performance characteristics of a commercial XQuery
processing engine, the BEA streaming XQuery processor.
This XQuery engine was designed to provide high
performance for message-processing applications, i.e.,
for transforming XML data streams. The engine is a
central component of the 8.1 release of BEA's WebLogic
Integration (WLI) product. The BEA XQuery engine is
fully compliant with the August 2002 draft of the W3C
XML Query Language specification and we are currently
porting it to the latest version of the XQuery language
(July 2004). A goal of this paper is to describe how a
fully compliant yet efficient XQuery engine has been
built from a few relatively simple components and
well-understood technologies.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gehrke:2004:GES,
author = "Johannes Gehrke and M. Hellerstein",
title = "{Guest Editorial} to the special issue on data stream
processing",
journal = j-VLDB-J,
volume = "13",
number = "4",
pages = "317--317",
month = dec,
year = "2004",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:12 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yang:2004:FHQ,
author = "Huai Yang and Li Lee and Wynne Hsu",
title = "Finding hot query patterns over an {XQuery} stream",
journal = j-VLDB-J,
volume = "13",
number = "4",
pages = "318--332",
month = dec,
year = "2004",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:12 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Caching query results is one efficient approach to
improving the performance of XML management systems.
This entails the discovery of frequent XML queries
issued by users. In this paper, we model user queries
as a stream of XML query pattern trees and mine the
frequent query patterns over the query stream. To
facilitate the one-pass mining process, we devise a
novel data structure called DTS to summarize the
pattern trees seen so far. By grouping the incoming
pattern trees into batches, we can dynamically mark the
active portion of the current batch in DTS and limit
the enumeration of candidate trees to only the
currently active pattern trees. We also design another
summary data structure called ECTree that provides for
the incremental computation of the frequent tree
patterns over the query stream. Based on the above two
constructs, we present two mining algorithms called
XQSMinerI and XQSMinerII. XQSMinerI is fast, but it
tends to overestimate, while XQSMinerII adopts a
filter-and-refine approach to minimize the amount of
overestimation. Experimental results show that the
proposed methods are both efficient and scalable and
require only small memory footprints.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "frequent pattern mining; pattern tree; stream mining;
tree mining; XML query pattern",
}
@Article{Babcock:2004:OSD,
author = "Brian Babcock and Shivnath Babu and Mayur Datar and
Rajeev Motwani and Dilys Thomas",
title = "Operator scheduling in data stream systems",
journal = j-VLDB-J,
volume = "13",
number = "4",
pages = "333--353",
month = dec,
year = "2004",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:12 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In many applications involving continuous data
streams, data arrival is bursty and data rate
fluctuates over time. Systems that seek to give rapid
or real-time query responses in such an environment
must be prepared to deal gracefully with bursts in data
arrival without compromising system performance. We
discuss one strategy for processing bursty streams ---
adaptive, load-aware scheduling of query operators to
minimize resource consumption during times of peak
load. We show that the choice of an operator scheduling
strategy can have significant impact on the runtime
system memory usage as well as output latency. Our aim
is to design a scheduling strategy that minimizes the
maximum runtime system memory while maintaining the
output latency within prespecified bounds. We first
present Chain scheduling, an operator scheduling
strategy for data stream systems that is near-optimal
in minimizing runtime memory usage for any collection
of single-stream queries involving selections,
projections, and foreign-key joins with stored
relations. Chain scheduling also performs well for
queries with sliding-window joins over multiple streams
and multiple queries of the above types. However,
during bursts in input streams, when there is a buildup
of unprocessed tuples, Chain scheduling may lead to
high output latency. We study the online problem of
minimizing maximum runtime memory, subject to a
constraint on maximum latency. We present preliminary
observations, negative results, and heuristics for this
problem. A thorough experimental evaluation is provided
where we demonstrate the potential benefits of Chain
scheduling and its different variants, compare it with
competing scheduling strategies, and validate our
analytical conclusions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data streams; latency; memory management; scheduling",
}
@Article{Ganguly:2004:TSE,
author = "Sumit Ganguly and Minos Garofalakis and Rajeev
Rastogi",
title = "Tracking set-expression cardinalities over continuous
update streams",
journal = j-VLDB-J,
volume = "13",
number = "4",
pages = "354--369",
month = dec,
year = "2004",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:12 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "There is growing interest in algorithms for processing
and querying continuous data streams (i.e., data seen
only once in a fixed order) with limited memory
resources. In its most general form, a data stream is
actually an update stream, i.e., comprising data-item
deletions as well as insertions. Such massive update
streams arise naturally in several application domains
(e.g., monitoring of large IP network installations or
processing of retail-chain transactions). Estimating
the cardinality of set expressions defined over several
(possibly distributed) update streams is perhaps one of
the most fundamental query classes of interest; as an
example, such a query may ask ``what is the number of
distinct IP source addresses seen in passing packets
from both router $ R_1 $ and $ R_2 $ but not router $
R_3 $ ?''. Earlier work only addressed very restricted
forms of this problem, focusing solely on the special
case of insert-only streams and specific operators
(e.g., union). In this paper, we propose the first
space-efficient algorithmic solution for estimating the
cardinality of full-fledged set expressions over
general update streams. Our estimation algorithms are
probabilistic in nature and rely on a novel, hash-based
synopsis data structure, termed ''2-level hash
sketch''. We demonstrate how our 2-level hash sketch
synopses can be used to provide low-error,
high-confidence estimates for the cardinality of set
expressions (including operators such as set union,
intersection, and difference) over continuous update
streams, using only space that is significantly
sublinear in the sizes of the streaming input
(multi-)sets. Furthermore, our estimators never require
rescanning or resampling of past stream items,
regardless of the number of deletions in the stream. We
also present lower bounds for the problem,
demonstrating that the space usage of our estimation
algorithms is within small factors of the optimal.
Finally, we propose an optimized, time-efficient stream
synopsis (based on 2-level hash sketches) that provides
similar, strong accuracy-space guarantees while
requiring only guaranteed logarithmic maintenance time
per update, thus making our methods applicable for
truly rapid-rate data streams. Our results from an
empirical study of our synopsis and estimation
techniques verify the effectiveness of our approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "approximate query processing; data streams; data
synopses; randomized algorithms; set expressions",
}
@Article{Balakrishnan:2004:RA,
author = "Hari Balakrishnan and Magdalena Balazinska and Don
Carney and U{\=g}ur {\c{C}}etintemel and Mitch
Cherniack and Christian Convey and Eddie Galvez and Jon
Salz and Michael Stonebraker and Nesime Tatbul and
Richard Tibbetts and Stan Zdonik",
title = "Retrospective on {Aurora}",
journal = j-VLDB-J,
volume = "13",
number = "4",
pages = "370--383",
month = dec,
year = "2004",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:12 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This experience paper summarizes the key lessons we
learned throughout the design and implementation of the
Aurora stream-processing engine. For the past 2 years,
we have built five stream-based applications using
Aurora. We first describe in detail these applications
and their implementation in Aurora. We then reflect on
the design of Aurora based on this experience. Finally,
we discuss our initial ideas on a follow-on project,
called Borealis, whose goal is to eliminate the
limitations of Aurora as well as to address new key
challenges and applications in the stream-processing
domain.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data stream management; distributed stream processing;
monitoring applications; quality-of-service;
stream-processing engines",
}
@Article{Sharaf:2004:BEE,
author = "A. Sharaf and Jonathan Beaver and Alexandros
Labrinidis and K. Chrysanthis",
title = "Balancing energy efficiency and quality of aggregate
data in sensor networks",
journal = j-VLDB-J,
volume = "13",
number = "4",
pages = "384--403",
month = dec,
year = "2004",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:12 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In-network aggregation has been proposed as one method
for reducing energy consumption in sensor networks. In
this paper, we explore two ideas related to further
reducing energy consumption in the context of
in-network aggregation. The first is by influencing the
construction of the routing trees for sensor networks
with the goal of reducing the size of transmitted data.
To this end, we propose a group-aware network
configuration method that ``clusters'' along the same
path sensor nodes that belong to the same group. The
second idea involves imposing a hierarchy of output
filters on the sensor network with the goal of both
reducing the size of transmitted data and minimizing
the number of transmitted messages. More specifically,
we propose a framework to use temporal coherency
tolerances in conjunction with in-network aggregation
to save energy at the sensor nodes while maintaining
specified quality of data. These tolerances are based
on user preferences or can be dictated by the network
in cases where the network cannot support the current
tolerance level. Our framework, called TiNA, works on
top of existing in-network aggregation schemes. We
evaluate experimentally our proposed schemes in the
context of existing in-network aggregation schemes. We
present experimental results measuring energy
consumption, response time, and quality of data for
Group-By queries. Overall, our schemes provide
significant energy savings with respect to
communication and a negligible drop in quality of
data.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "in-network query processing; power-aware computing;
semantic routing; sensor networks",
}
@Article{Ozsu:2005:E,
author = "Tamer {\"O}zsu",
title = "Editorial",
journal = j-VLDB-J,
volume = "14",
number = "1",
pages = "1--1",
month = mar,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:14 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gao:2005:JOT,
author = "Dengfeng Gao and S. Jensen and T. Snodgrass and D.
Soo",
title = "Join operations in temporal databases",
journal = j-VLDB-J,
volume = "14",
number = "1",
pages = "2--29",
month = mar,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:14 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Joins are arguably the most important relational
operators. Poor implementations are tantamount to
computing the Cartesian product of the input relations.
In a temporal database, the problem is more acute for
two reasons. First, conventional techniques are
designed for the evaluation of joins with equality
predicates rather than the inequality predicates
prevalent in valid-time queries. Second, the presence
of temporally varying data dramatically increases the
size of a database. These factors indicate that
specialized techniques are needed to efficiently
evaluate temporal joins. We address this need for
efficient join evaluation in temporal databases. Our
purpose is twofold. We first survey all previously
proposed temporal join operators. While many temporal
join operators have been defined in previous work, this
work has been done largely in isolation from competing
proposals, with little, if any, comparison of the
various operators. We then address evaluation
algorithms, comparing the applicability of various
algorithms to the temporal join operators and
describing a performance study involving algorithms for
one important operator, the temporal equijoin. Our
focus, with respect to implementation, is on
non-index-based join algorithms. Such algorithms do not
rely on auxiliary access paths but may exploit sort
orderings to achieve efficiency.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "attribute skew; interval join; partition join;
sort-merge join; temporal Cartesian product; temporal
join; timestamp skew",
}
@Article{Balmin:2005:SQX,
author = "Andrey Balmin and Yannis Papakonstantinou",
title = "Storing and querying {XML} data using denormalized
relational databases",
journal = j-VLDB-J,
volume = "14",
number = "1",
pages = "30--49",
month = mar,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:14 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "XML database systems emerge as a result of the
acceptance of the XML data model. Recent works have
followed the promising approach of building XML
database management systems on underlying RDBMS's.
Achieving query processing performance reduces to two
questions: (i) How should the XML data be decomposed
into data that are stored in the RDBMS? (ii) How should
the XML query be translated into an efficient plan that
sends one or more SQL queries to the underlying RDBMS
and combines the data into the XML result? We provide a
formal framework for XML Schema-driven decompositions,
which encompasses the decompositions proposed in prior
work and extends them with decompositions that employ
denormalized tables and binary-coded XML fragments. We
provide corresponding query processing algorithms that
translate the XML query conditions into conditions on
the relational tables and assemble the decomposed data
into the XML query result. Our key performance focus is
the response time for delivering the first results of a
query. The most effective of the described
decompositions have been implemented in XCacheDB, an
XML DBMS built on top of a commercial RDBMS, which
serves as our experimental basis. We present
experiments and analysis that point to a class of
decompositions, called inlined decompositions, that
improve query performance for full results and first
results, without significant increase in the size of
the database.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gal:2005:FME,
author = "Avigdor Gal and Ateret Anaby-Tavor and Alberto
Trombetta and Danilo Montesi",
title = "A framework for modeling and evaluating automatic
semantic reconciliation",
journal = j-VLDB-J,
volume = "14",
number = "1",
pages = "50--67",
month = mar,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:14 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The introduction of the Semantic Web vision and the
shift toward machine understandable Web resources has
unearthed the importance of automatic semantic
reconciliation. Consequently, new tools for automating
the process were proposed. In this work we present a
formal model of semantic reconciliation and analyze in
a systematic manner the properties of the process
outcome, primarily the inherent uncertainty of the
matching process and how it reflects on the resulting
mappings. An important feature of this research is the
identification and analysis of factors that impact the
effectiveness of algorithms for automatic semantic
reconciliation, leading, it is hoped, to the design of
better algorithms by reducing the uncertainty of
existing algorithms. Against this background we
empirically study the aptitude of two algorithms to
correctly match concepts. This research is both timely
and practical in light of recent attempts to develop
and utilize methods for automatic semantic
reconciliation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "mapping; ontology versioning; semantic
interoperability",
}
@Article{Halevy:2005:SML,
author = "Y. Halevy and G. Ives and Dan Suciu and Igor
Tatarinov",
title = "Schema mediation for large-scale semantic data
sharing",
journal = j-VLDB-J,
volume = "14",
number = "1",
pages = "68--83",
month = mar,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:14 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Intuitively, data management and data integration
tools should be well suited for exchanging information
in a semantically meaningful way. Unfortunately, they
suffer from two significant problems: they typically
require a common and comprehensive schema design before
they can be used to store or share information, and
they are difficult to extend because schema evolution
is heavyweight and may break backward compatibility. As
a result, many large-scale data sharing tasks are more
easily facilitated by non-database-oriented tools that
have little support for semantics. The goal of the peer
data management system (PDMS) is to address this need:
we propose the use of a decentralized, easily
extensible data management architecture in which any
user can contribute new data, schema information, or
even mappings between other peers' schemas. PDMSs
represent a natural step beyond data integration
systems, replacing their single logical schema with an
interlinked collection of semantic mappings between
peers' individual schemas. This paper considers the
problem of schema mediation in a PDMS. Our first
contribution is a flexible language for mediating
between peer schemas that extends known data
integration formalisms to our more complex
architecture. We precisely characterize the complexity
of query answering for our language. Next, we describe
a reformulation algorithm for our language that
generalizes both global-as-view and local-as-view query
answering algorithms. Then we describe several methods
for optimizing the reformulation algorithm and an
initial set of experiments studying its performance.
Finally, we define and consider several {\em global\/}
problems in managing semantic mappings in a PDMS.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data integration; peer data management; schema
mediation; Web and databases",
}
@Article{Benatallah:2005:AWS,
author = "Boualem Benatallah and Mohand-Said Hacid and Alain
Leger and Christophe Rey and Farouk Toumani",
title = "On automating {Web} services discovery",
journal = j-VLDB-J,
volume = "14",
number = "1",
pages = "84--96",
month = mar,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:14 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "One of the challenging problems that Web service
technology faces is the ability to effectively discover
services based on their capabilities. We present an
approach to tackling this problem in the context of
description logics (DLs). We formalize service
discovery as a new instance of the problem of rewriting
concepts using terminologies. We call this new instance
the {\em best covering problem}. We provide a
formalization of the {\em best covering problem\/} in
the framework of DL-based ontologies and propose a
hypergraph-based algorithm to effectively compute best
covers of a given request. We propose a novel
matchmaking algorithm that takes as input a service
request (or query) $Q$ and an ontology $ \mathcal {T}$
of services and finds a set of services called a ``best
cover'' of $Q$ whose descriptions contain as much {\em
common information\/} with $Q$ as possible and as
little {\em extra information\/} with respect to $Q$ as
possible. We have implemented the proposed discovery
technique and used the developed prototype in the
context of the {\em Multilingual Knowledge Based
European Electronic Marketplace\/} (MKBEEM) project.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "description logics; discovery; hypergraphs; semantic
matchmaking; Web services",
}
@Article{Sattler:2005:CBQ,
author = "Kai-Uwe Sattler and Ingolf Geist and Eike Schallehn",
title = "Concept-based querying in mediator systems",
journal = j-VLDB-J,
volume = "14",
number = "1",
pages = "97--111",
month = mar,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:14 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "One approach to overcoming heterogeneity as a part of
data integration in mediator systems is the use of
metadata in the form of a vocabulary or ontology to
represent domain knowledge explicitly. This requires
including this meta level during query formulation and
processing. In this paper, we address this problem in
the context of a mediator that uses a concept-based
integration model and an extension of the XQuery
language called CQuery. This mediator has been
developed as part of a project for integrating data
about cultural assets. We describe the language
extensions and their semantics as well as the rewriting
and evaluation steps. Furthermore, we discuss aspects
of caching and keyword-based search in support of an
efficient query formulation and processing.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data integration; mediator systems; query processing",
}
@Article{Tzitzikas:2005:MTB,
author = "Yannis Tzitzikas and Nicolas Spyratos and Panos
Constantopoulos",
title = "Mediators over taxonomy-based information sources",
journal = j-VLDB-J,
volume = "14",
number = "1",
pages = "112--136",
month = mar,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:14 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We propose a mediator model for providing integrated
and unified access to multiple taxonomy-based sources.
Each source comprises a taxonomy and a database that
indexes objects under the terms of the taxonomy. A
mediator comprises a taxonomy and a set of relations
between the mediator's and the sources' terms, called
articulations. By combining different modes of query
evaluation at the sources and the mediator and
different types of query translation, a flexible,
efficient scheme of mediator operation is obtained that
can accommodate various application needs and levels of
answer quality. We adopt a simple conceptual modeling
approach (taxonomies and intertaxonomy mappings) and we
illustrate its advantages in terms of ease of use,
uniformity, scalability, and efficiency. These
characteristics make this proposal appropriate for a
large-scale network of sources and mediators.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "approximate query translation; information
integration; mediators; taxonomies",
}
@Article{Gunopulos:2005:SEM,
author = "Dimitrios Gunopulos and George Kollios and J. Tsotras
and Carlotta Domeniconi",
title = "Selectivity estimators for multidimensional range
queries over real attributes",
journal = j-VLDB-J,
volume = "14",
number = "2",
pages = "137--154",
month = apr,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:15 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Estimating the selectivity of multidimensional range
queries over real valued attributes has significant
applications in data exploration and database query
optimization. In this paper, we consider the following
problem: given a table of $d$ attributes whose domain
is the real numbers and a query that specifies a range
in each dimension, find a good approximation of the
number of records in the table that satisfy the query.
The simplest approach to tackle this problem is to
assume that the attributes are independent. More
accurate estimators try to capture the joint data
distribution of the attributes. In databases, such
estimators include the construction of multidimensional
histograms, random sampling, or the wavelet transform.
In statistics, kernel estimation techniques are being
used. Many traditional approaches assume that attribute
values come from discrete, finite domains, where
different values have high frequencies. However, for
many novel applications (as in temporal, spatial, and
multimedia databases) attribute values come from the
infinite domain of real numbers. Consequently, each
value appears very infrequently, a characteristic that
affects the behavior and effectiveness of the
estimator. Moreover, real-life data exhibit attribute
correlations that also affect the estimator. We present
a new histogram technique that is designed to
approximate the density of multidimensional datasets
with real attributes. Our technique defines buckets of
variable size and allows the buckets to overlap. The
size of the cells is based on the local density of the
data. The use of overlapping buckets allows a more
compact approximation of the data distribution. We also
show how to generalize kernel density estimators and
how to apply them to the multidimensional query
approximation problem. Finally, we compare the accuracy
of the proposed techniques with existing techniques
using real and synthetic datasets. The experimental
results show that the proposed techniques behave more
accurately in high dimensionalities than previous
approaches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Alhajj:2005:VFC,
author = "Reda Alhajj and Faruk Polat and Cem Y{\'\i}lmaz",
title = "Views as first-class citizens in object-oriented
databases",
journal = j-VLDB-J,
volume = "14",
number = "2",
pages = "155--169",
month = apr,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:15 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Extensibility and dynamic schema evolution are among
the attractive features that lead to the wide
acceptance of the object-oriented paradigm. Not knowing
all class hierarchy details should not prevent a user
from introducing new classes when necessary. Naive or
professional users may define new classes either by
using class definition constructs or as views. However,
improper placement of such classes leads to a flat
hierarchy with many things duplicated. To overcome this
problem, we automated the process in order to help the
user find the most appropriate position with respect to
her class in the hierarchy regardless of her knowledge
of the hierarchy. The system must be responsible for
the proper placement of new classes because only the
system has complete knowledge of the details of the
class hierarchy, especially in a dynamic environment
where changes are very frequent. In other published
work, we proved that to define a view it is enough to
have the set of objects that qualify to be in a view in
addition to having message expressions (possible paths)
that lead to desired values within those objects. Here,
we go further to map a view that is intended to be
persistent into a class. Then we investigate the proper
position of that class in the hierarchy. To achieve
this, we consider current characteristics of a new
class in order to derive its relationship with other
existing classes in the hierarchy. Another advantage of
the presented model is that views that generate new
objects are still updatable simply because we based the
creation of new objects on existing identities. In
other words, an object participates inside view objects
by its identity regardless of which particular values
from that object are of interest to the view. Values
are reachable via message expressions, not violating
encapsulation. This way, actual values are present in
only one place and can be updated.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "class hierarchy; object-oriented databases;
reusability; schema evolution; views",
}
@Article{Zhang:2005:OSM,
author = "Donghui Zhang and J. Tsotras",
title = "Optimizing spatial {Min\slash Max} aggregations",
journal = j-VLDB-J,
volume = "14",
number = "2",
pages = "170--181",
month = apr,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:15 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Aggregate computation over a collection of spatial
objects appears in many real-life applications.
Aggregates are computed on values (weights) associated
with spatial objects, for example, the temperature or
rainfall over the area covered by the object. In this
paper we concentrate on MIN/MAX aggregations: ``given a
query rectangle, find the minimum/maximum weight among
all objects intersecting the query rectangle.''
Traditionally such queries have been performed as range
searches. Assuming that objects are indexed by a
spatial access method (SAM), the MIN/MAX is computed
while retrieving those objects intersecting the query
interval. This requires effort proportional to the
number of objects satisfying the query, which may be
large. A better approach is to maintain aggregate
information among the index nodes of the spatial access
method; then various index paths can be eliminated
during the range search. Yet another approach is to
build a specialized index that maintains the aggregate
incrementally. We propose four novel optimizations for
improving the performance of MIN/MAX queries when an
index structure (traditional or specialized) is
present. Moreover, we introduce the MR-tree, an
R-tree-like dynamic specialized index that incorporates
all four optimizations. Our experiments show that the
MR-tree offers drastic performance improvement over
previous solutions. As a byproduct of this work we
present an optimized version of the MSB-tree, an index
that has been proposed for the MIN/MAX computation over
1D interval objects.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "indexing; MIN/MAX; spatial aggregates",
}
@Article{Perich:2005:CJP,
author = "Filip Perich and Anupam Joshi and Yelena Yesha and Tim
Finin",
title = "Collaborative joins in a pervasive computing
environment",
journal = j-VLDB-J,
volume = "14",
number = "2",
pages = "182--196",
month = apr,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:15 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We present a collaborative query processing protocol
based on the principles of Contract Nets. The protocol
is designed for pervasive computing environments where,
in addition to operating on limited computing and
battery resources, mobile devices cannot always rely on
being able to access the wired infrastructure. Devices,
therefore, need to collaborate with each other in order
to obtain data otherwise inaccessible due to the nature
of the environment. Furthermore, by intelligently using
answers cached by peers, devices can reduce their
computation cost. We show the effectiveness of our
approach by evaluating performance of devices querying
for data while moving in a citylike environment.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "distributed join processing; mobile ad hoc networks;
peer-to-peer computing; pervasive computing
environments; query processing",
}
@Article{Josifovski:2005:QXS,
author = "Vanja Josifovski and Marcus Fontoura and Attila
Barta",
title = "Querying {XML} streams",
journal = j-VLDB-J,
volume = "14",
number = "2",
pages = "197--210",
month = apr,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:15 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Efficient querying of XML streams will be one of the
fundamental features of next-generation information
systems. In this paper we propose the TurboXPath path
processor, which accepts a language equivalent to a
subset of the for-let-where constructs of XQuery over a
single document. TurboXPath can be extended to provide
full XQuery support or used to augment federated
database engines for efficient handling of queries over
XML data streams produced by external sources.
Internally, TurboXPath uses a tree-shaped path
expression with multiple outputs to drive the
execution. The result of a query execution is a
sequence of tuples of XML fragments matching the output
nodes. Based on a streamed execution model, TurboXPath
scales up to large documents and has limited memory
consumption for increased concurrency. Experimental
evaluation of a prototype demonstrates performance
gains compared to other state-of-the-art path
processors.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Aggarwal:2005:EEA,
author = "C. Aggarwal and S. Yu",
title = "An effective and efficient algorithm for
high-dimensional outlier detection",
journal = j-VLDB-J,
volume = "14",
number = "2",
pages = "211--221",
month = apr,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:15 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The outlier detection problem has important
applications in the field of fraud detection, network
robustness analysis, and intrusion detection. Most such
applications are most important for high-dimensional
domains in which the data can contain hundreds of
dimensions. Many recent algorithms have been proposed
for outlier detection that use several concepts of
proximity in order to find the outliers based on their
relationship to the other points in the data. However,
in high-dimensional space, the data are sparse and
concepts using the notion of proximity fail to retain
their effectiveness. In fact, the sparsity of
high-dimensional data can be understood in a different
way so as to imply that every point is an equally good
outlier from the perspective of distance-based
definitions. Consequently, for high-dimensional data,
the notion of finding meaningful outliers becomes
substantially more complex and nonobvious. In this
paper, we discuss new techniques for outlier detection
that find the outliers by studying the behavior of
projections from the data set.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data mining; high-dimensional spaces; outlier
detection",
}
@Article{Yao:2005:HBL,
author = "D. Yao and Cyrus Shahabi and Per-{\AA}ke Larson",
title = "Hash-based labeling techniques for storage scaling",
journal = j-VLDB-J,
volume = "14",
number = "2",
pages = "222--237",
month = apr,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:15 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Scalable storage architectures allow for the addition
or removal of storage devices to increase storage
capacity and bandwidth or retire older devices.
Assuming random placement of data objects across
multiple storage devices of a storage pool, our
optimization objective is to redistribute a minimum
number of objects after scaling the pool. In addition,
a uniform distribution, and hence a balanced load,
should be ensured after redistribution. Moreover, the
redistributed objects should be retrieved efficiently
during the normal mode of operation: in one I/O access
and with low complexity computation. To achieve this,
we propose an algorithm called random disk labeling
(RDL), based on double hashing, where storage can be
added or removed without any increase in complexity. We
compare RDL with other proposed techniques and
demonstrate its effectiveness through
experimentation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "load balancing; random data placement; scalable
storage systems",
}
@Article{Kollios:2005:IMO,
author = "George Kollios and Dimitris Papadopoulos and Dimitrios
Gunopulos and J. Tsotras",
title = "Indexing mobile objects using dual transformations",
journal = j-VLDB-J,
volume = "14",
number = "2",
pages = "238--256",
month = apr,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:15 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "With the recent advances in wireless networks,
embedded systems, and GPS technology, databases that
manage the location of moving objects have received
increased interest. In this paper, we present indexing
techniques for moving object databases. In particular,
we propose methods to index moving objects in order to
efficiently answer range queries about their current
and future positions. This problem appears in real-life
applications such as predicting future congestion areas
in a highway system or allocating more bandwidth for
areas where a high concentration of mobile phones is
imminent. We address the problem in external memory and
present dynamic solutions, both for the one-dimensional
and the two-dimensional cases. Our approach transforms
the problem into a dual space that is easier to index.
Important in this dynamic environment is not only query
performance but also the update processing, given the
large number of moving objects that issue updates. We
compare the dual-transformation approach with the
TPR-tree, an efficient method for indexing moving
objects that is based on time-parameterized index
nodes. An experimental evaluation shows that the
dual-transformation approach provides comparable query
performance but has much faster update processing.
Moreover, the dual method does not require establishing
a predefined query horizon.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access methods; mobile objects; spatiotemporal
databases",
}
@Article{Jaluta:2005:CCR,
author = "Ibrahim Jaluta and Seppo Sippu and Eljas
Soisalon-Soininen",
title = "Concurrency control and recovery for balanced {B}-link
trees",
journal = j-VLDB-J,
volume = "14",
number = "2",
pages = "257--277",
month = apr,
year = "2005",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:15 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper we present new concurrent and
recoverable B-link-tree algorithms. Unlike previous
algorithms, ours maintain the balance of the B-link
tree at all times, so that a logarithmic time bound for
a search or an update operation is guaranteed under
arbitrary sequences of record insertions and deletions.
A database transaction can contain any number of
operations of the form ``fetch the first (or next)
matching record'', ``insert a record'', or ``delete a
record'', where database records are identified by
their primary keys. Repeatable-read-level isolation for
transactions is guaranteed by key-range locking. The
algorithms apply the write-ahead logging (WAL) protocol
and the steal and no-force buffering policies for index
and data pages. Record inserts and deletes on leaf
pages of a B-link tree are logged using physiological
redo-undo log records. Each structure modification such
as a page split or merge is made an atomic action by
keeping the pages involved in the modification latched
for the (short) duration of the modification and the
logging of that modification; at most two B-link-tree
pages are kept $X$-latched at a time. Each structure
modification brings the B-link tree into a structurally
consistent and balanced state whenever the tree was
structurally consistent and balanced initially. Each
structure modification is logged using a single
physiological redo-only log record. Thus, a structure
modification will never be undone even if the
transaction that gave rise to it eventually aborts. In
restart recovery, the redo pass of our ARIES-based
recovery protocol will always produce a structurally
consistent and balanced B-link tree, on which the
database updates by backward-rolling transactions can
always be undone logically, when a physical
(page-oriented) undo is no longer possible.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "concurrency control; recovery; transaction;
tree-structure modifications",
}
@Article{Gaasterland:2005:SID,
author = "Terry Gaasterland and H. V. Jagadish and Louiqa
Raschid",
title = "Special issue on data management, analysis, and mining
for the life sciences",
journal = j-VLDB-J,
volume = "14",
number = "3",
pages = "279--280",
month = sep,
year = "2005",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-005-0165-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:16 MDT 2008",
bibsource = "http://portal.acm.org/;
http://springerlink.metapress.com/openurl.asp?genre=issue&issn=0938-1287&volume=14&issue=3;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0938-1287&volume=14&issue=3&spage=279",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Tian:2005:PMC,
author = "Yuanyuan Tian and Sandeep Tata and Richard A. Hankins
and Jignesh M. Patel",
title = "Practical methods for constructing suffix trees",
journal = j-VLDB-J,
volume = "14",
number = "3",
pages = "281--299",
month = sep,
year = "2005",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-005-0154-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:16 MDT 2008",
bibsource = "http://portal.acm.org/;
http://springerlink.metapress.com/openurl.asp?genre=issue&issn=0938-1287&volume=14&issue=3;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0938-1287&volume=14&issue=3&spage=281",
abstract = "Sequence datasets are ubiquitous in modern
life-science applications, and querying sequences is a
common and critical operation in many of these
applications. The suffix tree is a versatile data
structure that can be used to evaluate a wide variety
of queries on sequence datasets, including evaluating
exact and approximate string matches, and finding
repeat patterns. However, methods for constructing
suffix trees are often very time-consuming, especially
for suffix trees that are large and do not fit in the
available main memory. Even when the suffix tree fits
in memory, it turns out that the processor cache
behavior of theoretically optimal suffix tree
construction methods is poor, resulting in poor
performance. Currently, there are a large number of
algorithms for constructing suffix trees, but the
practical tradeoffs in using these algorithms for
different scenarios are not well characterized. In this
paper, we explore suffix tree construction algorithms
over a wide spectrum of data sources and sizes. First,
we show that on modern processors, a cache-efficient
algorithm with $ O(n^2) $ worst-case complexity
outperforms popular linear time algorithms like Ukkonen
and McCreight, even for in-memory construction. For
larger datasets, the disk I/O requirement quickly
becomes the bottleneck in each algorithm's performance.
To address this problem, we describe two approaches.
First, we present a buffer management strategy for the
$ O(n^2) $ algorithm. The resulting new algorithm,
which we call ``Top Down Disk-based'' (TDD), scales to
sizes much larger than have been previously described
in literature. This approach far outperforms the best
known disk-based construction methods. Second, we
present a new disk-based suffix tree construction
algorithm that is based on a sort-merge paradigm, and
show that for constructing very large suffix trees with
very little resources, this algorithm is more efficient
than TDD.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "sequence matching; suffix tree construction",
}
@Article{Claypool:2005:SYD,
author = "Kajal T. Claypool and Elke A. Rundensteiner",
title = "Sync your data: update propagation for heterogeneous
protein databases",
journal = j-VLDB-J,
volume = "14",
number = "3",
pages = "300--317",
month = sep,
year = "2005",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-005-0155-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:16 MDT 2008",
bibsource = "http://portal.acm.org/;
http://springerlink.metapress.com/openurl.asp?genre=issue&issn=0938-1287&volume=14&issue=3;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0938-1287&volume=14&issue=3&spage=300",
abstract = "The traditional model of bench (wet) chemistry in many
life sciences domain is today actively complemented by
computer-based discoveries utilizing the growing number
of online data sources. A typical {\em computer-based
discovery\/} scenario for many life scientists includes
the creation of local caches of pertinent information
from multiple online resources such as Swissprot
[Nucleic Acid Res. 1 (28), 45--48 (2000)], PIR [Nucleic
Acids Res. 28 (1), 41--44 (2000)], PDB [The Protein
DataBank. Wiley, New York (2003)], to enable efficient
data analysis. This local caching of data, however,
exposes their research and eventual results to the
problems of data staleness, that is, cached data may
quickly be obsolete or incorrect, dependent on the
updates that are made to the source data. This
represents a significant challenge to the scientific
community, forcing scientists to be continuously aware
of the frequent changes made to public data sources,
and more importantly aware of the potential effects on
their own derived data sets during the course of their
research. To address this significant challenge, in
this paper we present an approach for handling update
propagation between heterogeneous databases,
guaranteeing data freshness for scientists irrespective
of their choice of data source and its underlying data
model or interface. We propose a {\em middle-layer\/}
--based solution wherein first the change in the online
data source is translated to a sequence of changes in
the middle-layer; next each change in the middle-layer
is propagated through an algebraic representation of
the translation between the source and the target; and
finally the net-change is translated to a set of
changes that are then applied to the local cache. In
this paper, we present our algebraic model that
represents the mapping of the online resource to the
local cache, as well as our adaptive propagation
algorithm that can incrementally propagate both schema
and data changes from the source to the cache in a data
model independent manner. We present a case study based
on a joint ongoing project with our collaborators in
the Chemistry Department at UMass-Lowell to explicate
our approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data transformation; data translation; schema
evolution; update propagation; view maintenance",
}
@Article{Conery:2005:RBW,
author = "John S. Conery and Julian M. Catchen and Michael
Lynch",
title = "Rule-based workflow management for bioinformatics",
journal = j-VLDB-J,
volume = "14",
number = "3",
pages = "318--329",
month = sep,
year = "2005",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-005-0153-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:16 MDT 2008",
bibsource = "http://portal.acm.org/;
http://springerlink.metapress.com/openurl.asp?genre=issue&issn=0938-1287&volume=14&issue=3;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0938-1287&volume=14&issue=3&spage=318",
abstract = "We describe a data-centric software architecture for
bioinformatics workflows and a rule-based workflow
enactment system that uses declarative specifications
of data dependences between steps to automatically
order the execution of those steps. A data-centric view
allows researchers to develop abstract descriptions of
workflow products and provides mechanisms for
describing workflow steps as objects. The rule-based
approach supports an iterative design methodology for
creating new workflows, where steps can be developed in
small, incremental updates, and the object orientation
allows workflow steps developed for one project to be
reused in other projects.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "bioinformatics; rule-based system; workflow",
}
@Article{Thakkar:2005:COE,
author = "Snehal Thakkar and Jos{\'e} Luis Ambite and Craig A.
Knoblock",
title = "Composing, optimizing, and executing plans for
bioinformatics web services",
journal = j-VLDB-J,
volume = "14",
number = "3",
pages = "330--353",
month = sep,
year = "2005",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-005-0158-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:16 MDT 2008",
bibsource = "http://portal.acm.org/;
http://springerlink.metapress.com/openurl.asp?genre=issue&issn=0938-1287&volume=14&issue=3;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0938-1287&volume=14&issue=3&spage=330",
abstract = "The emergence of a large number of bioinformatics
datasets on the Internet has resulted in the need for
flexible and efficient approaches to integrate
information from multiple bioinformatics data sources
and services. In this paper, we present our approach to
automatically generate composition plans for web
services, optimize the composition plans, and execute
these plans efficiently. While data integration
techniques have been applied to the bioinformatics
domain, the focus has been on answering specific user
queries. In contrast, we focus on automatically
generating {\em parameterized\/} integration plans that
can be hosted as web services that respond to a range
of inputs. In addition, we present two novel techniques
that improve the execution time of the generated plans
by reducing the number of requests to the existing data
sources and by executing the generated plan more
efficiently. The first optimization technique, called
tuple-level filtering, analyzes the source/service
descriptions in order to automatically insert filtering
conditions in the composition plans that result in
fewer requests to the component web services. To ensure
that the filtering conditions can be evaluated, this
technique may include sensing operations in the
integration plan. The savings due to filtering
significantly exceed the cost of the sensing
operations. The second optimization technique consists
in mapping the integration plans into programs that can
be executed by a dataflow-style, streaming execution
engine. We use real-world bioinformatics web services
to show experimentally that (1) our automatic
composition techniques can efficiently generate
parameterized plans that integrate data from large
numbers of existing services and (2) our optimization
techniques can significantly reduce the response time
of the generated integration plans.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "bioinformatics; data integration; dataflow-style
streaming execution; query optimization; Web service
composition",
}
@Article{Vlachos:2006:IMT,
author = "Michail Vlachos and Marios Hadjieleftheriou and
Dimitrios Gunopulos and Eamonn Keogh",
title = "Indexing {Multidimensional Time-Series}",
journal = j-VLDB-J,
volume = "15",
number = "1",
pages = "1--20",
month = jan,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:17 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "While most time series data mining research has
concentrated on providing solutions for a single
distance function, in this work we motivate the need
for an index structure that can support multiple
distance measures. Our specific area of interest is the
efficient retrieval and analysis of similar
trajectories. Trajectory datasets are very common in
environmental applications, mobility experiments, and
video surveillance and are especially important for the
discovery of certain biological patterns. Our primary
similarity measure is based on the longest common
subsequence (LCSS) model that offers enhanced
robustness, particularly for noisy data, which are
encountered very often in real-world applications.
However, our index is able to accommodate other
distance measures as well, including the ubiquitous
Euclidean distance and the increasingly popular dynamic
time warping (DTW). While other researchers have
advocated one or other of these similarity measures, a
major contribution of our work is the ability to
support all these measures without the need to
restructure the index. Our framework guarantees no
false dismissals and can also be tailored to provide
much faster response time at the expense of slightly
reduced precision/recall. The experimental results
demonstrate that our index can help speed up the
computation of expensive similarity measures such as
the LCSS and the DTW.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "dynamic time warping; ensemble index; longest common
subsequence; motion capture; trajectories",
}
@Article{Zheng:2006:GPI,
author = "Baihua Zheng and Jianliang Xu and Wang-Chien Lee and
Lun Lee",
title = "Grid-partition index: a hybrid method for
nearest-neighbor queries in wireless location-based
services",
journal = j-VLDB-J,
volume = "15",
number = "1",
pages = "21--39",
month = jan,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:17 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Traditional nearest-neighbor (NN) search is based on
two basic indexing approaches: object-based indexing
and solution-based indexing. The former is constructed
based on the locations of data objects: using some
distance heuristics on object locations. The latter is
built on a precomputed solution space. Thus, NN queries
can be reduced to and processed as simple point queries
in this solution space. Both approaches exhibit some
disadvantages, especially when employed for wireless
data broadcast in mobile computing environments. In
this paper, we introduce a new index method, called the
{\em grid-partition index}, to support NN search in
both on-demand access and periodic broadcast modes of
mobile computing. The grid-partition index is
constructed based on the Voronoi diagram, i.e., the
solution space of NN queries. However, it has two
distinctive characteristics. First, it divides the
solution space into grid cells such that a query point
can be efficiently mapped into a grid cell around which
the nearest object is located. This significantly
reduces the search space. Second, the grid-partition
index stores the {\em objects\/} that are potential NNs
of any query falling within the cell. The storage of
objects, instead of the Voronoi cells, makes the
grid-partition index a hybrid of the solution-based and
object-based approaches. As a result, it achieves a
much more compact representation than the pure
solution-based approach and avoids backtracked
traversals required in the typical object-based
approach, thus realizing the advantages of both
approaches. We develop an incremental construction
algorithm to address the issue of object update. In
addition, we present a cost model to approximate the
search cost of different grid partitioning schemes. The
performances of the grid-partition index and existing
indexes are evaluated using both synthetic and real
data. The results show that, overall, the
grid-partition index significantly outperforms
object-based indexes and solution-based indexes.
Furthermore, we extend the grid-partition index to
support continuous-nearest-neighbor search. Both
algorithms and experimental results are presented.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "continuous-nearest-neighbor search; index structure;
location-dependent data; nearest-neighbor search;
wireless broadcast",
}
@Article{Tamir:2006:CGM,
author = "Raz Tamir and Yehuda Singer",
title = "On a confidence gain measure for association rule
discovery and scoring",
journal = j-VLDB-J,
volume = "15",
number = "1",
pages = "40--52",
month = jan,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:17 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This article presents a new interestingness measure
for association rules called confidence gain (CG).
Focus is given to extraction of human associations
rather than associations between market products. There
are two main differences between the two (human and
market associations). The first difference is the
strong asymmetry of human associations (e.g., the
association ``shampoo''--``hair'' is much stronger than
``hair''--``shampoo''), where in market products
asymmetry is less intuitive and less evident. The
second is the background knowledge humans employ when
presented with a stimulus (input phrase).CG calculates
the local confidence of a given term compared to its
average confidence throughout a given database. CG is
found to outperform several association measures since
it captures both the asymmetric notion of an
association (as in the confidence measure) while adding
the comparison to an expected confidence (as in the
lift measure). The use of average confidence introduces
the ``background knowledge'' notion into the CG
measure. Various experiments have shown that CG and
local confidence gain (a low-complexity version of CG)
successfully generate association rules when compared
to human free associations. The experiments include a
large-scale ``free sssociation Turing test'' where
human free associations were compared to associations
generated by the CG and other association measures.
Rules discovered by CG were found to be significantly
better than those discovered by other measures. CG can
be used for many purposes, such as personalization,
sense disambiguation, query expansion, and improving
classification performance of small item sets within
large databases. Although CG was found to be useful for
Internet data retrieval, results can be easily used
over any type of database.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "association generation; association rule validation
methods; confidence gain; Web data management; Web
mining",
}
@Article{Bremer:2006:IDD,
author = "Jan-Marco Bremer and Michael Gertz",
title = "Integrating document and data retrieval based on
{XML}",
journal = j-VLDB-J,
volume = "15",
number = "1",
pages = "53--83",
month = jan,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:17 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "For querying structured and semistructured data, data
retrieval and document retrieval are two valuable and
complementary techniques that have not yet been fully
integrated. In this paper, we introduce integrated
information retrieval (IIR), an XML-based retrieval
approach that closes this gap. We introduce the syntax
and semantics of an extension of the XQuery language
called XQuery/IR. The extended language realizes IIR
and thereby allows users to formulate new kinds of
queries by nesting ranked document retrieval and
precise data retrieval queries. Furthermore, we detail
index structures and efficient query processing
approaches for implementing XQuery/IR. Based on a new
identification scheme for nodes in node-labeled tree
structures, the extended index structures require only
a fraction of the space of comparable index structures
that only support data retrieval.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data retrieval; document retrieval; index structures;
integrated information retrievals; structural join;
XML",
}
@Article{Ogras:2006:OSD,
author = "Y. Ogras and Hakan Ferhatosmanoglu",
title = "Online summarization of dynamic time series data",
journal = j-VLDB-J,
volume = "15",
number = "1",
pages = "84--98",
month = jan,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:17 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Managing large-scale time series databases has
attracted significant attention in the database
community recently. Related fundamental problems such
as dimensionality reduction, transformation, pattern
mining, and similarity search have been studied
extensively. Although the time series data are dynamic
by nature, as in data streams, current solutions to
these fundamental problems have been mostly for the
static time series databases. In this paper, we first
propose a framework to online summary generation for
large-scale and dynamic time series data, such as data
streams. Then, we propose online transform-based
summarization techniques over data streams that can be
updated in constant time and space. We present both the
exact and approximate versions of the proposed
techniques and provide error bounds for the approximate
case. One of our main contributions in this paper is
the extensive performance analysis. Our experiments
carefully evaluate the quality of the online summaries
for point, range, and $ k ???? n n $ queries using
real-life dynamic data sets of substantial size.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data streams; dimensionality reduction; time-series
data; transformation-based summarization",
}
@Article{Goh:2006:DBM,
author = "Leng Goh and Yanfeng Shu and Zhiyong Huang and Chin
Ooi",
title = "Dynamic buffer management with extensible replacement
policies",
journal = j-VLDB-J,
volume = "15",
number = "2",
pages = "99--120",
month = jun,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:18 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The objective of extensible DBMSs is to ease the
construction of specialized DBMSs for nontraditional
applications. Although much work has been done in
providing various levels of extensibility (e.g.,
extensibility of data types and operators, query
language extensibility, and query optimizer
extensibility), there has been very limited research in
providing extensibility at the buffer management level.
Supporting extensibility at the buffer management level
is important as it can contribute significantly to
overall system performance. This paper addresses the
problem of supporting extensibility of buffer
replacement policies. The main contribution is the
proposal of a framework for modeling buffer replacement
policies. This work is novel in two aspects. First, by
providing a uniform and generic specification of buffer
replacement policies, the proposed framework unifies
existing work in this area. Second, our work introduces
a new level of extensibility. None of the existing
extensible DBMSs, to our knowledge, provides
extensibility at the buffer management level. The
proposed framework provides a basis for the
construction of an extensible buffer manager as part of
a 100\% Java-based storage manager. We conducted an
extensive performance study to investigate the
performance of the proposed framework. The experimental
results demonstrate that the proposed framework is
indeed feasible for existing DBMSs and improves system
performance significantly without costing significant
overhead.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "buffer management; extensible DBMS; replacement
strategies",
}
@Article{Arasu:2006:CCQ,
author = "Arvind Arasu and Shivnath Babu and Jennifer Widom",
title = "The {CQL} continuous query language: semantic
foundations and query execution",
journal = j-VLDB-J,
volume = "15",
number = "2",
pages = "121--142",
month = jun,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:18 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "{\em CQL}, a {\em continuous query language}, is
supported by the STREAM prototype data stream
management system (DSMS) at Stanford. CQL is an
expressive SQL-based declarative language for
registering continuous queries against streams and
stored relations. We begin by presenting an abstract
semantics that relies only on ``black-box'' mappings
among streams and relations. From these mappings we
define a precise and general interpretation for
continuous queries. CQL is an instantiation of our
abstract semantics using SQL to map from relations to
relations, window specifications derived from SQL-99 to
map from streams to relations, and three new operators
to map from relations to streams. Most of the CQL
language is operational in the STREAM system. We
present the structure of CQL's query execution plans as
well as details of the most important components:
operators, interoperator queues, synopses, and sharing
of components among multiple operators and queries.
Examples throughout the paper are drawn from the {\em
Linear Road\/} benchmark recently proposed for DSMSs.
We also curate a public repository of data stream
applications that includes a wide variety of queries
expressed in CQL. The relative ease of capturing these
applications in CQL is one indicator that the language
contains an appropriate set of constructs for data
stream processing.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "continuous queries; data streams; query language;
query processing",
}
@Article{Hadjieleftheriou:2006:ISA,
author = "Marios Hadjieleftheriou and George Kollios and J.
Tsotras and Dimitrios Gunopulos",
title = "Indexing spatiotemporal archives",
journal = j-VLDB-J,
volume = "15",
number = "2",
pages = "143--164",
month = jun,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:18 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Spatiotemporal objects --- that is, objects that
evolve over time --- appear in many applications. Due
to the nature of such applications, storing the
evolution of objects through time in order to answer
historical queries (queries that refer to past states
of the evolution) requires a very large specialized
database, what is termed in this article a {\em
spatiotemporal archive}. Efficient processing of
historical queries on spatiotemporal archives requires
equally sophisticated indexing schemes. Typical
spatiotemporal indexing techniques represent the
objects using minimum bounding regions (MBR) extended
with a temporal dimension, which are then indexed using
traditional multidimensional index structures. However,
rough MBR approximations introduce excessive overlap
between index nodes, which deteriorates query
performance. This article introduces a robust indexing
scheme for answering spatiotemporal queries more
efficiently. A number of algorithms and heuristics are
elaborated that can be used to preprocess a
spatiotemporal archive in order to produce {\em finer
object approximations}, which, in combination with {\em
a multiversion index structure}, will greatly improve
query performance in comparison to the straightforward
approaches. The proposed techniques introduce a query
efficiency vs. space tradeoff that can help tune a
structure according to available resources. Empirical
observations for estimating the necessary amount of
additional storage space required for improving query
performance by a given factor are also provided.
Moreover, heuristics for applying the proposed ideas in
an online setting are discussed. Finally, a thorough
experimental evaluation is conducted to show the merits
of the proposed techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "indexing; moving objects; spatiotemporal databases;
trajectories",
}
@Article{Guting:2006:MQM,
author = "Hartmut G{\"u}ting and Teixeira de Almeida and Zhiming
Ding",
title = "Modeling and querying moving objects in networks",
journal = j-VLDB-J,
volume = "15",
number = "2",
pages = "165--190",
month = jun,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:18 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Moving objects databases have become an important
research issue in recent years. For modeling and
querying moving objects, there exists a comprehensive
framework of abstract data types to describe objects
moving freely in the 2D plane, providing data types
such as {\em moving point\/} or {\em moving region}.
However, in many applications people or vehicles move
along transportation networks. It makes a lot of sense
to model the network explicitly and to describe
movements relative to the network rather than
unconstrained space, because then it is much easier to
formulate in queries relationships between moving
objects and the network. Moreover, such models can be
better supported in indexing and query processing. In
this paper, we extend the ADT approach by modeling
networks explicitly and providing data types for static
and moving network positions and regions. In a highway
network, example entities corresponding to these data
types are motels, construction areas, cars, and traffic
jams. The network model is not too simplistic; it
allows one to distinguish simple roads and divided
highways and to describe the possible traversals of
junctions precisely. The new types and operations are
integrated seamlessly into the ADT framework to achieve
a relatively simple, consistent and powerful overall
model and query language for constrained and
unconstrained movement.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "ADT; data type; moving object; network;
spatio-temporal",
}
@Article{Chirkova:1999:AQU,
author = "Rada Chirkova and Chen Li and Jia Li",
title = "Answering queries using materialized views with
minimum size",
journal = j-VLDB-J,
volume = "15",
number = "3",
pages = "191--210",
month = apr,
year = "1999",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:19 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we study the following problem. Given a
database and a set of queries, we want to find a set of
views that can compute the answers to the queries, such
that the amount of space, in bytes, required to store
the viewset is minimum on the given database. (We also
handle problem instances where the input has a {\em
set\/} of database instances, as described by an oracle
that returns the sizes of view relations for given view
definitions.) This problem is important for
applications such as distributed databases, data
warehousing, and data integration. We explore the
decidability and complexity of the problem for
workloads of conjunctive queries. We show that results
differ significantly depending on whether the workload
queries have self-joins. Further, for queries without
self-joins we describe a very compact search space of
views, which contains all views in at least one optimal
viewset. We present techniques for finding a
minimum-size viewset for a single query without
self-joins by using the shape of the query and its
constraints, and validate the approach by extensive
experiments.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data warehouses; distributed systems; minimum-size
viewsets; views",
remark = "Check month: April or May??",
}
@Article{Cao:1999:STD,
author = "Hu Cao and Ouri Wolfson and Goce Trajcevski",
title = "Spatio-temporal data reduction with deterministic
error bounds",
journal = j-VLDB-J,
volume = "15",
number = "3",
pages = "211--228",
month = apr,
year = "1999",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:19 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A common way of storing spatio-temporal information
about mobile devices is in the form of a 3D (2D
geography + time) trajectory. We argue that when
cellular phones and Personal Digital Assistants become
location-aware, the size of the spatio-temporal
information generated may prohibit efficient
processing. We propose to adopt a technique studied in
computer graphics, namely line-simplification, as an
approximation technique to solve this problem. Line
simplification will reduce the size of the
trajectories. Line simplification uses a distance
function in producing the trajectory approximation. We
postulate the desiderata for such a distance-function:
it should be sound, namely the error of the answers to
spatio-temporal queries must be bounded. We analyze
several distance functions, and prove that some are
sound in this sense for some types of queries, while
others are not. A distance function that is sound for
all common spatio-temporal query types is introduced
and analyzed. Then we propose an aging mechanism which
gradually shrinks the size of the trajectories as time
progresses. We also propose to adopt existing
linguistic constructs to manage the uncertainty
introduced by the trajectory approximation. Finally, we
analyze experimentally the effectiveness of
line-simplification in reducing the size of a
trajectories database.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data reduction; line simplification; moving objects
database; uncertainty",
remark = "Check month: April or May??",
}
@Article{Benetis:1999:NRN,
author = "Rimantas Benetis and S. Jensen and Gytis
Kar{\c{c}}iauskas and Simonas {\ocirc{S}}altenis",
title = "Nearest and reverse nearest neighbor queries for
moving objects",
journal = j-VLDB-J,
volume = "15",
number = "3",
pages = "229--249",
month = apr,
year = "1999",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:19 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "With the continued proliferation of wireless
communications and advances in positioning
technologies, algorithms for efficiently answering
queries about large populations of moving objects are
gaining interest. This paper proposes algorithms for
$k$ nearest and reverse $k$ nearest neighbor queries on
the current and anticipated future positions of points
moving continuously in the plane. The former type of
query returns $k$ objects nearest to a query object for
each time point during a time interval, while the
latter returns the objects that have a specified query
object as one of their $k$ closest neighbors, again for
each time point during a time interval. In addition,
algorithms for so-called persistent and continuous
variants of these queries are provided. The algorithms
are based on the indexing of object positions
represented as linear functions of time. The results of
empirical performance experiments are reported.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "continuous queries; incremental update; location-based
services; mobile objects; neighbor queries; persistent
queries",
remark = "Check month: April or May??",
}
@Article{Pelleg:1999:DTS,
author = "Dan Pelleg and Andrew Moore",
title = "Dependency trees in sub-linear time and bounded
memory",
journal = j-VLDB-J,
volume = "15",
number = "3",
pages = "250--262",
month = apr,
year = "1999",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:19 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We focus on the problem of efficient learning of
dependency trees. Once grown, they can be used as a
special case of a Bayesian network, for PDF
approximation, and for many other uses. Given the data,
a well-known algorithm can fit an optimal tree in time
that is quadratic in the number of attributes and
linear in the number of records. We show how to modify
it to exploit partial knowledge about edge weights.
Experimental results show running time that is
near-constant in the number of records, without
significant loss in accuracy of the generated trees.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data mining; dependency trees; fast algorithms;
probably approximately correct learning",
remark = "Check month: April or May??",
}
@Article{Che:1999:QOX,
author = "Dunren Che and Karl Aberer and Tamer {\"O}zsu",
title = "Query optimization in {XML} structured-document
databases",
journal = j-VLDB-J,
volume = "15",
number = "3",
pages = "263--289",
month = apr,
year = "1999",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:19 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "While the information published in the form of
XML-compliant documents keeps fast mounting up,
efficient and effective query processing and
optimization for XML have now become more important
than ever. This article reports our recent advances in
XML structured-document query optimization. In this
article, we elaborate on a novel approach and the
techniques developed for XML query optimization. Our
approach performs heuristic-based algebraic
transformations on XPath queries, represented as PAT
algebraic expressions, to achieve query optimization.
This article first presents a comprehensive set of
general equivalences with regard to XML documents and
XML queries. Based on these equivalences, we developed
a large set of deterministic algebraic transformation
rules for XML query optimization. Our approach is
unique, in that it performs exclusively deterministic
transformations on queries for fast optimization. The
deterministic nature of the proposed approach
straightforwardly renders high optimization efficiency
and simplicity in implementation. Our approach is a
logical-level one, which is independent of any
particular storage model. Therefore, the optimizers
developed based on our approach can be easily adapted
to a broad range of XML data/information servers to
achieve fast query optimization. Experimental study
confirms the validity and effectiveness of the proposed
approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "deterministic query optimization; query
transformation; XML database; XML query optimization;
XML query processing",
remark = "Check month: April or May??",
}
@Article{Ferrari:2006:GES,
author = "Elena Ferrari and Bhavani Thuraisingham",
title = "Guest editorial: special issue on privacy preserving
data management",
journal = j-VLDB-J,
volume = "15",
number = "4",
pages = "291--292",
month = nov,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
remark = "Check month: April or November??",
}
@Article{Mukherjee:2006:PPT,
author = "Shibnath Mukherjee and Zhiyuan Chen and Aryya
Gangopadhyay",
title = "A privacy-preserving technique for {Euclidean}
distance-based mining algorithms using
{Fourier}-related transforms",
journal = j-VLDB-J,
volume = "15",
number = "4",
pages = "293--315",
month = nov,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Privacy preserving data mining has become increasingly
popular because it allows sharing of privacy-sensitive
data for analysis purposes. However, existing
techniques such as random perturbation do not fare well
for simple yet widely used and efficient Euclidean
distance-based mining algorithms. Although original
data distributions can be pretty accurately
reconstructed from the perturbed data, distances
between individual data points are not preserved,
leading to poor accuracy for the distance-based mining
methods. Besides, they do not generally focus on data
reduction. Other studies on secure multi-party
computation often concentrate on techniques useful to
very specific mining algorithms and scenarios such that
they require modification of the mining algorithms and
are often difficult to generalize to other mining
algorithms or scenarios. This paper proposes a novel
generalized approach using the well-known energy
compaction power of Fourier-related transforms to hide
sensitive data values and to approximately preserve
Euclidean distances in centralized and distributed
scenarios to a great degree of accuracy. Three
algorithms to select the most important transform
coefficients are presented, one for a centralized
database case, the second one for a horizontally
partitioned, and the third one for a vertically
partitioned database case. Experimental results
demonstrate the effectiveness of the proposed
approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data mining; Fourier transform; privacy",
remark = "Check month: September or November??",
}
@Article{Jiang:2006:SDF,
author = "Wei Jiang and Chris Clifton",
title = "A secure distributed framework for achieving
$k$-anonymity",
journal = j-VLDB-J,
volume = "15",
number = "4",
pages = "316--333",
month = nov,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "$k$-anonymity provides a measure of privacy protection
by preventing re-identification of data to fewer than a
group of $k$ data items. While algorithms exist for
producing $k$-anonymous data, the model has been that
of a single source wanting to publish data. Due to
privacy issues, it is common that data from different
sites cannot be shared directly. Therefore, this paper
presents a two-party framework along with an
application that generates $k$-anonymous data from two
vertically partitioned sources without disclosing data
from one site to the other. The framework is privacy
preserving in the sense that it satisfies the secure
definition commonly defined in the literature of Secure
Multiparty Computation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "anonymity; privacy; security",
remark = "Check month: April or November??",
}
@Article{Blanton:2006:SRF,
author = "Marina Blanton and Mikhail Atallah",
title = "Succinct representation of flexible and
privacy-preserving access rights",
journal = j-VLDB-J,
volume = "15",
number = "4",
pages = "334--354",
month = nov,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We explore the problem of portable and flexible
privacy preserving access rights that permit access to
a large collection of digital goods. {\em
Privacy-preserving\/} access control means that the
service provider can neither learn what access rights a
customer has nor link a request to access an item to a
particular customer, thus maintaining privacy of both
customer activity and customer access rights. {\em
Flexible\/} access rights allow a customer to choose a
subset of items or groups of items from the repository,
obtain access to and be charged only for the items
selected. And {\em portability\/} of access rights
means that the rights themselves can be stored on small
devices of limited storage space and computational
capabilities such as smartcards or sensors, and
therefore the rights must be enforced using the limited
resources available. In this paper, we present and
compare two schemes that address the problem of such
access rights. We show that much can be achieved if one
allows for even a negligible amount of false positives
--- items that were not requested by the customer, but
inadvertently were included in the customer access
right representation due to constrained space
resources. But minimizing false positives is one of
many other desiderata that include protection against
sharing of false positives information by unscrupulous
users, providing the users with transaction
untraceability and unlinkability, and forward
compatibility of the scheme. Our first scheme does not
place any constraints on the amount of space available
on the limited-capacity storage device, and searches
for the best representation that meets the
requirements. The second scheme, on the other hand, has
(modest) requirements on the storage space available,
but guarantees a low rate of false positives: with $
O(m c) $ storage space available on the smartcard
(where $m$ is the number of items or groups of items
included in the subscription and $c$ is a selectable
parameter), it achieves a rate of false positives of $
m^{-c}$.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "compact representation; flexible access rights;
privacy-preserving access rights",
remark = "Check month: April or November??",
}
@Article{Domingo-Ferrer:2006:EMD,
author = "Josep Domingo-Ferrer and Antoni
Mart{\'\i}nez-Ballest{\'e} and Josep Maria Mateo-Sanz
and Francesc Seb{\'e}",
title = "Efficient multivariate data-oriented
microaggregation",
journal = j-VLDB-J,
volume = "15",
number = "4",
pages = "355--369",
month = nov,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Microaggregation is a family of methods for
statistical disclosure control (SDC) of microdata
(records on individuals and/or companies), that is, for
masking microdata so that they can be released while
preserving the privacy of the underlying individuals.
The principle of microaggregation is to aggregate
original database records into small groups prior to
publication. Each group should contain at least $k$
records to prevent disclosure of individual
information, where $k$ is a constant value preset by
the data protector. Recently, microaggregation has been
shown to be useful to achieve $k$-anonymity, in
addition to it being a good masking method. Optimal
microaggregation (with minimum within-groups
variability loss) can be computed in polynomial time
for univariate data. Unfortunately, for multivariate
data it is an NP-hard problem. Several heuristic
approaches to microaggregation have been proposed in
the literature. Heuristics yielding groups with fixed
size $k$ tends to be more efficient, whereas
data-oriented heuristics yielding variable group size
tends to result in lower information loss. This paper
presents new data-oriented heuristics which improve on
the trade-off between computational complexity and
information loss and are thus usable for large
datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "anonymity; microaggregation; microdata protection;
privacy; statistical databases; statistical disclosure
control",
remark = "Check month: April or November??",
}
@Article{Massacci:2006:HHD,
author = "Fabio Massacci and John Mylopoulos and Nicola
Zannone",
title = "Hierarchical {Hippocratic} databases with minimal
disclosure for virtual organizations",
journal = j-VLDB-J,
volume = "15",
number = "4",
pages = "370--387",
month = nov,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The protection of customer privacy is a fundamental
issue in today's corporate marketing strategies. Not
surprisingly, many research efforts have proposed new
privacy-aware technologies. Among them, Hippocratic
databases offer mechanisms for enforcing privacy rules
in database systems for inter-organizational business
processes (also known as virtual organizations). This
paper extends these mechanisms to allow for
hierarchical purposes, distributed authorizations and
minimal disclosure supporting the business processes of
virtual organizations that want to offer their clients
a number of ways to fulfill a service. Specifically, we
use a goal-oriented approach to analyze privacy
policies of the enterprises involved in a business
process. On the basis of the purpose hierarchy derived
through a goal refinement process, we provide
algorithms for determining the minimum set of
authorizations needed to achieve a service. This allows
us to automatically derive access control policies for
an inter-organizational business process from the
collection of privacy policies associated with
different participating enterprises. By using effective
on-line algorithms, the derivation of such minimal
information can also be done on-the-fly by the customer
wishing to access a service.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access control; delegation; information security;
minimal disclosure; privacy protection; private data
management; virtual organizations",
remark = "Check month: April or November??",
}
@Article{Xiong:2006:PLM,
author = "Hui Xiong and Michael Steinbach and Vipin Kumar",
title = "Privacy leakage in multi-relational databases: a
semi-supervised learning perspective",
journal = j-VLDB-J,
volume = "15",
number = "4",
pages = "388--402",
month = nov,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In multi-relational databases, a view, which is a
context- and content-dependent subset of one or more
tables (or other views), is often used to preserve
privacy by hiding sensitive information. However,
recent developments in data mining present a new
challenge for database security even when traditional
database security techniques, such as database access
control, are employed. This paper presents a data
mining framework using semi-supervised learning that
demonstrates the potential for privacy leakage in
multi-relational databases. Many different types of
semi-supervised learning techniques, such as the
K-nearest neighbor (KNN) method, can be used to
demonstrate privacy leakage. However, we also introduce
a new approach to semi-supervised learning, hyperclique
pattern-based semi-supervised learning (HPSL), which
differs from traditional semi-supervised learning
approaches in that it considers the similarity among
groups of objects instead of only pairs of objects. Our
experimental results show that both the KNN and HPSL
methods have the ability to compromise database
security, although the HPSL is better at this privacy
violation (has higher prediction accuracy) than the KNN
method. Finally, we provide a principle for avoiding
privacy leakage in multi-relational databases via
semi-supervised learning and illustrate this principle
with a simple preventive technique whose effectiveness
is demonstrated by experiments.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
remark = "Check month: April or November??",
}
@Article{Haas:2006:SIB,
author = "Laura M. Haas and Christian S. Jensen and Martin L.
Kersten",
title = "Special issue: best papers of {VLDB 2005}",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "1--3",
month = oct,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 15 06:36:12 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Godfrey:2006:AAM,
author = "Parke Godfrey and Ryan Shipley and Jarek Gryz",
title = "Algorithms and analyses for maximal vector
computation",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "5--28",
month = oct,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 15 06:36:12 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Larson:2006:VMO,
author = "Per-{\AA}ke Larson and Jingren Zhou",
title = "View matching for outer-join views",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "29--53",
month = oct,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 15 06:36:12 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Markl:2006:CSE,
author = "V. Markl and P. J. Haas and M. Kutsch and N. Megiddo
and U. Srivastava and T. M. Tran",
title = "Consistent selectivity estimation via maximum
entropy",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "55--76",
month = oct,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 15 06:36:12 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ghoting:2006:CCF,
author = "Amol Ghoting and Gregory Buehrer and Srinivasan
Parthasarathy and Daehyun Kim and Anthony Nguyen and
Yen-Kuang Chen and Pradeep Dubey",
title = "Cache-conscious frequent pattern mining on modern and
emerging processors",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "77--96",
month = oct,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 15 06:36:12 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lee:2006:ETS,
author = "Yoonkyong Lee and Mayssam Sayyadian and AnHai Doan and
Arnon S. Rosenthal",
title = "{eTuner}: tuning schema matching software using
synthetic scenarios",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "97--122",
month = oct,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 15 06:36:12 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Burdick:2006:OUI,
author = "Doug Burdick and Prasad M. Deshpande and T. S. Jayram
and Raghu Ramakrishnan and Shivakumar Vaithyanathan",
title = "{OLAP} over uncertain and imprecise data",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "123--144",
month = oct,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 15 06:36:12 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Haftmann:2006:FER,
author = "Florian Haftmann and Donald Kossmann and Eric Lo",
title = "A framework for efficient regression tests on database
applications",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "145--164",
month = oct,
year = "2006",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 15 06:36:12 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Haas:2007:SIB,
author = "Laura M. Haas and Christian S. Jensen and Martin L.
Kersten",
title = "Special issue: best papers of {VLDB} 2005",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "1--3",
month = jan,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:22 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Godfrey:2007:AAM,
author = "Parke Godfrey and Ryan Shipley and Jarek Gryz",
title = "Algorithms and analyses for maximal vector
computation",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "5--28",
month = jan,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:22 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The maximal vector problem is to identify the maximals
over a collection of vectors. This arises in many
contexts and, as such, has been well studied. The
problem recently gained renewed attention with skyline
queries for relational databases and with work to
develop skyline algorithms that are external and
relationally well behaved. While many algorithms have
been proposed, how they perform has been unclear. We
study the performance of, and design choices behind,
these algorithms. We prove runtime bounds based on the
number of vectors $N$ and the dimensionality $K$. Early
algorithms based on {\em divide and conquer\/}
established seemingly good average and worst-case
asymptotic runtimes. In fact, the problem can be solved
in \mathcal{O}(KN) average-case (holding $K$ as fixed).
We prove, however, that the performance is quite bad
with respect to $K$. We demonstrate that the more
recent skyline algorithms are better behaved, and can
also achieve $ \mathcal {O}(K N)$ average-case. While
$K$ matters for these, in practice, its effect vanishes
in the asymptotic. We introduce a new external
algorithm, LESS, that is more efficient and better
behaved. We evaluate LESS's effectiveness and
improvement over the field, and prove that its
average-case running time is $ \mathcal {O}(K N)$.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Larson:2007:VMO,
author = "Per-{\AA}ke Larson and Jingren Zhou",
title = "View matching for outer-join views",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "29--53",
month = jan,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:22 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Prior work on computing queries from materialized
views has focused on views defined by expressions
consisting of selection, projection, and inner joins,
with an optional aggregation on top (SPJG views). This
paper provides a view matching algorithm for views that
may also contain outer joins (SPOJG views). The
algorithm relies on a normal form for outer-join
expressions and is not based on bottom-up syntactic
matching of expressions. It handles any combination of
inner and outer joins, deals correctly with SQL bag
semantics, and exploits not-null constraints,
uniqueness constraints and foreign key constraints.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "aggregation; materialized views; outer joins; query
processing; view matching",
}
@Article{Markl:2007:CSE,
author = "V. Markl and P. J. Haas and M. Kutsch and N. Megiddo
and U. Srivastava and T. M. Tran",
title = "Consistent selectivity estimation via maximum
entropy",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "55--76",
month = jan,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:22 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Cost-based query optimizers need to estimate the
selectivity of conjunctive predicates when comparing
alternative query execution plans. To this end,
advanced optimizers use multivariate statistics to
improve information about the joint distribution of
attribute values in a table. The joint distribution for
all columns is almost always too large to store
completely, and the resulting use of partial
distribution information raises the possibility that
multiple, non-equivalent selectivity estimates may be
available for a given predicate. Current optimizers use
cumbersome ad hoc methods to ensure that selectivities
are estimated in a consistent manner. These methods
ignore valuable information and tend to bias the
optimizer toward query plans for which the least
information is available, often yielding poor results.
In this paper we present a novel method for consistent
selectivity estimation based on the principle of
maximum entropy (ME). Our method exploits all available
information and avoids the bias problem. In the absence
of detailed knowledge, the ME approach reduces to
standard uniformity and independence assumptions.
Experiments with our prototype implementation in DB2
UDB show that use of the ME approach can improve the
optimizer's cardinality estimates by orders of
magnitude, resulting in better plan quality and
significantly reduced query execution times. For almost
all queries, these improvements are obtained while
adding only tens of milliseconds to the overall time
required for query optimization.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ghoting:2007:CCF,
author = "Amol Ghoting and Gregory Buehrer and Srinivasan
Parthasarathy and Daehyun Kim and Anthony Nguyen and
Yen-Kuang Chen and Pradeep Dubey",
title = "Cache-conscious frequent pattern mining on modern and
emerging processors",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "77--96",
month = jan,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:22 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Algorithms are typically designed to exploit the
current state of the art in processor technology.
However, as processor technology evolves, said
algorithms are often unable to derive the maximum
achievable performance on these modern architectures.
In this paper, we examine the performance of frequent
pattern mining algorithms on a modern processor. A
detailed performance study reveals that even the best
frequent pattern mining implementations, with highly
efficient memory managers, still grossly under-utilize
a modern processor. The primary performance bottlenecks
are {\em poor data locality\/} and {\em low instruction
level parallelism (ILP)}. We propose a {\em
cache-conscious prefix tree\/} to address this problem.
The resulting tree improves spatial locality and also
enhances the benefits from hardware cache line
prefetching. Furthermore, the design of this data
structure allows the use of {\em path tiling}, a novel
tiling strategy, to improve temporal locality. The
result is an overall speedup of up to 3.2 when compared
with state of the art implementations. We then show how
these algorithms can be improved further by realizing a
non-naive thread-based decomposition that targets {\em
simultaneously multi-threaded processors (SMT)}. A key
aspect of this decomposition is to ensure cache re-use
between threads that are co-scheduled at a fine
granularity. This optimization affords an additional
speedup of 50\%, resulting in an overall speedup of up
to 4.8. The proposed optimizations also provide
performance improvements on SMPs, and will most likely
be beneficial on emerging processors.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "architecture-conscious algorithms; association rule
mining; cache-conscious data mining; frequent itemset
mining; frequent pattern mining",
}
@Article{Lee:2007:ETS,
author = "Yoonkyong Lee and Mayssam Sayyadian and AnHai Doan and
Arnon S. Rosenthal",
title = "{eTuner}: tuning schema matching software using
synthetic scenarios",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "97--122",
month = jan,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:22 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Most recent schema matching systems assemble {\em
multiple components}, each employing a particular
matching technique. The domain user must then {\em
tune\/} the system: select the right component to be
executed and correctly adjust their numerous ``knobs''
(e.g., thresholds, formula coefficients). Tuning is
skill and time intensive, but (as we show) without it
the matching accuracy is significantly inferior. We
describe eTuner, an approach to {\em automatically\/}
tune schema matching systems. Given a schema $S$, we
match $S$ against synthetic schemas, for which the
ground truth mapping is known, and find a tuning that
demonstrably improves the performance of matching $S$
against real schemas. To efficiently search the huge
space of tuning configurations, eTuner works
sequentially, starting with tuning the lowest level
components. To increase the applicability of eTuner, we
develop methods to tune a broad range of matching
components. While the tuning process is completely
automatic, eTuner can also exploit user assistance
(whenever available) to further improve the tuning
quality. We employed eTuner to tune four recently
developed matching systems on several real-world
domains. The results show that eTuner produced tuned
matching systems that achieve higher accuracy than
using the systems with currently possible tuning
methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "compositional approach; machine learning; schema
matching; synthetic schemas; tuning",
}
@Article{Burdick:2007:OUI,
author = "Doug Burdick and Prasad M. Deshpande and T. S. Jayram
and Raghu Ramakrishnan and Shivakumar Vaithyanathan",
title = "{OLAP} over uncertain and imprecise data",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "123--144",
month = jan,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:22 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We extend the OLAP data model to represent data
ambiguity, specifically imprecision and uncertainty,
and introduce an allocation-based approach to the
semantics of aggregation queries over such data. We
identify three natural query properties and use them to
shed light on alternative query semantics. While there
is much work on representing and querying ambiguous
data, to our knowledge this is the first paper to
handle both imprecision and uncertainty in an OLAP
setting.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "aggregation; ambiguous; imprecision; uncertainty",
}
@Article{Haftmann:2007:FER,
author = "Florian Haftmann and Donald Kossmann and Eric Lo",
title = "A framework for efficient regression tests on database
applications",
journal = j-VLDB-J,
volume = "16",
number = "1",
pages = "145--164",
month = jan,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:22 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Regression testing is an important software
maintenance activity to ensure the integrity of a
software after modification. However, most methods and
tools developed for software testing today do not work
well for database applications; these tools only work
well if applications are stateless or tests can be
designed in such a way that they do not alter the
state. To execute tests for database applications
efficiently, the challenge is to control the state of
the database during testing and to order the test runs
such that expensive database {\em reset\/} operations
that bring the database into the right state need to be
executed as seldom as possible. This work devises a
regression testing framework for database applications
so that test runs can be executed in parallel. The goal
is to achieve linear speed-up and/or exploit the
available resources as well as possible. This problem
is challenging because parallel testing needs to
consider both load balancing and controlling the state
of the database. Experimental results show that test
run execution can achieve linear speed-up by using the
proposed framework.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database applications; regression tests",
}
@Article{Tanin:2007:UDQ,
author = "Egemen Tanin and Aaron Harwood and Hanan Samet",
title = "Using a distributed quadtree index in peer-to-peer
networks",
journal = j-VLDB-J,
volume = "16",
number = "2",
pages = "165--178",
month = apr,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:23 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Peer-to-peer (P2P) networks have become a powerful
means for online data exchange. Currently, users are
primarily utilizing these networks to perform
exact-match queries and retrieve complete files.
However, future more data intensive applications, such
as P2P auction networks, P2P job-search networks, P2P
multiplayer games, will require the capability to
respond to more complex queries such as range queries
involving numerous data types including those that have
a spatial component. In this paper, a distributed
quadtree index that adapts the MX-CIF quadtree is
described that enables more powerful accesses to data
in P2P networks. This index has been implemented for
various prototype P2P applications and results of
experiments are presented. Our index is easy to use,
scalable, and exhibits good load-balancing properties.
Similar indices can be constructed for various
multidimensional data types with both spatial and
non-spatial components.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "distributed data structures; peer-to-peer networks;
quadtrees; spatial data structures",
}
@Article{Viqueira:2007:SES,
author = "Jose R. Rios Viqueira and Nikos A. Lorentzos",
title = "{SQL} extension for spatio-temporal data",
journal = j-VLDB-J,
volume = "16",
number = "2",
pages = "179--200",
month = apr,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:23 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "An SQL extension is formalized for the management of
spatio-temporal data, i.e. of spatial data that evolves
with respect to time. The extension is dedicated to
applications such as topography, cartography, and
cadastral systems, hence it considers discrete changes
both in space and in {\em time}. It is based on the
rigid formalization of data types and of SQL
constructs. Data types are defined in terms of time and
{\em spatial quanta}. The SQL constructs are defined in
terms of a kernel of {\em few\/} relational algebra
operations, composed of the well-known operations of
the 1NF model and of two more, {\em Unfold\/} and {\em
Fold}. In conjunction with previous work, it enables
the uniform management of 1NF structures that may
contain not only spatio-temporal but also either purely
temporal or purely spatial or conventional data. The
syntax and semantics of the extension is fully
consistent with the {SQL:2003} standard.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data modelling; spatial databases; spatio-temporal
databases; SQL",
}
@Article{Dai:2007:CDC,
author = "Bi-Ru Dai and Cheng-Ru Lin and Ming-Syan Chen",
title = "Constrained data clustering by depth control and
progressive constraint relaxation",
journal = j-VLDB-J,
volume = "16",
number = "2",
pages = "201--217",
month = apr,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:23 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In order to import the domain knowledge or
application-dependent parameters into the data mining
systems, constraint-based mining has attracted a lot of
research attention recently. In this paper, the
attributes employed to model the constraints are called
constraint attributes and those attributes involved in
the objective function to be optimized are called
optimization attributes. The constrained clustering
considered in this paper is conducted in such a way
that the objective function of optimization attributes
is optimized subject to the condition that the imposed
constraint is satisfied. Explicitly, we address the
problem of constrained clustering with numerical
constraints, in which the constraint attribute values
of any two data items in the same cluster are required
to be within the corresponding constraint range. This
numerical constrained clustering problem, however,
cannot be dealt with by any conventional clustering
algorithms. Consequently, we devise several effective
and efficient algorithms to solve such a clustering
problem. It is noted that due to the intrinsic nature
of the numerical constrained clustering, there is an
order dependency on the process of attaining the
clustering, which in many cases degrades the clustering
results. In view of this, we devise a {\em progressive
constraint relaxation\/} technique to remedy this
drawback and improve the overall performance of
clustering results. Explicitly, by using a smaller
(tighter) constraint range in earlier iterations of
merge, we will have more room to relax the constraint
and seek for better solutions in subsequent iterations.
It is empirically shown that the progressive constraint
relaxation technique is able to improve not only the
execution efficiency but also the clustering quality.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "constrained clustering; data clustering; data mining",
}
@Article{Shen:2007:ADD,
author = "Heng Tao Shen and Xiaofang Zhou and Aoying Zhou",
title = "An adaptive and dynamic dimensionality reduction
method for high-dimensional indexing",
journal = j-VLDB-J,
volume = "16",
number = "2",
pages = "219--234",
month = apr,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:23 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The notorious ``dimensionality curse'' is a well-known
phenomenon for any multi-dimensional indexes attempting
to scale up to high dimensions. One well-known approach
to overcome degradation in performance with respect to
increasing dimensions is to reduce the dimensionality
of the original dataset before constructing the index.
However, identifying the correlation among the
dimensions and effectively reducing them are
challenging tasks. In this paper, we present an
adaptive {\em Multi-level Mahalanobis-based
Dimensionality Reduction\/} (MMDR) technique for
high-dimensional indexing. Our MMDR technique has four
notable features compared to existing methods. First,
it discovers elliptical clusters for more effective
dimensionality reduction by using only the
low-dimensional subspaces. Second, data points in the
different axis systems are indexed using a single $
B^+$-tree. Third, our technique is highly scalable in
terms of data size and dimension. Finally, it is also
dynamic and adaptive to insertions. An extensive
performance study was conducted using both real and
synthetic datasets, and the results show that our
technique not only achieves higher precision, but also
enables queries to be processed efficiently.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "correlated clustering; dimensionality reduction;
high-dimensional indexing; projection; subspace",
}
@Article{He:2007:PCC,
author = "Zhen He and Alonso Marquez",
title = "Path and cache conscious prefetching {(PCCP)}",
journal = j-VLDB-J,
volume = "16",
number = "2",
pages = "235--249",
month = apr,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:23 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Main memory cache performance continues to play an
important role in determining the overall performance
of object-oriented, object-relational and XML
databases. An effective method of improving main memory
cache performance is to prefetch or pre-load pages in
advance to their usage, in anticipation of main memory
cache misses. In this paper we describe a framework for
creating prefetching algorithms with the novel features
of path and cache consciousness. Path consciousness
refers to the use of short sequences of object
references at key points in the reference trace to
identify paths of navigation. Cache consciousness
refers to the use of historical page access knowledge
to guess which pages are likely to be main memory cache
resident most of the time and then assumes these pages
do not exist in the context of prefetching. We have
conducted a number of experiments comparing our
approach against four highly competitive prefetching
algorithms. The results shows our approach outperforms
existing prefetching techniques in some situations
while performing worse in others. We provide guidelines
as to when our algorithm should be used and when others
maybe more desirable.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "caching; clustering; databases; prefetching",
}
@Article{Yu:2007:MBS,
author = "Hailing Yu and Divyakant Agrawal and Amr {El Abbadi}",
title = "{MEMS} based storage architecture for relational
databases",
journal = j-VLDB-J,
volume = "16",
number = "2",
pages = "251--268",
month = apr,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:23 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Due to recent advances in semiconductor manufacturing,
the gap between main memory and disks is constantly
increasing. This leads to a significant performance
bottleneck for Relational Database Management Systems.
Recent advances in nanotechnology have led to the
invention of MicroElectroMechanical Systems (MEMS)
based storage technology to replace disks. In this
paper, we exploit the physical characteristics of
MEMS-based storage devices to develop a placement
scheme for relational data that enables retrieval in
both row-wise and column-wise manner. We develop
algorithms for different relational operations based on
this data layout. Our experimental results and analysis
demonstrate that this data layout not only improves I/O
utilization, but results in better cache performance
for a variety of different relational operations.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data placement; MEMS; relational databases; storage",
}
@Article{Yiannis:2007:CTF,
author = "John Yiannis and Justin Zobel",
title = "Compression techniques for fast external sorting",
journal = j-VLDB-J,
volume = "16",
number = "2",
pages = "269--291",
month = apr,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:23 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "External sorting of large files of records involves
use of disk space to store temporary files, processing
time for sorting, and transfer time between CPU, cache,
memory, and disk. Compression can reduce disk and
transfer costs, and, in the case of external sorts, cut
merge costs by reducing the number of runs. It is
therefore plausible that overall costs of external
sorting could be reduced through use of compression. In
this paper, we propose new compression techniques for
data consisting of sets of records. The best of these
techniques, based on building a trie of variable-length
common strings, provides fast compression and
decompression and allows random access to individual
records. We show experimentally that our trie-based
compression leads to significant reduction in sorting
costs; that is, it is faster to compress the data, sort
it, and then decompress it than to sort the
uncompressed data. While the degree of compression is
not quite as great as can be obtained with adaptive
techniques such as Lempel--Ziv methods, these cannot be
applied to sorting. Our experiments show that, in
comparison to approaches such as Huffman coding of
fixed-length substrings, our novel trie-based method is
faster and provides greater size reductions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "external sorting; query evaluation; semi-static
compression; sorting",
}
@Article{Jermaine:2007:PEF,
author = "Christopher Jermaine and Edward Omiecinski and Wai Gen
Yee",
title = "The partitioned exponential file for database storage
management",
journal = j-VLDB-J,
volume = "16",
number = "4",
pages = "417--437",
month = oct,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:25 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The rate of increase in hard disk storage capacity
continues to outpace the rate of decrease in hard disk
seek time. This trend implies that the value of a seek
is increasing exponentially relative to the value of
storage.\par
With this trend in mind, we introduce the partitioned
exponential file (PE file) which is a generic storage
manager that can be customized for many different types
of data (e.g., numerical, spatial, or temporal). The PE
file is intended for use in environments with intense
update loads and concurrent, analytic queries. Such an
environment may be found, for example, in long-running
scientific applications which can produce petabytes of
data. For example, the proposed Large Synoptic Survey
Telescope [36] will produce 50---100 petabytes of
observational, scientific data over its multi-year
lifetime. This database will never be taken off-line,
so bursty update loads of tens of terabytes per day
must be handled concurrently with data analysis. In the
PE file, data are organized as a series of on-disk
sorts with a careful, global organization. Because the
PE file relies heavily on sequential I/O, only a
fraction of a disk seek is required for a typical
record insertion or retrieval.\par
In addition to describing the PE file, we also detail a
set of benchmarking experiments for T1SM, which is a PE
file customized for use with multi-attribute data
records ordered on a single numerical attribute. In our
benchmarking, we implement and test many competing data
organizations that can be used to index and store such
data, such as the B+-Tree, the LSM-Tree, the Buffer
Tree, the Stepped Merge Method, and the Y-Tree. As
expected, no organization is the best over all
benchmarks, but our experiments show that T1SM is the
best choice in many situations, suggesting that it is
the best overall. Specifically, T1SM performs
exceptionally well in the case of a heavy query
workload that must be handled concurrently with an
intense insertion stream. Our experiments show that
T1SM (and its close cousin, the T2SM storage manager
for spatial data) can handle very heavy mixed workloads
of this type, and still maintain acceptably small query
latencies.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data warehousing; indexing; storage management",
}
@Article{Deligiannakis:2007:DCH,
author = "Antonios Deligiannakis and Yannis Kotidis and Nick
Roussopoulos",
title = "Dissemination of compressed historical information in
sensor networks",
journal = j-VLDB-J,
volume = "16",
number = "4",
pages = "439--461",
month = oct,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:25 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Sensor nodes are small devices that `measure' their
environment and communicate feeds of low-level data
values to a base station for further processing and
archiving. Dissemination of these multi-valued feeds is
challenging because of the limited resources
(processing, bandwidth, energy) available in the nodes
of the network. In this paper, we first describe the
SBR algorithm for compressing multi-valued feeds
containing historical data from each sensor. The key to
our technique is the base signal, a series of values
extracted from the real measurements that is used to
provide piece-wise approximation of the measurements.
While our basic technique exploits correlations among
measurements taken on a single node, we further show
how it can be adapted to exploit correlations among
multiple nodes in a localized setting. Sensor nodes may
form clusters and, within a cluster, a group leader
identifies and coalesces similar measurements taken by
different nodes. This localized mode of operation
further improves the accuracy of the approximation,
typically by a factor from 5 to 15. We provide detailed
experiments of our algorithms and make direct
comparisons against standard approximation techniques
like Wavelets, Histograms and the Discrete Cosine
Transform, on a variety of error metrics and for real
data sets from different domains.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "compression; sensor networks",
}
@Article{Bohm:2007:FRA,
author = "Klemens B{\"o}hm and Erik Buchmann",
title = "Free riding-aware forwarding in {Content-Addressable
Networks}",
journal = j-VLDB-J,
volume = "16",
number = "4",
pages = "463--482",
month = oct,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:25 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Research on P2P data structures has tacitly assumed
that peers readily participate in the work, i.e., are
cooperative. But such participation is voluntary, and
free riding is the dominant strategy. This article
describes a protocol that renders free riding
unattractive, for one particular P2P data structure.
The protocol is based on feedback that adjacent nodes
exchange. This induces transitive logical networks of
nodes that rule out uncooperative peers. The protocol
uses proofs of work to deter free riding. To show that
cooperative behavior dominates, we have come up with a
cost model that quantifies the overall cost of peers,
depending on their degree of cooperativeness and many
other parameters. The cost model tells us that we can
achieve a good discrimination against peers that are
less cooperative, with moderate additional cost for
cooperative peers. Extensive experiments confirm the
validity of our approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "distributed hashtables; free riding; incentives;
peer-to-peer; reputation",
}
@Article{Traina:2007:OFA,
author = "Caetano {Traina, Jr.} and Roberto F. Filho and Agma J.
Traina and Marcos R. Vieira and Christos Faloutsos",
title = "The {Omni-family} of all-purpose access methods: a
simple and effective way to make similarity search more
efficient",
journal = j-VLDB-J,
volume = "16",
number = "4",
pages = "483--505",
month = oct,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:25 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Similarity search operations require executing
expensive algorithms, and although broadly useful in
many new applications, they rely on specific structures
not yet supported by commercial DBMS. In this paper we
discuss the new Omni-technique, which allows to build a
variety of dynamic Metric Access Methods based on a
number of selected objects from the dataset, used as
global reference objects. We call them as the
Omni-family of metric access methods. This technique
enables building similarity search operations on top of
existing structures, significantly improving their
performance, regarding the number of disk access and
distance calculations. Additionally, our methods scale
up well, exhibiting sub-linear behavior with growing
database size.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "index structures; metric access methods; multimedia
databases; similarity search",
}
@Article{Khan:2007:NID,
author = "Latifur Khan and Mamoun Awad and Bhavani
Thuraisingham",
title = "A new intrusion detection system using support vector
machines and hierarchical clustering",
journal = j-VLDB-J,
volume = "16",
number = "4",
pages = "507--521",
month = oct,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:25 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Whenever an intrusion occurs, the security and value
of a computer system is compromised. Network-based
attacks make it difficult for legitimate users to
access various network services by purposely occupying
or sabotaging network resources and services. This can
be done by sending large amounts of network traffic,
exploiting well-known faults in networking services,
and by overloading network hosts. Intrusion Detection
attempts to detect computer attacks by examining
various data records observed in processes on the
network and it is split into two groups, anomaly
detection systems and misuse detection systems. Anomaly
detection is an attempt to search for malicious
behavior that deviates from established normal
patterns. Misuse detection is used to identify
intrusions that match known attack scenarios. Our
interest here is in anomaly detection and our proposed
method is a scalable solution for detecting
network-based anomalies. We use Support Vector Machines
(SVM) for classification. The SVM is one of the most
successful classification algorithms in the data mining
area, but its long training time limits its use. This
paper presents a study for enhancing the training time
of SVM, specifically when dealing with large data sets,
using hierarchical clustering analysis. We use the
Dynamically Growing Self-Organizing Tree (DGSOT)
algorithm for clustering because it has proved to
overcome the drawbacks of traditional hierarchical
clustering algorithms (e.g., hierarchical agglomerative
clustering). Clustering analysis helps find the
boundary points, which are the most qualified data
points to train SVM, between two classes. We present a
new approach of combination of SVM and DGSOT, which
starts with an initial training set and expands it
gradually using the clustering structure produced by
the DGSOT algorithm. We compare our approach with the
Rocchio Bundling technique and random selection in
terms of accuracy loss and training time gain using a
single benchmark real data set. We show that our
proposed variations contribute significantly in
improving the training process of SVM with high
generalization accuracy and outperform the Rocchio
Bundling technique.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Dalvi:2007:EQE,
author = "Nilesh Dalvi and Dan Suciu",
title = "Efficient query evaluation on probabilistic
databases",
journal = j-VLDB-J,
volume = "16",
number = "4",
pages = "523--544",
month = oct,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:25 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We describe a framework for supporting arbitrarily
complex SQL queries with `uncertain' predicates. The
query semantics is based on a probabilistic model and
the results are ranked, much like in Information
Retrieval. Our main focus is query evaluation. We
describe an optimization algorithm that can compute
efficiently most queries. We show, however, that the
data complexity of some queries is \#P-complete, which
implies that these queries do not admit any efficient
evaluation methods. For these queries we describe both
an approximation algorithm and a Monte-Carlo simulation
algorithm.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Croft:2008:ISI,
author = "W. Bruce Croft and Hans-J. Schek",
title = "Introduction to the special issue on database and
information retrieval integration",
journal = j-VLDB-J,
volume = "17",
number = "1",
pages = "1--3",
month = jan,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:26 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Roelleke:2008:MRM,
author = "Thomas Roelleke and Hengzhi Wu and Jun Wang and Hany
Azzam",
title = "Modelling retrieval models in a probabilistic
relational algebra with a new operator: the relational
{Bayes}",
journal = j-VLDB-J,
volume = "17",
number = "1",
pages = "5--37",
month = jan,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:26 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper presents a probabilistic relational
modelling (implementation) of the major probabilistic
retrieval models. Such a high-level implementation is
useful since it supports the ranking of any object, it
allows for the reasoning across structured and
unstructured data, and it gives the software
(knowledge) engineer control over ranking and thus
supports customisation. The contributions of this paper
include the specification of probabilistic SQL (PSQL)
and probabilistic relational algebra (PRA), a new
relational operator for probability estimation (the
relational Bayes), the probabilistic relational
modelling of retrieval models, a comparison of
modelling retrieval with traditional SQL versus
modelling retrieval with PSQL, and a comparison of the
performance of probability estimation with traditional
SQL versus PSQL. The main findings are that the
PSQL/PRA paradigm allows for the description of
advanced retrieval models, is suitable for solving
large-scale retrieval tasks, and outperforms
traditional SQL in terms of abstraction and performance
regarding probability estimation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "DB + IR integration; probabilistic databases;
probabilistic relational modelling; retrieval models",
}
@Article{Schmitt:2008:QDQ,
author = "Ingo Schmitt",
title = "{QQL}: {A DB\&IR Query Language}",
journal = j-VLDB-J,
volume = "17",
number = "1",
pages = "39--56",
month = jan,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:26 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Traditional database query languages are based on set
theory and crisp first order logic. However, many
applications require retrieval-like queries which
return result objects associated with a degree of being
relevant to the query. Historically, retrieval systems
estimate relevance by exploiting hidden object
semantics whereas query processing in database systems
relies on matching select-conditions with attribute
values. Thus, different mechanisms were developed for
database and information retrieval systems. In
consequence, there is a lack of support for queries
involving both retrieval and database search terms. In
this work, we introduce the quantum query language
(QQL). Its underlying unifying theory is based on the
mathematical formalism of quantum mechanics and quantum
logic. Van Rijsbergen already discussed the strong
relation between the formalism of quantum mechanics and
information retrieval. In this work, we interrelate
concepts from database query processing to concepts
from quantum mechanics and logic. As result, we obtain
a common theory which allows us to incorporate
seamlessly retrieval search into traditional database
query processing.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database query language; DB \& IR; information
retrieval",
}
@Article{Lau:2008:MRM,
author = "Ho Lam Lau and Wilfred Ng",
title = "A multi-ranker model for adaptive {XML} searching",
journal = j-VLDB-J,
volume = "17",
number = "1",
pages = "57--80",
month = jan,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:26 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The evolution of computing technology suggests that it
has become more feasible to offer access to Web
information in a ubiquitous way, through various kinds
of interaction devices such as PCs, laptops, palmtops,
and so on. As XML has become a de-facto standard for
exchanging Web data, an interesting and practical
research problem is the development of models and
techniques to satisfy various needs and preferences in
searching XML data. In this paper, we employ a list of
simple XML tagged keywords as a vehicle for searching
XML fragments in a collection of XML documents. In
order to deal with the diversified nature of XML
documents as well as user preferences, we propose a
novel multi-ranker model (MRM), which is able to
abstract a spectrum of important XML properties and
adapt the features to different XML search needs. The
MRM is composed of three ranking levels. The lowest
level consists of two categories of similarity and
granularity features. At the intermediate level, we
define four tailored XML rankers (XRs), which consist
of different lower level features and have different
strengths in searching XML fragments. The XRs are
trained via a learning mechanism called the Ranking
Support Vector Machine in a voting Spy Na{\"\i}ve Bayes
framework (RSSF). The RSSF takes as input a set of
labeled fragments and feature vectors and generates as
output Adaptive Rankers (ARs) in the learning process.
The ARs are defined over the XRs and generated at the
top level of the MRM. We show empirically that the RSSF
is able to improve the MRM significantly in the
learning process that needs only a small set of
training XML fragments. We demonstrate that the trained
MRM is able to bring out the strengths of the XRs in
order to adapt different preferences and queries.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Theobald:2008:TEV,
author = "Martin Theobald and Holger Bast and Debapriyo Majumdar
and Ralf Schenkel and Gerhard Weikum",
title = "{TopX}: efficient and versatile top-$k$ query
processing for semistructured data",
journal = j-VLDB-J,
volume = "17",
number = "1",
pages = "81--115",
month = jan,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:26 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Recent IR extensions to XML query languages such as
Xpath 1.0 Full-Text or the NEXI query language of the
INEX benchmark series reflect the emerging interest in
IR-style ranked retrieval over semistructured data.
TopX is a top-$k$ retrieval engine for text and
semistructured data. It terminates query execution as
soon as it can safely determine the $k$ top-ranked
result elements according to a monotonic score
aggregation function with respect to a multidimensional
query. It efficiently supports vague search on both
content- and structure-oriented query conditions for
dynamic query relaxation with controllable influence on
the result ranking. The main contributions of this
paper unfold into four main points: (1) fully
implemented models and algorithms for ranked XML
retrieval with XPath Full-Text functionality, (2)
efficient and effective top-$k$ query processing for
semistructured data, (3) support for integrating
thesauri and ontologies with statistically quantified
relationships among concepts, leveraged for word-sense
disambiguation and query expansion, and (4) a
comprehensive description of the TopX system, with
performance experiments on large-scale corpora like
TREC TeraByte and INEX Wikipedia.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "content- and structure-aware ranking; cost-based index
access scheduling; DB{\&} dynamic query expansion;
efficient XML full-text search; IR integration;
probabilistic candidate pruning; top-$k$ query
processing",
}
@Article{Simitsis:2008:PUK,
author = "Alkis Simitsis and Georgia Koutrika and Yannis
Ioannidis",
title = "Pr{\'e}cis: from unstructured keywords as queries to
structured databases as answers",
journal = j-VLDB-J,
volume = "17",
number = "1",
pages = "117--149",
month = jan,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:26 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Pr{\'e}cis queries represent a novel way of accessing
data, which combines ideas and techniques from the
fields of databases and information retrieval. They are
free-form, keyword-based, queries on top of relational
databases that generate entire multi-relation
databases, which are logical subsets of the original
ones. A logical subset contains not only items directly
related to the given query keywords but also items
implicitly related to them in various ways, with the
purpose of providing to the user much greater insight
into the original data. In this paper, we lay the
foundations for the concept of logical database subsets
that are generated from pr{\'e}cis queries under a
generalized perspective that removes several
restrictions of previous work. In particular, we extend
the semantics of pr{\'e}cis queries considering that
they may contain multiple terms combined through the
AND, OR, and NOT operators. On the basis of these
extended semantics, we define the concept of a logical
database subset, we identify the one that is most
relevant to a given query, and we provide algorithms
for its generation. Finally, we present an extensive
set of experimental results that demonstrate the
efficiency and benefits of our approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "free-from queries; keyword search; query processing",
}
@Article{Cornacchia:2008:FEI,
author = "Roberto Cornacchia and S{\'a}ndor H{\'e}man and Marcin
Zukowski and Arjen P. Vries and Peter Boncz",
title = "Flexible and efficient {IR} using array databases",
journal = j-VLDB-J,
volume = "17",
number = "1",
pages = "151--168",
month = jan,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:26 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The Matrix Framework is a recent proposal by
Information Retrieval (IR) researchers to flexibly
represent information retrieval models and concepts in
a single multi-dimensional array framework. We provide
computational support for exactly this framework with
the array database system SRAM (Sparse Relational Array
Mapping), that works on top of a DBMS. Information
retrieval models can be specified in its
comprehension-based array query language, in a way that
directly corresponds to the underlying mathematical
formulas. SRAM efficiently stores sparse arrays in
(compressed) relational tables and translates and
optimizes array queries into relational queries. In
this work, we describe a number of array query
optimization rules. To demonstrate their effect on text
retrieval, we apply them in the TREC TeraByte track
(TREC-TB) efficiency task, using the Okapi BM25 model
as our example. It turns out that these optimization
rules enable SRAM to automatically translate the BM25
array queries into the relational equivalent of
inverted list processing including compression, score
materialization and quantization, such as employed by
custom-built IR systems. The use of the
high-performance MonetDB/X100 relational backend, that
provides transparent database compression, allows the
system to achieve very fast response times with good
precision and low resource usage.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "array databases; database compression; information
retrieval; query optimization",
}
@Article{Lockemann:2008:MKR,
author = "Peter C. Lockemann",
title = "In memoriam {Klaus R. Dittrich} (1950---2007)",
journal = j-VLDB-J,
volume = "17",
number = "2",
pages = "169--170",
month = mar,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:27 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Alonso:2008:GEM,
author = "Gustavo Alonso and David Lomet and Umesh Dayal",
title = "Guest {Editors}' message",
journal = j-VLDB-J,
volume = "17",
number = "2",
pages = "171--172",
month = mar,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:27 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gemulla:2008:MBS,
author = "Rainer Gemulla and Wolfgang Lehner and Peter J. Haas",
title = "Maintaining bounded-size sample synopses of evolving
datasets",
journal = j-VLDB-J,
volume = "17",
number = "2",
pages = "173--201",
month = mar,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:27 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Perhaps the most flexible synopsis of a database is a
uniform random sample of the data; such samples are
widely used to speed up processing of analytic queries
and data-mining tasks, enhance query optimization, and
facilitate information integration. The ability to
bound the maximum size of a sample can be very
convenient from a system-design point of view, because
the task of memory management is simplified, especially
when many samples are maintained simultaneously. In
this paper, we study methods for incrementally
maintaining a bounded-size uniform random sample of the
items in a dataset in the presence of an arbitrary
sequence of insertions and deletions. For `stable'
datasets whose size remains roughly constant over time,
we provide a novel sampling scheme, called `random
pairing' (RP), that maintains a bounded-size uniform
sample by using newly inserted data items to compensate
for previous deletions. The RP algorithm is the first
extension of the 45-year-old reservoir sampling
algorithm to handle deletions; RP reduces to the
`passive' algorithm of Babcock et al. when the
insertions and deletions correspond to a moving window
over a data stream. Experiments show that, when
dataset-size fluctuations over time are not too
extreme, RP is the algorithm of choice with respect to
speed and sample-size stability. For `growing'
datasets, we consider algorithms for periodically
resizing a bounded-size random sample upwards. We prove
that any such algorithm cannot avoid accessing the base
data, and provide a novel resizing algorithm that
minimizes the time needed to increase the sample size.
We also show how to merge uniform samples from disjoint
datasets to obtain a uniform sample of the union of the
datasets; the merged sample can be incrementally
maintained. Our new RPMerge algorithm extends the
HRMerge algorithm of Brown and Haas to effectively deal
with deletions, thereby facilitating efficient parallel
sampling.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database sampling; reservoir sampling; sample
maintenance; synopsis",
}
@Article{Yu:2008:XSR,
author = "Cong Yu and H. V. Jagadish",
title = "{XML} schema refinement through redundancy detection
and normalization",
journal = j-VLDB-J,
volume = "17",
number = "2",
pages = "203--223",
month = mar,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:27 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "As XML becomes increasingly popular, XML schema design
has become an increasingly important issue. One of the
central objectives of good schema design is to avoid
data redundancies: redundantly stored information can
lead not just only to a higher data storage cost but
also to increased costs for data transfer and data
manipulation. Furthermore, such data redundancies can
lead to potential update anomalies, rendering the
database inconsistent. One strategy to avoid data
redundancies is to design redundancy-free schema from
the start on the basis of known functional
dependencies. We observe that XML databases are often
`casually designed' and XML FDs may not be determined
in advance. Under such circumstances, discovering XML
data redundancies from the data itself becomes
necessary and is an integral part of the schema
refinement (or re-design) process. We present the
design and implementation of the first system,
DiscoverXFD, for efficient discovery of XML data
redundancies. It employs a novel XML data structure and
introduces a new class of partition-based algorithms.
The XML data redundancies are defined on the basis of a
new notion of XML functional dependency (XML FD) that
(1) extends previous notions by incorporating set
elements into the XML FD specification, and (2)
maintains tuple-based semantics through the novel
concept of Generalized Tree Tuple (GTT). Using this
comprehensive XML FD notion, we introduce a new normal
form (GTT-XNF) for XML documents, and provide
comprehensive comparisons with previous studies. Given
the set of data redundancies (in the form of
redundancy-indicating XML FDs) discovered by
DiscoverXFD, we describe a normalization algorithm for
converting any original XML schema into one in
GTT-XNF.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data redundancy; functional dependency; normal form;
schema design; XML",
}
@Article{Mitra:2008:TKS,
author = "Soumyadeb Mitra and Marianne Winslett and Windsor W.
Hsu and Kevin Chen-Chuan Chang",
title = "Trustworthy keyword search for compliance storage",
journal = j-VLDB-J,
volume = "17",
number = "2",
pages = "225--242",
month = mar,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:27 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Intense regulatory focus on secure retention of
electronic records has led to a need to ensure that
records are trustworthy, i.e., able to provide
irrefutable proof and accurate details of past events.
In this paper, we analyze the requirements for a
trustworthy index to support keyword-based search
queries. We argue that trustworthy index entries must
be durable--the index must be updated when new
documents arrive, and not periodically deleted and
rebuilt. To this end, we propose a scheme for
efficiently updating an inverted index, based on
judicious merging of the posting lists of terms.
Through extensive simulations and experiments with two
real world data sets and workloads, we demonstrate that
the scheme achieves online update speed while
maintaining good query performance. We also present and
evaluate jump indexes, a novel trustworthy and
efficient index for join operations on posting lists
for multi-keyword queries. Jump indexes support insert,
lookup and range queries in time logarithmic in the
number of indexed documents.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "compliance storage; inverted index; jump index",
}
@Article{Benjelloun:2008:DUL,
author = "Omar Benjelloun and Anish Das Sarma and Alon Halevy
and Martin Theobald and Jennifer Widom",
title = "Databases with uncertainty and lineage",
journal = j-VLDB-J,
volume = "17",
number = "2",
pages = "243--264",
month = mar,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:27 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper introduces uldbs, an extension of
relational databases with simple yet expressive
constructs for representing and manipulating both
lineage and uncertainty. Uncertain data and data
lineage are two important areas of data management that
have been considered extensively in isolation, however
many applications require the features in tandem.
Fundamentally, lineage enables simple and consistent
representation of uncertain data, it correlates
uncertainty in query results with uncertainty in the
input data, and query processing with lineage and
uncertainty together presents computational benefits
over treating them separately. We show that the uldb
representation is complete, and that it permits
straightforward implementation of many relational
operations. We define two notions of uldb
minimality--data-minimal and lineage-minimal--and study
minimization of uldb representations under both
notions. With lineage, derived relations are no longer
self-contained: their uncertainty depends on
uncertainty in the base data. We provide an algorithm
for the new operation of extracting a database subset
in the presence of interconnected uncertainty. We also
show how uldbs enable a new approach to query
processing in probabilistic databases. Finally, we
describe the current state of the Trio system, our
implementation of uldbs under development at
Stanford.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "lineage; probabilistic data management; provenance;
uncertainty in databases",
}
@Article{Jeffery:2008:ARM,
author = "Shawn R. Jeffery and Michael J. Franklin and Minos
Garofalakis",
title = "An adaptive {RFID} middleware for supporting
metaphysical data independence",
journal = j-VLDB-J,
volume = "17",
number = "2",
pages = "265--289",
month = mar,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:27 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Sensor devices produce data that are unreliable,
low-level, and seldom able to be used directly by
applications. In this paper, we propose metaphysical
data independence (MDI), a layer of independence that
shields applications from the challenges that arise
when interacting directly with sensor devices. The key
philosophy behind MDI is that applications do not deal
with any aspect of physical device data, but rather
interface with a high-level reconstruction of the
physical world created by a sensor infrastructure. As a
concrete instantiation of MDI in such a sensor
infrastructure, we detail MDI-SMURF, a Radio Frequency
Identification (RFID) middleware system that alleviates
issues associated with using RFID data through adaptive
techniques based on a novel statistical framework.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "data cleaning; RFID technology; sensor-based
applications; statistical sampling",
}
@Article{Parreira:2008:JAP,
author = "Josiane Xavier Parreira and Carlos Castillo and Debora
Donato and Sebastian Michel and Gerhard Weikum",
title = "The {Juxtaposed} approximate {PageRank} method for
robust {PageRank} approximation in a peer-to-peer web
search network",
journal = j-VLDB-J,
volume = "17",
number = "2",
pages = "291--313",
month = mar,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0057-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat May 8 18:33:08 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We present Juxtaposed approximate PageRank (JXP), a
distributed algorithm for computing PageRank-style
authority scores of Web pages on a peer-to-peer (P2P)
network. Unlike previous algorithms, JXP allows peers
to have overlapping content and requires no a priori
knowledge of other peers' content. Our algorithm
combines locally computed authority scores with
information obtained from other peers by means of
random meetings among the peers in the network. This
computation is based on a Markov-chain state-lumping
technique, and iteratively approximates global
authority scores. The algorithm scales with the number
of peers in the network and we show that the JXP scores
converge to the true PageRank scores that one would
obtain with a centralized algorithm. Finally, we show
how to deal with misbehaving peers by extending JXP
with a reputation model.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "link analysis; Markov chain aggregation; peer-to-peer
systems; social reputation; Web graph",
}
@Article{Narayanan:2008:DAQ,
author = "Dushyanth Narayanan and Austin Donnelly and Richard
Mortier and Antony Rowstron",
title = "Delay aware querying with {Seaweed}",
journal = j-VLDB-J,
volume = "17",
number = "2",
pages = "315--331",
month = mar,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:27 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Large highly distributed data sets are poorly
supported by current query technologies. Applications
such as endsystem-based network management are
characterized by data stored on large numbers of
endsystems, with frequent local updates and relatively
infrequent global one-shot queries. The challenges are
scale ($ 10^3 $ to $ 10^9 $ endsystems) and endsystem
unavailability. In such large systems, a significant
fraction of endsystems and their data will be
unavailable at any given time. Existing methods to
provide high data availability despite endsystem
unavailability involve centralizing, redistributing or
replicating the data. At large scale these methods are
not scalable. We advocate a design that trades query
delay for completeness, incrementally returning results
as endsystems become available. We also introduce the
idea of completeness prediction, which provides the
user with explicit feedback about this
delay/completeness trade-off. Completeness prediction
is based on replication of compact data summaries and
availability models. This metadata is orders of
magnitude smaller than the data. Seaweed is a scalable
query infrastructure supporting incremental results,
online in-network aggregation and completeness
prediction. It is built on a distributed hash table
(DHT) but unlike previous DHT based approaches it does
not redistribute data across the network. It exploits
the DHT infrastructure for failure-resilient metadata
replication, query dissemination, and result
aggregation. We analytically compare Seaweed's
scalability against other approaches and also evaluate
the Seaweed prototype running on a large-scale network
simulator driven by real-world traces.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bernstein:2008:IMC,
author = "Philip A. Bernstein and Todd J. Green and Sergey
Melnik and Alan Nash",
title = "Implementing mapping composition",
journal = j-VLDB-J,
volume = "17",
number = "2",
pages = "333--353",
month = mar,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:27 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Mapping composition is a fundamental operation in
metadata driven applications. Given a mapping over
schemas $ \caret {A}_1 $ and $ \caret {A}_2 $ and a
mapping over schemas $ \caret {A}_2 $ and $ \caret
{A}_3 $, the composition problem is to compute an
equivalent mapping over $ \caret {A}_1 $ and $ \caret
{A}_3 $. We describe a new composition algorithm that
targets practical applications. It incorporates view
unfolding. It eliminates as many $ \caret {A}_2 $
symbols as possible, even if not all can be eliminated.
It covers constraints expressed using arbitrary
monotone relational operators and, to a lesser extent,
non-monotone operators. And it introduces the new
technique of left composition. We describe our
implementation, explain how to extend it to support
user-defined operators, and present experimental
results which validate its effectiveness.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "mapping composition; model management; schema
mappings",
}
@Article{Li:2008:ESF,
author = "Yunyao Li and Cong Yu and H. V. Jagadish",
title = "Enabling {Schema-Free XQuery} with meaningful query
focus",
journal = j-VLDB-J,
volume = "17",
number = "3",
pages = "355--377",
month = may,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The widespread adoption of XML holds the promise that
document structure can be exploited to specify precise
database queries. However, users may have only a
limited knowledge of the XML structure, and may be
unable to produce a correct XQuery expression,
especially in the context of a heterogeneous
information collection. The default is to use
keyword-based search and we are all too familiar with
how difficult it is to obtain precise answers by these
means. We seek to address these problems by introducing
the notion of Meaningful Query Focus (MQF) for finding
related nodes within an XML document. MQF enables users
to take full advantage of the preciseness and
efficiency of XQuery without requiring (perfect)
knowledge of the document structure. Such a Schema-Free
XQuery is potentially of value not just to casual users
with partial knowledge of schema, but also to experts
working in data integration or data evolution. In such
a context, a schema-free query, once written, can be
applied universally to multiple data sources that
supply similar content under different schemas, and
applied `forever' as these schemas evolve. Our
experimental evaluation found that it is possible to
express a wide variety of queries in a schema-free
manner and efficiently retrieve correct results over a
broad diversity of schemas. Furthermore, the evaluation
of a schema-free query is not expensive: using a novel
stack-based algorithm we developed for computing MQF,
the overhead is from 1 to 4 times the execution time of
an equivalent schema-aware query. The evaluation cost
of schema-free queries can be further reduced by as
much as 68\% using a selectivity-based algorithm we
develop to enable the integration of MQF operation into
the query pipeline.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "hierarchical; query language; schema; semi-structured;
XML; XQuery",
}
@Article{Yiu:2008:BTI,
author = "Man Lung Yiu and Yufei Tao and Nikos Mamoulis",
title = "The {Bdual-Tree}: indexing moving objects by space
filling curves in the dual space",
journal = j-VLDB-J,
volume = "17",
number = "3",
pages = "379--400",
month = may,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Existing spatiotemporal indexes suffer from either
large update cost or poor query performance, except for
the $ B_x$-tree (the state-of-the-art), which consists
of multiple $ B + $-trees indexing the 1D values
transformed from the (multi-dimensional) moving objects
based on a space filling curve (Hilbert, in
particular). This curve, however, does not consider
object velocities, and as a result, query processing
with a $ B_x$-tree retrieves a large number of false
hits, which seriously compromises its efficiency. It is
natural to wonder `can we obtain better performance by
capturing also the velocity information, using a
Hilbert curve of a higher dimensionality?'. This paper
provides a positive answer by developing the $B$
dual-tree, a novel spatiotemporal access method
leveraging pure relational methodology. We show, with
theoretical evidence, that the $B$ dual-tree indeed
outperforms the $ B_x$-tree in most circumstances.
Furthermore, our technique can effectively answer
progressive spatiotemporal queries, which are poorly
supported by $ B_x$-trees.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access method; space filling curve; spatiotemporal",
}
@Article{Awad:2008:PWS,
author = "Mamoun Awad and Latifur Khan and Bhavani
Thuraisingham",
title = "Predicting {WWW} surfing using multiple evidence
combination",
journal = j-VLDB-J,
volume = "17",
number = "3",
pages = "401--417",
month = may,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The improvement of many applications such as web
search, latency reduction, and personalization/
recommendation systems depends on surfing prediction.
Predicting user surfing paths involves tradeoffs
between model complexity and predictive accuracy. In
this paper, we combine two classification techniques,
namely, the Markov model and Support Vector Machines
(SVM), to resolve prediction using Dempster's rule.
Such fusion overcomes the inability of the Markov model
in predicting the unseen data as well as overcoming the
problem of multiclassification in the case of SVM,
especially when dealing with large number of classes.
We apply feature extraction to increase the power of
discrimination of SVM. In addition, during prediction
we employ domain knowledge to reduce the number of
classifiers for the improvement of accuracy and the
reduction of prediction time. We demonstrate the
effectiveness of our hybrid approach by comparing our
results with widely used techniques, namely, SVM, the
Markov model, and association rule mining.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2008:HBM,
author = "Hai Wang and Kenneth C. Sevcik",
title = "Histograms based on the minimum description length
principle",
journal = j-VLDB-J,
volume = "17",
number = "3",
pages = "419--442",
month = may,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Histograms have been widely used for selectivity
estimation in query optimization, as well as for fast
approximate query answering in many OLAP, data mining,
and data visualization applications. This paper
presents a new family of histograms, the Hierarchical
Model Fitting (HMF) histograms, based on the Minimum
Description Length principle. Rather than having each
bucket of a histogram described by the same type of
model, the HMF histograms employ a local optimal model
for each bucket. The improved effectiveness of the
locally chosen models offsets more than the overhead of
keeping track of the representation of each individual
bucket. Through a set of experiments, we show that the
HMF histograms are capable of providing more accurate
approximations than previously proposed techniques for
many real and synthetic data sets across a variety of
query workloads.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "approximate query answering; data summarization;
histograms; query processing",
}
@Article{Deligiannakis:2008:BCQ,
author = "Antonios Deligiannakis and Yannis Kotidis and Nick
Roussopoulos",
title = "Bandwidth-constrained queries in sensor networks",
journal = j-VLDB-J,
volume = "17",
number = "3",
pages = "443--467",
month = may,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Sensor networks consist of battery-powered wireless
devices that are required to operate unattended for
long periods of time. Thus, reducing energy drain is of
utmost importance when designing algorithms and
applications for such networks. Aggregate queries are
often used by monitoring applications to assess the
status of the network and detect abnormal behavior.
Since radio transmission often constitutes the biggest
factor of energy drain in a node, in this paper we
propose novel algorithms for the evaluation of
bandwidth-constrained queries over sensor networks. The
goal of our techniques is, given a target bandwidth
utilization factor, to program the sensor nodes in a
way that seeks to maximize the accuracy of the produced
query results at the monitoring node, while always
providing strong error guarantees to the monitoring
application. This is a distinct difference of our
framework from previous techniques that only provide
probabilistic guarantees on the accuracy of the query
result. Our algorithms are equally applicable when the
nodes have ample power resources, but bandwidth
consumption needs to be minimized, for instance in
densely distributed networks, to ensure proper
operation of the nodes. Our experiments with real
sensor data show that bandwidth-constrained queries can
substantially reduce the number of messages in the
network while providing very tight error bounds on the
query result.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "in-network aggregation; sensor networks",
}
@Article{Hammad:2008:QPM,
author = "Moustafa A. Hammad and Walid G. Aref and Ahmed K.
Elmagarmid",
title = "Query processing of multi-way stream window joins",
journal = j-VLDB-J,
volume = "17",
number = "3",
pages = "469--488",
month = may,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper introduces a class of join algorithms,
termed W-join, for joining multiple infinite data
streams. W-join addresses the infinite nature of the
data streams by joining stream data items that lie
within a sliding window and that match a certain join
condition. In addition to its general applicability in
stream query processing, W-join can be used to track
the motion of a moving object or detect the propagation
of clouds of hazardous material or pollution spills
over time in a sensor network environment. We describe
two new algorithms for W-join and address variations
and local/global optimizations related to specifying
the nature of the window constraints to fulfill the
posed queries. The performance of the proposed
algorithms is studied experimentally in a prototype
stream database system, using synthetic data streams
and real time-series data. Tradeoffs of the proposed
algorithms and their advantages and disadvantages are
highlighted, given variations in the aggregate arrival
rates of the input data streams and the desired
response times per query.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "multi-way window join; stream query processing",
}
@Article{Luo:2008:FBP,
author = "Qiong Luo and Jeffrey F. Naughton and Wenwei Xue",
title = "Form-based proxy caching for database-backed web
sites: keywords and functions",
journal = j-VLDB-J,
volume = "17",
number = "3",
pages = "489--513",
month = may,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Web caching proxy servers are essential for improving
web performance and scalability, and recent research
has focused on making proxy caching work for
database-backed web sites. In this paper, we explore a
new proxy caching framework that exploits the query
semantics of HTML forms. We identify two common classes
of form-based queries from real-world database-backed
web sites, namely, keyword-based queries and
function-embedded queries. Using typical examples of
these queries, we study two representative caching
schemes within our framework: (i) traditional passive
query caching, and (ii) active query caching, in which
the proxy cache can service a request by evaluating a
query over the contents of the cache. Results from our
experimental implementation show that our form-based
proxy is a general and flexible approach that
efficiently enables active caching schemes for
database-backed web sites. Furthermore, handling query
containment at the proxy yields significant performance
advantages over passive query caching, but extending
the power of the active cache to do full semantic
caching appears to be less generally effective.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "database-backed Web sites; Web proxy caching",
}
@Article{Wang:2008:EAM,
author = "Yida Wang and Ee-Peng Lim and San-Yih Hwang",
title = "Efficient algorithms for mining maximal valid groups",
journal = j-VLDB-J,
volume = "17",
number = "3",
pages = "515--535",
month = may,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A valid group is defined as a group of moving users
that are within a distance threshold from one another
for at least a minimum time duration. Unlike grouping
of users determined by traditional clustering
algorithms, members of a valid group are expected to
stay close to one another during their movement. Each
valid group suggests some social grouping that can be
used in targeted marketing and social network analysis.
The existing valid group mining algorithms are designed
to mine a complete set of valid groups from time series
of user location data, known as the user movement
database. Unfortunately, there are considerable
redundancy in the complete set of valid groups. In this
paper, we therefore address this problem of mining the
set of maximal valid groups. We first extend our
previous valid group mining algorithms to mine maximal
valid groups, leading to AMG and VGMax algorithms. We
further propose the VGBK algorithm based on maximal
clique enumeration to mine the maximal valid groups.
The performance results of these algorithms under
different sets of mining parameters are also
reported.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yu:2008:DMW,
author = "Qi Yu and Xumin Liu and Athman Bouguettaya and Brahim
Medjahed",
title = "Deploying and managing {Web} services: issues,
solutions, and directions",
journal = j-VLDB-J,
volume = "17",
number = "3",
pages = "537--572",
month = may,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Web services are expected to be the key technology in
enabling the next installment of the Web in the form of
the Service Web. In this paradigm shift, Web services
would be treated as first-class objects that can be
manipulated much like data is now manipulated using a
database management system. Hitherto, Web services have
largely been driven by standards. However, there is a
strong impetus for defining a solid and integrated
foundation that would facilitate the kind of
innovations witnessed in other fields, such as
databases. This survey focuses on investigating the
different research problems, solutions, and directions
to deploying Web services that are managed by an
integrated Web Service Management System (WSMS). The
survey identifies the key features of a WSMS and
conducts a comparative study on how current research
approaches and projects fit in.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "interoperability; service-oriented computing; Web
service management system",
}
@Article{Li:2008:EUD,
author = "Changqing Li and Tok Wang Ling and Min Hu",
title = "Efficient updates in dynamic {XML} data: from binary
string to quaternary string",
journal = j-VLDB-J,
volume = "17",
number = "3",
pages = "573--601",
month = may,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "XML query processing based on labeling schemes has
been thoroughly studied in the past several years.
Recently efficient processing of updates in dynamic XML
data has gained more attention. However, all the
existing techniques have high update cost, they cannot
completely avoid re-labeling in XML updates, and they
will increase the label size which will influence the
query performance. Thus, in this paper we propose a
novel Compact Dynamic Binary String (CDBS) encoding to
efficiently process updates. CDBS has two important
properties which form the foundations of this paper:
(1) CDBS supports that CDBS codes can be inserted
between any two consecutive CDBS codes with orders kept
and without re-encoding the existing codes; (2) CDBS is
orthogonal to specific labeling schemes; thus it can be
applied broadly to different labeling schemes or other
applications to efficiently process updates. Moreover,
because CDBS will encounter the overflow problem, we
improve CDBS to Compact Dynamic Quaternary String
(CDQS) encoding which can completely avoid re-labeling
in XML leaf node updates no matter what the labeling
schemes are. Meanwhile, we also discuss how to
efficiently process internal node updates. We report
the experimental results to show that our CDBS and CDQS
are superior to previous approaches to process both
leaf node and internal node updates.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Tao:2007:MRK,
author = "Yufei Tao and Dimitris Papadias and Xiang Lian and
Xiaokui Xiao",
title = "Multidimensional reverse {kNN} search",
journal = j-VLDB-J,
volume = "16",
number = "3",
pages = "293--316",
month = jul,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Given a multidimensional point $q$, a reverse $k$
nearest neighbor (RkNN) query retrieves all the data
points that have $q$ as one of their $k$ nearest
neighbors. Existing methods for processing such queries
have at least one of the following deficiencies: they
(i) do not support arbitrary values of $k$, (ii) cannot
deal efficiently with database updates, (iii) are
applicable only to 2D data but not to higher
dimensionality, and (iv) retrieve only approximate
results. Motivated by these shortcomings, we develop
algorithms for exact RkNN processing with arbitrary
values of $k$ on dynamic, multidimensional datasets.
Our methods utilize a conventional data-partitioning
index on the dataset and do not require any
pre-computation. As a second step, we extend the
proposed techniques to continuous RkNN search, which
returns the RkNN results for every point on a line
segment. We evaluate the effectiveness of our
algorithms with extensive experiments using both real
and synthetic datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "continuous search; reverse nearest neighbor; spatial
database",
}
@Article{Koch:2007:AGS,
author = "Christoph Koch and Stefanie Scherzinger",
title = "Attribute grammars for scalable query processing on
{XML} streams",
journal = j-VLDB-J,
volume = "16",
number = "3",
pages = "317--342",
month = jul,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We introduce the notion of XML Stream Attribute
Grammars (XSAGs). XSAGs are the first scalable query
language for XML streams (running strictly in linear
time with bounded memory consumption independent of the
size of the stream) that allows for actual data
transformations rather than just document filtering.
XSAGs are also relatively easy to use for humans.
Moreover, the XSAG formalism provides a strong
intuition for which queries can or cannot be processed
scalably on streams. We introduce XSAGs together with
the necessary language-theoretic machinery, study their
theoretical properties such as expressiveness and
complexity, and discuss their implementation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "attribute grammars; query languages; stream
processing; XML",
}
@Article{Chan:2007:OES,
author = "Edward P. Chan and Heechul Lim",
title = "Optimization and evaluation of shortest path queries",
journal = j-VLDB-J,
volume = "16",
number = "3",
pages = "343--369",
month = jul,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We investigate the problem of how to evaluate
efficiently a collection of shortest path queries on
massive graphs that are too big to fit in the main
memory. To evaluate a shortest path query efficiently,
we introduce two pruning algorithms. These algorithms
differ on the extent of materialization of shortest
path cost and on how the search space is pruned. By
grouping shortest path queries properly, batch
processing improves the performance of shortest path
query evaluation. Extensive study is also done on
fragment sizes, cache sizes and query types that we
show that affect the performance of a disk-based
shortest path algorithm. The performance and
scalability of proposed techniques are evaluated with
large road systems in the Eastern United States. To
demonstrate that the proposed disk-based algorithms are
viable, we show that their search times are significant
better than that of main-memory Dijkstra's algorithm.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "disk-based algorithms; graph algorithms; graph
pruning; query evaluation and optimization; route
queries; shortest path queries",
}
@Article{Lee:2007:DPI,
author = "Jae-Gil Lee and Kyu-Young Whang and Wook-Shin Han and
Il-Yeol Song",
title = "The dynamic predicate: integrating access control with
query processing in {XML} databases",
journal = j-VLDB-J,
volume = "16",
number = "3",
pages = "371--387",
month = jul,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Recently, access control on XML data has become an
important research topic. Previous research on access
control mechanisms for XML data has focused on
increasing the efficiency of access control itself, but
has not addressed the issue of integrating access
control with query processing. In this paper, we
propose an efficient access control mechanism tightly
integrated with query processing for XML databases. We
present the novel concept of the dynamic predicate $
\caret {A} $ (DP), which represents a dynamically
constructed condition during query execution. A DP is
derived from instance-level authorizations and
constrains accessibility of the elements. The DP allows
us to effectively integrate authorization checking into
the query plan so that unauthorized elements are
excluded in the process of query execution.
Experimental results show that the proposed access
control mechanism improves query processing time
significantly over the state-of-the-art access control
mechanisms. We conclude that the DP is highly effective
in efficiently checking instance-level authorizations
in databases with hierarchical structures.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access control; privacy/security; query processing;
XML databases",
}
@Article{Papazoglou:2007:SOA,
author = "Mike P. Papazoglou and Willem-Jan Heuvel",
title = "Service oriented architectures: approaches,
technologies and research issues",
journal = j-VLDB-J,
volume = "16",
number = "3",
pages = "389--415",
month = jul,
year = "2007",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Service-oriented architectures (SOA) is an emerging
approach that addresses the requirements of loosely
coupled, standards-based, and protocol-independent
distributed computing. Typically business operations
running in an SOA comprise a number of invocations of
these different components, often in an event-driven or
asynchronous fashion that reflects the underlying
business process needs. To build an SOA a highly
distributable communications and integration backbone
is required. This functionality is provided by the
Enterprise Service Bus (ESB) that is an integration
platform that utilizes Web services standards to
support a wide variety of communications patterns over
multiple transport protocols and deliver value-added
capabilities for SOA applications. This paper reviews
technologies and approaches that unify the principles
and concepts of SOA with those of event-based
programming. The paper also focuses on the ESB and
describes a range of functions that are designed to
offer a manageable, standards-based SOA backbone that
extends middleware functionality throughout by
connecting heterogeneous components and systems and
offers integration services. Finally, the paper
proposes an approach to extend the conventional SOA to
cater for essential ESB requirements that include
capabilities such as service orchestration,
`intelligent' routing, provisioning, integrity and
security of message as well as service management. The
layers in this extended SOA, in short xSOA, are used to
classify research issues and current research
activities.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "application and service integration; asynchronous and
event-driven processing; enterprise bus; service
oriented architecture; Web services",
}
@Article{Byun:2008:PBA,
author = "Ji-Won Byun and Ninghui Li",
title = "Purpose based access control for privacy protection in
relational database systems",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "603--619",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this article, we present a comprehensive approach
for privacy preserving access control based on the
notion of purpose. In our model, purpose information
associated with a given data element specifies the
intended use of the data element. A key feature of our
model is that it allows multiple purposes to be
associated with each data element and also supports
explicit prohibitions, thus allowing privacy officers
to specify that some data should not be used for
certain purposes. An important issue addressed in this
article is the granularity of data labeling, i.e., the
units of data with which purposes can be associated. We
address this issue in the context of relational
databases and propose four different labeling schemes,
each providing a different granularity. We also propose
an approach to represent purpose information, which
results in low storage overhead, and we exploit query
modification techniques to support access control based
on purpose information. Another contribution of our
work is that we address the problem of how to determine
the purpose for which certain data are accessed by a
given user. Our proposed solution relies on role-based
access control (RBAC) models as well as the notion of
conditional role which is based on the notions of role
attribute and system attribute.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "access control; privacy; private data management;
purpose",
}
@Article{Karayannidis:2008:HCO,
author = "Nikos Karayannidis and Timos Sellis",
title = "Hierarchical clustering for {OLAP}: the {CUBE File}
approach",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "621--655",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper deals with the problem of physical
clustering of multidimensional data that are organized
in hierarchies on disk in a hierarchy-preserving
manner. This is called hierarchical clustering. A
typical case, where hierarchical clustering is
necessary for reducing I/Os during query evaluation, is
the most detailed data of an OLAP cube. The presence of
hierarchies in the multidimensional space results in an
enormous search space for this problem. We propose a
representation of the data space that results in a
chunk-tree representation of the cube. The model is
adaptive to the cube's extensive sparseness and
provides efficient access to subsets of data based on
hierarchy value combinations. Based on this
representation of the search space we formulate the
problem as a chunk-to-bucket allocation problem, which
is a packing problem as opposed to the linear ordering
approach followed in the literature.\par
We propose a metric to evaluate the quality of
hierarchical clustering achieved (i.e., evaluate the
solutions to the problem) and formulate the problem as
an optimization problem. We prove its NP-Hardness and
provide an effective solution based on a linear time
greedy algorithm. The solution of this problem leads to
the construction of the CUBE File data structure. We
analyze in depth all steps of the construction and
provide solutions for interesting sub-problems arising,
such as the formation of bucket-regions, the storage of
large data chunks and the caching of the upper nodes
(root directory) in main memory.\par
Finally, we provide an extensive experimental
evaluation of the CUBE File's adaptability to the data
space sparseness as well as to an increasing number of
data points. The main result is that the CUBE File is
highly adaptive to even the most sparse data spaces and
for realistic cases of data point cardinalities
provides hierarchical clustering of high quality and
significant space savings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "CUBE File; data cube; hierarchical clustering; OLAP;
Physical data clustering",
}
@Article{Plattner:2008:EDS,
author = "Christian Plattner and Gustavo Alonso and M. Tamer
{\"O}zsu",
title = "Extending {DBMSs} with satellite databases",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "657--682",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we propose an extensible architecture
for database engines where satellite databases are used
to scale out and implement additional functionality for
a centralized database engine. The architecture uses a
middleware layer that offers consistent views and a
single system image over a cluster of machines with
database engines. One of these engines acts as a master
copy while the others are read-only snapshots which we
call satellites. The satellites are lightweight DBMSs
used for scalability and to provide functionality
difficult or expensive to implement in the main engine.
Our approach also supports the dynamic creation of
satellites to be able to autonomously adapt to varying
loads. The paper presents the architecture, discusses
the research problems it raises, and validates its
feasibility with extensive experimental results.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "dynamic satellite creation; extending database
functionality; satellite databases; snapshot
isolation",
}
@Article{Hsieh:2008:DEF,
author = "Ming-Jyh Hsieh and Wei-Guang Teng and Ming-Syan Chen
and Philip S. Yu",
title = "{DAWN}: an efficient framework of {DCT} for data with
error estimation",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "683--702",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "On-line analytical processing (OLAP) has become an
important component in most data warehouse systems and
decision support systems in recent years. In order to
deal with the huge amount of data, highly complex
queries and increasingly strict response time
requirements, approximate query processing has been
deemed a viable solution. Most works in this area,
however, focus on the space efficiency and are unable
to provide quality-guaranteed answers to queries. To
remedy this, in this paper, we propose an efficient
framework of DCT for dAta With error estimatioN, called
DAWN, which focuses on answering range-sum queries from
compressed OP-cubes transformed by DCT. Specifically,
utilizing the techniques of Geometric series and
Euler's formula, we devise a robust summation function,
called the GE function, to answer range queries in
constant time, regardless of the number of data cells
involved. Note that the GE function can estimate the
summation of cosine functions precisely; thus the
quality of the answers is superior to that of previous
works. Furthermore, an estimator of errors based on the
Brown noise assumption (BNA) is devised to provide
tight bounds for answering range-sum queries. Our
experiment results show that the DAWN framework is
scalable to the selectivity of queries and the
available storage space. With GE functions and the BNA
method, the DAWN framework not only delivers high
quality answers for range-sum queries, but also leads
to shorter query response time due to its effectiveness
in error estimation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Atzori:2008:APP,
author = "Maurizio Atzori and Francesco Bonchi and Fosca
Giannotti and Dino Pedreschi",
title = "Anonymity preserving pattern discovery",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "703--727",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "It is generally believed that data mining results do
not violate the anonymity of the individuals recorded
in the source database. In fact, data mining models and
patterns, in order to ensure a required statistical
significance, represent a large number of individuals
and thus conceal individual identities: this is the
case of the minimum support threshold in frequent
pattern mining. In this paper we show that this belief
is ill-founded. By shifting the concept of
$k$-anonymity from the source data to the extracted
patterns, we formally characterize the notion of a
threat to anonymity in the context of pattern
discovery, and provide a methodology to efficiently and
effectively identify all such possible threats that
arise from the disclosure of the set of extracted
patterns. On this basis, we obtain a formal notion of
privacy protection that allows the disclosure of the
extracted knowledge while protecting the anonymity of
the individuals in the source database. Moreover, in
order to handle the cases where the threats to
anonymity cannot be avoided, we study how to eliminate
such threats by means of pattern (not data!) distortion
performed in a controlled way.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "anonymity; frequent pattern mining; individual
privacy; knowledge discovery; privacy preserving data
mining",
}
@Article{Morfonios:2008:SDC,
author = "Konstantinos Morfonios and Yannis Ioannidis",
title = "Supporting the data cube lifecycle: the power of
{ROLAP}",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "729--764",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The lifecycle of a data cube involves efficient
construction and storage, fast query answering, and
incremental updating. Existing ROLAP methods that
implement data cubes are weak with respect to one or
more of the above, focusing mainly on construction and
storage. In this paper, we present a comprehensive
ROLAP solution that addresses efficiently all
functionality in the lifecycle of a cube and can be
implemented easily over existing relational servers. It
is a family of algorithms centered around a purely
ROLAP construction method that provides fast
computation of a fully materialized cube in compressed
form, is incrementally updatable, and exhibits quick
query response times that can be improved by low-cost
indexing and caching. This is demonstrated through
comprehensive experiments on both synthetic and
real-world datasets, whose results have shown great
promise for the performance and scalability potential
of the proposed techniques, with respect to both the
size and dimensionality of the fact table.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "caching; compressed storage; data cube; incremental
updating; indexing; query processing; ROLAP",
}
@Article{Sharifzadeh:2008:OSR,
author = "Mehdi Sharifzadeh and Mohammad Kolahdouzan and Cyrus
Shahabi",
title = "The optimal sequenced route query",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "765--787",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Real-world road-planning applications often result in
the formulation of new variations of the nearest
neighbor (NN) problem requiring new solutions. In this
paper, we study an unexplored form of NN queries named
optimal sequenced route (OSR) query in both vector and
metric spaces. OSR strives to find a route of minimum
length starting from a given source location and
passing through a number of typed locations in a
particular order imposed on the types of the locations.
We first transform the OSR problem into a shortest path
problem on a large planar graph. We show that a classic
shortest path algorithm such as Dijkstra's is
impractical for most real-world scenarios. Therefore,
we propose LORD, a light threshold-based iterative
algorithm, which utilizes various thresholds to prune
the locations that cannot belong to the optimal route.
Then we propose R-LORD, an extension of LORD which uses
R-tree to examine the threshold values more
efficiently. Finally, for applications that cannot
tolerate the Euclidean distance as estimation and
require exact distance measures in metric spaces (e.g.,
road networks) we propose PNE that progressively issues
NN queries on different point types to construct the
optimal route for the OSR query. Our extensive
experiments on both real-world and synthetic datasets
verify that our algorithms significantly outperform a
disk-based variation of the Dijkstra approach in terms
of processing time (up to two orders of magnitude) and
required workspace (up to 90\% reduction on average).",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "nearest neighbor search; spatial databases; trip
planning queries",
}
@Article{Friedman:2008:PAD,
author = "Arik Friedman and Ran Wolff and Assaf Schuster",
title = "Providing $k$-anonymity in data mining",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "789--804",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper we present extended definitions of
$k$-anonymity and use them to prove that a given data
mining model does not violate the $k$-anonymity of the
individuals represented in the learning examples. Our
extension provides a tool that measures the amount of
anonymity retained during data mining. We show that our
model can be applied to various data mining problems,
such as classification, association rule mining and
clustering. We describe two data mining algorithms
which exploit our extension to guarantee they will
generate only $k$-anonymous output, and provide
experimental results for one of them. Finally, we show
that our method contributes new and efficient ways to
anonymize data and preserve patterns during
anonymization.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Harder:2008:VCC,
author = "Theo H{\"a}rder and Andreas B{\"u}hmann",
title = "Value complete, column complete, predicate complete",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "805--826",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Caching is a proven remedy to enhance scalability and
availability of software systems as well as to reduce
latency of user requests. In contrast to Web caching
where single Web objects are accessed and kept ready
somewhere in caches in the user-to-server path,
database caching uses full-fledged database management
systems as caches, close to application servers at the
edge of the Web, to adaptively maintain sets of records
from a remote database and to evaluate queries on them.
We analyze a new class of approaches to database
caching where the extensions of query predicates that
are to be evaluated are constructed by constraints in
the cache. Starting from the key concept of value
completeness, we explore the application of cache
constraints and their implications on query evaluation
correctness and on controllable cache loading called
cache safeness. Furthermore, we identify simple rules
for the design of cache groups and their optimization
before discussing the use of single cache groups and
cache group federations. Finally, we argue that
predicate completeness can be used to develop new
variants of constraint-based database caching.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "cache constraints; database caching; predicate
completeness; query processing",
}
@Article{Ou:2008:EAI,
author = "Jian-Chih Ou and Chang-Hung Lee and Ming-Syan Chen",
title = "Efficient algorithms for incremental {Web} log mining
with dynamic thresholds",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "827--845",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "With the fast increase in Web activities, Web data
mining has recently become an important research topic
and is receiving a significant amount of interest from
both academic and industrial environments. While
existing methods are efficient for the mining of
frequent path traversal patterns from the access
information contained in a log file, these approaches
are likely to over evaluate associations. Explicitly,
most previous studies of mining path traversal patterns
are based on the model of a uniform support threshold,
where a single support threshold is used to determine
frequent traversal patterns without taking into
consideration such important factors as the length of a
pattern, the positions of Web pages, and the importance
of a particular pattern, etc. As a result, a low
support threshold will lead to lots of uninteresting
patterns derived whereas a high support threshold may
cause some interesting patterns with lower supports to
be ignored. In view of this, this paper broadens the
horizon of frequent path traversal pattern mining by
introducing a flexible model of mining Web traversal
patterns with dynamic thresholds. Specifically, we
study and apply the Markov chain model to provide the
determination of support threshold of Web documents;
and further, by properly employing some effective
techniques devised for joining reference sequences, the
proposed algorithm dynamic threshold miner (DTM) not
only possesses the capability of mining with dynamic
thresholds, but also significantly improves the
execution efficiency as well as contributes to the
incremental mining of Web traversal patterns.
Performance of algorithm DTM and the extension of
existing methods is comparatively analyzed with
synthetic and real Web logs. It is shown that the
option of algorithm DTM is very advantageous in
reducing the number of unnecessary rules produced and
leads to prominent performance improvement.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "dynamic support threshold; Web mining path traversal
pattern",
}
@Article{Alagic:2008:GJP,
author = "Suad Alagi{\'c} and Mark Royer",
title = "Genericity in {Java}: persistent and database systems
implications",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "847--878",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Lack of parametric polymorphism has been a major
obstacle for making Java a viable database programming
language. Regrettably, a recently accepted solution for
genericity in Java 5.0 has far-reaching negative
implications for persistent and database systems
because of static and dynamic type violations. Severe
implications occur in typical database transactions
when processing a variety of database collections.
Well-known approaches to persistence in Java, including
Java's own persistence mechanism, do not perform
correctly due to incorrect dynamic type information
that gets promoted to persistence along with objects.
Dynamic checking of types of objects fetched from the
persistent store may now lead to unexpected type
violations. Further problems occur in reflective
transactions as Java Core Reflection now allows dynamic
type violations without detecting them or throwing
standard exceptions. All of this shows that extending
Java with parametric polymorphism has not made Java a
more viable database programming language. Both legacy
systems, such as those based on the Java binding of the
ODMG or JDO, and future Java-related persistent and
database technologies will be affected. The source of
these problems is in an implementation idiom called
type erasure. This paper provides formal proofs of the
above implications of type erasure along with specific
samples of code in Java 5.0 illustrating these
violations. The limitations of the virtual platform and
extensions required for persistent systems to solve
this problem are also elaborated.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Java; object persistence; object-oriented databases;
reflection; transactions; type systems; virtual
platforms",
}
@Article{Vaidya:2008:PPN,
author = "Jaideep Vaidya and Murat Kantarc{\i}o{\u{g}}lu and
Chris Clifton",
title = "Privacy-preserving {Na{\"\i}ve Bayes} classification",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "879--898",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Privacy-preserving data mining--developing models
without seeing the data --- is receiving growing
attention. This paper assumes a privacy-preserving
distributed data mining scenario: data sources
collaborate to develop a global model, but must not
disclose their data to others. The problem of secure
distributed classification is an important one. In many
situations, data is split between multiple
organizations. These organizations may want to utilize
all of the data to create more accurate predictive
models while revealing neither their training
data/databases nor the instances to be classified.
Na{\"\i}ve Bayes is often used as a baseline
classifier, consistently providing reasonable
classification performance. This paper brings
privacy-preservation to that baseline, presenting
protocols to develop a Na{\"\i}ve Bayes classifier on
both vertically as well as horizontally partitioned
data.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Data mining; Distributed computing; Na{\"\i} Privacy;
Security; ve Bayes",
}
@Article{Fu:2008:STW,
author = "Ada Wai-Chee Fu and Eamonn Keogh and Leo Yung Lau and
Chotirat Ann Ratanamahatana and Raymond Chi-Wing Wong",
title = "Scaling and time warping in time series querying",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "899--921",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The last few years have seen an increasing
understanding that dynamic time warping (DTW), a
technique that allows local flexibility in aligning
time series, is superior to the ubiquitous Euclidean
distance for time series classification, clustering,
and indexing. More recently, it has been shown that for
some problems, uniform scaling (US), a technique that
allows global scaling of time series, may just be as
important for some problems. In this work, we note that
for many real world problems, it is necessary to
combine both DTW and US to achieve meaningful results.
This is particularly true in domains where we must
account for the natural variability of human actions,
including biometrics, query by humming,
motion-capture/animation, and handwriting recognition.
We introduce the first technique which can handle both
DTW and US simultaneously, our techniques involve
search pruning by means of a lower bounding technique
and multi-dimensional indexing to speed up the search.
We demonstrate the utility and effectiveness of our
method on a wide range of problems in industry,
medicine, and entertainment.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "dynamic time warping; nearest neighbor search; scaled
and warped matching; subsequence matching; uniform
scaling",
}
@Article{Mouratidis:2008:TBP,
author = "Kyriakos Mouratidis and Dimitris Papadias and Spiros
Papadimitriou",
title = "Tree-based partition querying: a methodology for
computing medoids in large spatial datasets",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "923--945",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Besides traditional domains (e.g., resource
allocation, data mining applications), algorithms for
medoid computation and related problems will play an
important role in numerous emerging fields, such as
location based services and sensor networks. Since the
$k$-medoid problem is NP-hard, all existing work deals
with approximate solutions on relatively small
datasets. This paper aims at efficient methods for very
large spatial databases, motivated by: (1) the high and
ever increasing availability of spatial data, and (2)
the need for novel query types and improved services.
The proposed solutions exploit the intrinsic grouping
properties of a data partition index in order to read
only a small part of the dataset. Compared to previous
approaches, we achieve results of comparable or better
quality at a small fraction of the CPU and I/O costs
(seconds as opposed to hours, and tens of node accesses
instead of thousands). In addition, we study
medoid-aggregate queries, where $k$ is not known in
advance, but we are asked to compute a medoid set that
leads to an average distance close to a user-specified
value. Similarly, medoid-optimization queries aim at
minimizing both the number of medoids $k$ and the
average distance. We also consider the max version for
the aforementioned problems, where the goal is to
minimize the maximum (instead of the average) distance
between any object and its closest medoid. Finally, we
investigate bichromatic and weighted medoid versions
for all query types, as well as, maximum capacity and
dynamic medoids.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "medoid queries; query processing; spatial databases",
}
@Article{Yu:2008:DMP,
author = "Jeffrey Xu Yu and Zhiheng Li and Guimei Liu",
title = "A data mining proxy approach for efficient frequent
itemset mining",
journal = j-VLDB-J,
volume = "17",
number = "4",
pages = "947--970",
month = jul,
year = "2008",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Jun 23 10:51:30 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Data mining has attracted a lot of research efforts
during the past decade. However, little work has been
reported on the efficiency of supporting a large number
of users who issue different data mining queries
periodically when there are new needs and when data is
updated. Our work is motivated by the fact that the
pattern-growth method is one of the most efficient
methods for frequent pattern mining which constructs an
initial tree and mines frequent patterns on top of the
tree. In this paper, we present a data mining proxy
approach that can reduce the I/O costs to construct an
initial tree by utilizing the trees that have already
been resident in memory. The tree we construct is the
smallest for a given data mining query. In addition,
our proxy approach can also reduce CPU cost in mining
patterns, because the cost of mining relies on the
sizes of trees. The focus of the work is to construct
an initial tree efficiently. We propose three tree
operations to construct a tree. With a unique coding
scheme, we can efficiently project subtrees from
on-disk trees or in-memory trees. Our performance study
indicated that the data mining proxy significantly
reduces the I/O cost to construct trees and CPU cost to
mine patterns over the trees constructed.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mokbel:2008:SSL,
author = "Mohamed F. Mokbel and Walid G. Aref",
title = "{SOLE}: scalable on-line execution of continuous
queries on spatio-temporal data streams",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "971--995",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0046-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper presents the scalable on-line execution
(SOLE) algorithm for continuous and on-line evaluation
of concurrent continuous spatio-temporal queries over
data streams. Incoming spatio-temporal data streams are
processed in-memory against a set of outstanding
continuous queries. The SOLE algorithm utilizes the
scarce memory resource efficiently by keeping track of
only the significant objects. In-memory stored objects
are expired (i.e., dropped) from memory once they
become insignificant. SOLE is a scalable algorithm
where all the continuous outstanding queries share the
same buffer pool. In addition, SOLE is presented as a
spatio-temporal join between two input streams, a
stream of spatio-temporal objects and a stream of
spatio-temporal queries. To cope with intervals of high
arrival rates of objects and/or queries, SOLE utilizes
a load-shedding approach where some of the stored
objects are dropped from memory. SOLE is implemented as
a pipelined query operator that can be combined with
traditional query operators in a query execution plan
to support a wide variety of continuous queries.
Performance experiments based on a real implementation
of SOLE inside a prototype of a data stream management
system show the scalability and efficiency of SOLE in
highly dynamic environments.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Pol:2008:MVL,
author = "Abhijit Pol and Christopher Jermaine and Subramanian
Arumugam",
title = "Maintaining very large random samples using the
geometric file",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "997--1018",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0048-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Random sampling is one of the most fundamental data
management tools available. However, most current
research involving sampling considers the problem of
how to use a sample, and not how to compute one. The
implicit assumption is that a 'sample' is a small data
structure that is easily maintained as new data are
encountered, even though simple statistical arguments
demonstrate that very large samples of gigabytes or
terabytes in size can be necessary to provide high
accuracy. No existing work tackles the problem of
maintaining very large, disk-based samples from a data
management perspective, and no techniques now exist for
maintaining very large samples in an online manner from
streaming data. In this paper, we present online
algorithms for maintaining on-disk samples that are
gigabytes or terabytes in size. The algorithms are
designed for streaming data, or for any environment
where a large sample must be maintained online in a
single pass through a data set. The algorithms meet the
strict requirement that the sample always be a true,
statistically random sample (without replacement) of
all of the data processed thus far. We also present
algorithms to retrieve small size random sample from
large disk-based sample which may be used for various
purposes including statistical analyses by a DBMS.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Abiteboul:2008:AXP,
author = "Serge Abiteboul and Omar Benjelloun and Tova Milo",
title = "The {Active XML} project: an overview",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1019--1040",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0049-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper provides an overview of the Active XML
project developed at INRIA over the past five years.
Active XML (AXML, for short), is a declarative
framework that harnesses Web services for distributed
data management, and is put to work in a peer-to-peer
architecture. The model is based on AXML documents,
which are XML documents that may contain embedded calls
to Web services, and on AXML services, which are Web
services capable of exchanging AXML documents. An AXML
peer is a repository of AXML documents that acts both
as a client by invoking the embedded service calls, and
as a server by providing AXML services, which are
generally defined as queries or updates over the
persistent AXML documents. The approach gracefully
combines stored information with data defined in an
intensional manner as well as dynamic information. This
simple, rather classical idea leads to a number of
technically challenging problems, both theoretical and
practical. In this paper, we describe and motivate the
AXML model and language, overview the research results
obtained in the course of the project, and show how all
the pieces come together in our implementation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Data exchange; Intensional information; Web services;
XML",
}
@Article{Buccafurri:2008:EHT,
author = "Francesco Buccafurri and Gianluca Lax and Domenico
Sacc{\`a} and Luigi Pontieri and Domenico Rosaci",
title = "Enhancing histograms by tree-like bucket indices",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1041--1061",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0050-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Histograms are used to summarize the contents of
relations into a number of buckets for the estimation
of query result sizes. Several techniques have been
proposed in the past for determining bucket boundaries
which provide accurate estimations. However, while
search strategies for optimal bucket boundaries are
rather sophisticated, no much attention has been paid
for estimating queries inside buckets and all of the
above techniques adopt naive methods for such an
estimation. This paper focuses on the problem of
improving the estimation inside a bucket once its
boundaries have been fixed. The proposed technique is
based on the addition, to each bucket, of a memory-word
additional information (organized into a tree-like
index), storing approximate cumulative frequencies in a
hierarchical fashion. Both theoretical analysis and
experimental results show that the proposed approach
improves the accuracy of the estimation inside buckets,
w.r.t. both classical approaches (like continuous value
assumption and uniform spread assumption) and a number
of alternative ways to organize the additional
information. The index is later added to
state-of-the-art histograms obtaining the non-obvious
result that despite the spatial overhead which reduces
the number of allowed buckets once the storage space
has been fixed, the original methods are strongly
improved in terms of accuracy.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Approximate OLAP; Histograms; Range query estimation",
}
@Article{Kamra:2008:DAA,
author = "Ashish Kamra and Evimaria Terzi and Elisa Bertino",
title = "Detecting anomalous access patterns in relational
databases",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1063--1077",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0051-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A considerable effort has been recently devoted to the
development of Database Management Systems (DBMS) which
guarantee high assurance and security. An important
component of any strong security solution is
represented by Intrusion Detection (ID) techniques,
able to detect anomalous behavior of applications and
users. To date, however, there have been few ID
mechanisms proposed which are specifically tailored to
function within the DBMS. In this paper, we propose
such a mechanism. Our approach is based on mining SQL
queries stored in database audit log files. The result
of the mining process is used to form profiles that can
model normal database access behavior and identify
intruders. We consider two different scenarios while
addressing the problem. In the first case, we assume
that the database has a Role Based Access Control
(RBAC) model in place. Under a RBAC system permissions
are associated with roles, grouping several users,
rather than with single users. Our ID system is able to
determine role intruders, that is, individuals while
holding a specific role, behave differently than
expected. An important advantage of providing an ID
technique specifically tailored to RBAC databases is
that it can help in protecting against insider threats.
Furthermore, the existence of roles makes our approach
usable even for databases with large user population.
In the second scenario, we assume that there are no
roles associated with users of the database. In this
case, we look directly at the behavior of the users. We
employ clustering algorithms to form concise profiles
representing normal user behavior. For detection, we
either use these clustered profiles as the roles or
employ outlier detection techniques to identify
behavior that deviates from the profiles. Our
preliminary experimental evaluation on both real and
synthetic database traces shows that our methods work
well in practical situations.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Anomaly detection; DBMS; Intrusion detection; RBAC;
User profiles",
}
@Article{Guha:2008:WSH,
author = "Sudipto Guha and Hyoungmin Park and Kyuseok Shim",
title = "Wavelet synopsis for hierarchical range queries with
workloads",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1079--1099",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0052-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Synopses structures and approximate query answering
have become increasingly important in DSS/ OLAP
applications with stringent response time requirements.
Range queries are an important class of problems in
this domain, and have a wide variety of applications
and have been studied in the context of histograms.
However, wavelets have been shown to be quite useful in
several scenarios and in fact their multi-resolution
structure makes them especially appealing for
hierarchical domains. Furthermore the fact that the
Haar wavelet basis has a linear time algorithm for the
computation of coefficients has made the Haar basis one
of the important and widely used synopsis structures.
Very recently optimal algorithms were proposed for the
wavelet synopsis construction problem for
equality/point queries. In this paper we investigate
the problem of optimum Haar wavelet synopsis
construction for range queries with workloads. We
provide optimum algorithms as well as approximation
heuristics and demonstrate the effectiveness of these
algorithms with our extensive experimental evaluation
using synthetic and real-life data sets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Approximate query answers; Data synopses; Query
processing; Wavelet decomposition",
}
@Article{Deng:2008:MRS,
author = "Ke Deng and Xiaofang Zhou and Heng Tao Shen and Qing
Liu and Kai Xu and Xuemin Lin",
title = "A multi-resolution surface distance model for {$k$-NN}
query processing",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1101--1119",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0053-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A spatial k-NN query returns k nearest points in a
point dataset to a given query point. To measure the
distance between two points, most of the literature
focuses on the Euclidean distance or the network
distance. For many applications, such as wildlife
movement, it is necessary to consider the surface
distance, which is computed from the shortest path
along a terrain surface. In this paper, we investigate
the problem of efficient surface k-NN (sk-NN) query
processing. This is an important yet highly challenging
problem because the underlying environment data can be
very large and the computational cost of finding the
shortest path on a surface can be very high. To
minimize the amount of surface data to be used and the
cost of surface distance computation, a
multi-resolution surface distance model is proposed in
this paper to take advantage of monotonic distance
changes when the distances are computed at different
resolution levels. Based on this innovative model,
sk-NN queries can be processed efficiently by accessing
and processing surface data at a just-enough resolution
level within a just-enough search region. Our extensive
performance evaluations using real world datasets
confirm the efficiency of our proposed model.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chuang:2008:PLR,
author = "Kun-Ta Chuang and Jiun-Long Huang and Ming-Syan Chen",
title = "Power-law relationship and self-similarity in the
itemset support distribution: analysis and
applications",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1121--1141",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0054-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we identify and explore that the
power-law relationship and the self-similar phenomenon
appear in the itemset support distribution. The itemset
support distribution refers to the distribution of the
count of itemsets versus their supports. Exploring the
characteristics of these natural phenomena is useful to
many applications such as providing the direction of
tuning the performance of the frequent-itemset mining.
However, due to the explosive number of itemsets, it is
prohibitively expensive to retrieve lots of itemsets
before we identify the characteristics of the itemset
support distribution in targeted data. As such, we also
propose a valid and cost-effective algorithm, called
algorithm PPL, to extract characteristics of the
itemset support distribution. Furthermore, to fully
explore the advantages of our discovery, we also
propose novel mechanisms with the help of PPL to solve
two important problems: (1) determining a subtle
parameter for mining approximate frequent itemsets over
data streams; and (2) determining the sufficient sample
size for mining frequent patterns. As validated in our
experimental results, PPL can efficiently and precisely
identify the characteristics of the itemset support
distribution in various real data. In addition,
empirical studies also demonstrate that our mechanisms
for those two challenging problems are in orders of
magnitude better than previous works, showing the
prominent advantage of PPL to be an important
pre-processing means for mining applications.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Padmanabhan:2008:SDR,
author = "Prasanna Padmanabhan and Le Gruenwald and Anita Vallur
and Mohammed Atiquzzaman",
title = "A survey of data replication techniques for mobile ad
hoc network databases",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1143--1164",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0055-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A mobile ad hoc network (MANET) is a network that
allows mobile servers and clients to communicate in the
absence of a fixed infrastructure. MANET is a fast
growing area of research as it finds use in a variety
of applications. In order to facilitate efficient data
access and update, databases are deployed on MANETs.
These databases that operate on MANETs are referred to
as MANET databases. Since data availability in MANETs
is affected by the mobility and power constraints of
the servers and clients, data in MANETs are replicated.
A number of data replication techniques have been
proposed for MANET databases. This paper identifies
issues involved in MANET data replication and attempts
to classify existing MANET data replication techniques
based on the issues they address. The attributes of the
replication techniques are also tabulated to facilitate
a feature comparison of the existing MANET data
replication works. Parameters and performance metrics
are also presented to measure the performance of MANET
replication techniques. In addition, this paper also
proposes criteria for selecting appropriate data
replication techniques for various application
requirements. Finally, the paper concludes with a
discussion on future research directions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Data replication; Mobile ad hoc network databases;
Mobile databases",
}
@Article{Zhong:2008:GPT,
author = "Sheng Zhong and Zhiqiang Yang",
title = "Guided perturbation: towards private and accurate
mining",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1165--1177",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0056-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "There have been two methods for privacy- preserving
data mining: the perturbation approach and the
cryptographic approach. The perturbation approach is
typically very efficient, but it suffers from a
tradeoff between accuracy and privacy. In contrast, the
cryptographic approach usually maintains accuracy, but
it is more expensive in computation and communication
overhead. We propose a novel perturbation method,
called guided perturbation. Specifically, we focus on a
central problem of privacy-preserving data mining--the
secure scalar product problem of vertically partitioned
data, and give a solution based on guided perturbation,
with good, provable privacy guarantee. Our solution
achieves accuracy comparable to the cryptographic
solutions, while keeping the efficiency of perturbation
solutions. Our experimental results show that it can be
more than one hundred times faster than a typical
cryptographic solution.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Rizzolo:2008:TXM,
author = "Flavio Rizzolo and Alejandro A. Vaisman",
title = "Temporal {XML}: modeling, indexing, and query
processing",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1179--1212",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0058-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper we address the problem of modeling and
implementing temporal data in XML. We propose a data
model for tracking historical information in an XML
document and for recovering the state of the document
as of any given time. We study the temporal constraints
imposed by the data model, and present algorithms for
validating a temporal XML document against these
constraints, along with methods for fixing inconsistent
documents. In addition, we discuss different ways of
mapping the abstract representation into a temporal XML
document, and introduce TXPath, a temporal XML query
language that extends XPath 2.0. In the second part of
the paper, we present our approach for summarizing and
indexing temporal XML documents. In particular we show
that by indexing continuous paths, i.e., paths that are
valid continuously during a certain interval in a
temporal XML graph, we can dramatically increase query
performance. To achieve this, we introduce a new class
of summaries, denoted TSummary, that adds the time
dimension to the well-known path summarization schemes.
Within this framework, we present two new summaries:
LCP and Interval summaries. The indexing scheme,
denoted TempIndex, integrates these summaries with
additional data structures. We give a query processing
strategy based on TempIndex and a type of
ancestor-descendant encoding, denoted temporal interval
encoding. We present a persistent implementation of
TempIndex, and a comparison against a system based on a
non-temporal path index, and one based on DOM. Finally,
we sketch a language for updates, and show that the
cost of updating the index is compatible with
real-world requirements.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Semistructured data; Structural summaries; Temporal
databases; XML; XPath",
}
@Article{Jin:2008:SES,
author = "Liang Jin and Chen Li and Rares Vernica",
title = "{SEPIA}: estimating selectivities of approximate
string predicates in large databases",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1213--1229",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0061-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Many database applications have the emerging need to
support approximate queries that ask for strings that
are similar to a given string, such as 'name similar to
smith' and 'telephone number similar to 412-0964'.
Query optimization needs the selectivity of such an
approximate predicate, i.e., the fraction of records in
the database that satisfy the condition. In this paper,
we study the problem of estimating selectivities of
approximate string predicates. We develop a novel
technique, called Sepia, to solve the problem. Given a
bag of strings, our technique groups the strings into
clusters, builds a histogram structure for each
cluster, and constructs a global histogram. It is based
on the following intuition: given a query string $q$, a
preselected string $p$ in a cluster, and a string $s$
in the cluster, based on the proximity between $q$ and
$p$, and the proximity between $p$ and $s$, we can
obtain a probability distribution from a global
histogram about the similarity between $q$ and $s$. We
give a full specification of the technique using the
edit distance metric. We study challenges in adopting
this technique, including how to construct the
histogram structures, how to use them to do selectivity
estimation, and how to alleviate the effect of
non-uniform errors in the estimation. We discuss how to
extend the techniques to other similarity functions.
Our extensive experiments on real data sets show that
this technique can accurately estimate selectivities of
approximate string predicates.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Approximate; Estimation; Selectivity; SEPIA; String",
}
@Article{Venkateswaran:2008:RBI,
author = "Jayendra Venkateswaran and Tamer Kahveci and
Christopher Jermaine and Deepak Lachwani",
title = "Reference-based indexing for metric spaces with costly
distance measures",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1231--1251",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0062-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We consider the problem of similarity search in
databases with costly metric distance measures. Given
limited main memory, our goal is to develop a
reference-based index that reduces the number of
comparisons in order to answer a query. The idea in
reference-based indexing is to select a small set of
reference objects that serve as a surrogate for the
other objects in the database. We consider novel
strategies for selection of references and assigning
references to database objects. For dynamic databases
with frequent updates, we propose two incremental
versions of the selection algorithm. Our experimental
results show that our selection and assignment methods
far outperform competing methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Earth mover's distance; Edit distance; Metric
measures; Reference-indexing",
}
@Article{Tao:2008:PDW,
author = "Yufei Tao and Xiaokui Xiao",
title = "Primal or dual: which promises faster spatiotemporal
search?",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1253--1270",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0064-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The existing predictive spatiotemporal indexes can be
classified into two categories, depending on whether
they are based on the primal or dual methodology.
Although we have gained considerable empirical
knowledge about various access methods, currently there
is only limited understanding on the theoretical
characteristics of the two methodologies. In fact, the
experimental results in different papers even
contradict each other, regarding the relative
superiority of the primal and dual techniques. This
paper presents a careful study on the query performance
of general primal and dual indexes, and reveals
important insight into the behavior of each technique.
In particular, we mathematically establish the
conditions that determine the superiority of each
methodology, and provide rigorous justification for
well-known observations that have not been properly
explained in the literature. Our analytical findings
also resolve the contradiction in the experiments of
previous work.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Moving objects; Range search; Spatial database;
Theory",
}
@Article{Tao:2008:ETC,
author = "Yufei Tao and Xiaokui Xiao",
title = "Efficient temporal counting with bounded error",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1271--1292",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0066-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper studies aggregate search in transaction
time databases. Specifically, each object in such a
database can be modeled as a horizontal segment, whose
$y$-projection is its search key, and its
$x$-projection represents the period when the key was
valid in history. Given a query timestamp $ q_t$ and a
key range $ \vec {q \_ k}$, a count-query retrieves the
number of objects that are alive at $ q_t$, and their
keys fall in $ \vec {q \_ k}$. We provide a method that
accurately answers such queries, with error less than $
\frac {1}{\varepsilon } + \varepsilon \cdot N \_ {\rm
alive}(q \_ t)$, where $ N {\rm alive}(q_t)$ is the
number of objects alive at time $ q_t$, and $C$ is any
constant in $ (0, 1]$. Denoting the disk page size as
$B$, and $ n = C N / B$, our technique requires $ O(n)$
space, processes any query in $ O(\log_B n)$ time, and
supports each update in $ O(\log_B n)$ amortized I/Os.
As demonstrated by extensive experiments, the proposed
solutions guarantee query results with extremely high
precision (median relative error below 5\%), while
consuming only a fraction of the space occupied by the
existing approaches that promise precise results.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Aggregate search; Approximate query processing;
Temporal database",
}
@Article{Islam:2008:ACB,
author = "Aminul Islam and Diana Inkpen and Iluju Kiringa",
title = "Applications of corpus-based semantic similarity and
word segmentation to database schema matching",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1293--1320",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0067-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we present a method for database schema
matching: the problem of identifying elements of two
given schemas that correspond to each other. Schema
matching is useful in e-commerce exchanges, in data
integration/warehousing, and in semantic web
applications. We first present two corpus-based
methods: one method is for determining the semantic
similarity of two target words and the other is for
automatic word segmentation. Then we present a
name-based element-level database schema matching
method that exploits both the semantic similarity and
the word segmentation methods. Our word similarity
method uses pointwise mutual information (PMI) to sort
lists of important neighbor words of two target words;
the words which are common in both lists are selected
and their PMI values are aggregated to calculate the
relative similarity score. Our word segmentation method
uses corpus type frequency information to choose the
type with maximum length and frequency from
'desegmented' text. It also uses a modified
forward---backward matching technique using maximum
length frequency and entropy rate if any non-matching
portions of the text exist. Finally, we exploit both
the semantic similarity and the word segmentation
methods in our proposed name-based element-level schema
matching method. This method uses a single property
(i.e., element name) for schema matching and
nevertheless achieves a measure score that is
comparable to the methods that use multiple properties
(e.g., element name, text description, data instance,
context description). Our schema matching method also
uses normalized and modified versions of the longest
common subsequence string matching algorithm with
weight factors to allow for a balanced combination. We
validate our methods with experimental studies, the
results of which suggest that these methods can be a
useful addition to the set of existing methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Corpus-based methods; Database schema matching;
Semantic similarity; Word segmentation",
}
@Article{Chuang:2008:MTK,
author = "Kun-Ta Chuang and Jiun-Long Huang and Ming-Syan Chen",
title = "Mining top-k frequent patterns in the presence of the
memory constraint",
journal = j-VLDB-J,
volume = "17",
number = "5",
pages = "1321--1344",
month = aug,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0078-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 10 10:00:50 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We explore in this paper a practicably interesting
mining task to retrieve top-$k$ (closed) itemsets in
the presence of the memory constraint. Specifically, as
opposed to most previous works that concentrate on
improving the mining efficiency or on reducing the
memory size by best effort, we first attempt to specify
the available upper memory size that can be utilized by
mining frequent itemsets. To comply with the upper
bound of the memory consumption, two efficient
algorithms, called MTK and MTK\_Close, are devised for
mining frequent itemsets and closed itemsets,
respectively, without specifying the subtle minimum
support. Instead, users only need to give a more
human-understandable parameter, namely the desired
number of frequent (closed) itemsets $k$. In practice,
it is quite challenging to constrain the memory
consumption while also efficiently retrieving top-$k$
itemsets. To effectively achieve this, MTK and
MTK\_Close are devised as level-wise search algorithms,
where the number of candidates being
generated-and-tested in each database scan will be
limited. A novel search approach, called {\^A}`?-stair
search, is utilized in MTK and MTK\_Close to
effectively assign the available memory for testing
candidate itemsets with various itemset-lengths, which
leads to a small number of required database scans. As
demonstrated in the empirical study on real data and
synthetic data, instead of only providing the
flexibility of striking a compromise between the
execution efficiency and the memory consumption, MTK
and MTK\_Close can both achieve high efficiency and
have a constrained memory bound, showing the prominent
advantage to be practical algorithms of mining frequent
patterns.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Catarci:2008:GES,
author = "Tiziana Catarci and Ren{\'e} J. Miller",
title = "Guest editorial: special issue on metadata
management",
journal = j-VLDB-J,
volume = "17",
number = "6",
pages = "1345--1346",
month = nov,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0112-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 22 09:20:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Atzeni:2008:MIS,
author = "Paolo Atzeni and Paolo Cappellari and Riccardo Torlone
and Philip A. Bernstein and Giorgio Gianforme",
title = "Model-independent schema translation",
journal = j-VLDB-J,
volume = "17",
number = "6",
pages = "1347--1370",
month = nov,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0105-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 22 09:20:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We discuss a proposal for the implementation of the
model management operator ModelGen, which translates
schemas from one model to another, for example from
object-oriented to SQL or from SQL to XML schema
descriptions. The operator can be used to generate
database wrappers (e.g., object-oriented or XML to
relational), default user interfaces (e.g., relational
to forms), or default database schemas from other
representations. The approach translates schemas from a
model to another, within a predefined, but large and
extensible, set of models: given a source schema S
expressed in a source model, and a target model TM, it
generates a schema $ S' $ expressed in TM that is
'equivalent' to $S$. A wide family of models is handled
by using a metamodel in which models can be succinctly
and precisely described. The approach expresses the
translation as Datalog rules and exposes the source and
target of the translation in a generic relational
dictionary. This makes the translation transparent,
easy to customize and model-independent. The proposal
includes automatic generation of translations as
composition of basic steps.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Data models; Model management; Schema translation",
}
@Article{Cudre-Mauroux:2008:PMM,
author = "Philippe Cudr{\'e}-Mauroux and Adriana Budura and
Manfred Hauswirth and Karl Aberer",
title = "{PicShark}: mitigating metadata scarcity through
large-scale {P2P} collaboration",
journal = j-VLDB-J,
volume = "17",
number = "6",
pages = "1371--1384",
month = nov,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0103-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 22 09:20:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "With the commoditization of digital devices, personal
information and media sharing is becoming a key
application on the pervasive Web. In such a context,
data annotation rather than data production is the main
bottleneck. Metadata scarcity represents a major
obstacle preventing efficient information processing in
large and heterogeneous communities. However, social
communities also open the door to new possibilities for
addressing local metadata scarcity by taking advantage
of global collections of resources. We propose to
tackle the lack of metadata in large-scale distributed
systems through a collaborative process leveraging on
both content and metadata. We develop a community-based
and self-organizing system called PicShark in which
information entropy--in terms of missing metadata--is
gradually alleviated through decentralized instance and
schema matching. Our approach focuses on
semi-structured metadata and confines computationally
expensive operations to the edge of the network, while
keeping distributed operations as simple as possible to
ensure scalability. PicShark builds on structured
Peer-to-Peer networks for distributed look-up
operations, but extends the application of
self-organization principles to the propagation of
metadata and the creation of schema mappings. We
demonstrate the practical applicability of our method
in an image sharing scenario and provide experimental
evidences illustrating the validity of our approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Metadata entropy; Metadata heterogeneity; Metadata
scarcity; Peer data management; Peer-to-Peer
collaboration",
}
@Article{Cruz:2008:LFS,
author = "Isabel F. Cruz and Huiyong Xiao",
title = "A layered framework supporting personal information
integration and application design for the semantic
desktop",
journal = j-VLDB-J,
volume = "17",
number = "6",
pages = "1385--1406",
month = nov,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0102-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 22 09:20:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "With the development of inexpensive storage devices,
space usage is no longer a bottleneck for computer
users. However, the increasingly large amount of
personal information poses a critical problem to those
users: traditional file organization in hierarchical
directories may not be suited to the effective
management of personal information because it ignores
the semantic associations therein and bears no
connection with the applications that users will run.
To address such limitations, we present our vision of a
semantic desktop, which relies on the use of ontologies
to annotate and organize data and on the concept of
personal information application (PIA), which is
associated with a user's task. The PIA designer is the
tool that is provided for building a variety of PIAs
consisting of views (e.g., text, list, table, graph),
which are spatially arranged and display interrelated
fragments of the overall personal information. The
semantic organization of the data follows a layered
architecture that models separately the personal
information, the domain data, and the application data.
The network of concepts that ensues from extensive
annotation and explicit associations lends itself well
to rich browsing capabilities and to the formulation of
expressive database-like queries. These queries are
also the basis for the interaction among views of the
PIAs in the same desktop or in networked desktops. In
the latter case, the concept of desktop service
provides for a semantic platform for the integration of
information across different desktops and the web. In
this paper, we present in detail the semantic
organization of the information, the overall system
architecture and implementation aspects, queries and
their processing, PIAs and the PIA designer, including
usability studies on the designer, and the concepts of
semantic navigation in a desktop and of interoperation
in a network of desktops.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Candan:2008:SSE,
author = "K. Sel{\c{c}}uk Candan and Huiping Cao and Yan Qi and
Maria Luisa Sapino",
title = "System support for exploration and expert feedback in
resolving conflicts during integration of metadata",
journal = j-VLDB-J,
volume = "17",
number = "6",
pages = "1407--1444",
month = nov,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0109-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 22 09:20:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A critical reality in integration is that knowledge
obtained from different sources may often be
conflicting. Conflict-resolution, whether performed
during the design phase or during run-time, can be
costly and, if done without a proper understanding of
the usage context, can be ineffective. In this paper,
we propose a novel exploration and feedback-based
approach [FICSR (Pronounced as 'fixer')] to
conflict-resolution when integrating metadata from
different sources. Rather than relying on purely
automated conflict-resolution mechanisms, FICSR brings
the domain expert in the conflict-resolution process
and informs the integration based on the expert's
feedback. In particular, instead of relying on
traditional model based definition of consistency
(which, whenever there are conflicts, picks a possible
world among many), we introduce a ranked interpretation
of the metadata and statements about the metadata. This
not only enables FICSR to avoid committing to an
interpretation too early, but also helps in achieving a
more direct correspondence between the experts'
(subjective) interpretation of the data and the
system's (objective) treatment of the available
alternatives. Consequently, the ranked interpretation
leads to new opportunities for exploratory feedback for
conflict-resolution: within the context of a given
statement of interest, (a) a preliminary ranking of
candidate matches, representing different resolutions
of the conflicts, informs the user about the
alternative interpretations of the metadata, while (b)
user feedback regarding the preferences among
alternatives is exploited to inform the system about
the expert's relevant domain knowledge. The expert's
feedback, then, is used for resolving not only the
conflicts among different sources, but also possible
mis-alignments due to the initial matching phase. To
enable this {(system
\stackrel{\_{informs}}{\longleftrightarrow} user)}
feedback process, we develop data structures and
algorithms for efficient off-line conflict/agreement
analysis of the integrated metadata. We also develop
algorithms for efficient on-line query processing,
candidate result enumeration, validity analysis, and
system feedback. The results are brought together and
evaluated in the Feedback-based InConSistency
Resolution (FICSR) system.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Exploration of alternatives; Feedback-based
conflict-resolution; Metadata integration; System
feedback; Taxonomy; User feedback",
}
@Article{Wang:2008:AXB,
author = "Fusheng Wang and Carlo Zaniolo and Xin Zhou",
title = "{ArchIS}: an {XML}-based approach to transaction-time
temporal database systems",
journal = j-VLDB-J,
volume = "17",
number = "6",
pages = "1445--1463",
month = nov,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0086-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 22 09:20:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Effective support for temporal applications by
database systems represents an important technical
objective that is difficult to achieve since it
requires an integrated solution for several problems,
including (i) expressive temporal representations and
data models, (ii) powerful languages for temporal
queries and snapshot queries, (iii) indexing,
clustering and query optimization techniques for
managing temporal information efficiently, and (iv)
architectures that bring together the different pieces
of enabling technology into a robust system. In this
paper, we present the ArchIS system that achieves these
objectives by supporting a temporally grouped data
model on top of RDBMS. ArchIS' architecture uses (a)
XML to support temporally grouped (virtual)
representations of the database history, (b) XQuery to
express powerful temporal queries on such views, (c)
temporal clustering and indexing techniques for
managing the actual historical data in a relational
database, and (d) SQL/XML for executing the queries on
the XML views as equivalent queries on the relational
database. The performance studies presented in the
paper show that ArchIS is quite effective at storing
and retrieving under complex query conditions the
transaction-time history of relational databases, and
can also assure excellent storage efficiency by
providing compression as an option. This approach
achieves full-functionality transaction-time databases
without requiring temporal extensions in XML or
database standards, and provides critical support to
emerging application areas such as RFID.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Temporal database; Temporal grouping; Temporal query;
XML database; XQuery",
}
@Article{Zhou:2008:DSD,
author = "Yongluan Zhou and Beng Chin Ooi and Kian-Lee Tan",
title = "Disseminating streaming data in a dynamic environment:
an adaptive and cost-based approach",
journal = j-VLDB-J,
volume = "17",
number = "6",
pages = "1465--1483",
month = nov,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0077-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 22 09:20:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In a distributed stream processing system, streaming
data are continuously disseminated from the sources to
the distributed processing servers. To enhance the
dissemination efficiency, these servers are typically
organized into one or more dissemination trees. In this
paper, we focus on the problem of constructing
dissemination trees to minimize the average loss of
fidelity of the system. We observe that existing
heuristic-based approaches can only explore a limited
solution space and hence may lead to sub-optimal
solutions. On the contrary, we propose an adaptive and
cost-based approach. Our cost model takes into account
both the processing cost and the communication cost.
Furthermore, as a distributed stream processing system
is vulnerable to inaccurate statistics, runtime
fluctuations of data characteristics, server workloads,
and network conditions, we have designed our scheme to
be adaptive to these situations: an operational
dissemination tree may be incrementally transformed to
a more cost-effective one. Our adaptive strategy
employs distributed decisions made by the distributed
servers independently based on localized statistics
collected by each server at runtime. For a relatively
static environment, we also propose two static tree
construction algorithms relying on a priori system
statistics. These static trees can also be used as
initial trees in a dynamic environment. We apply our
schemes to both single- and multi-object dissemination.
Our extensive performance study shows that the adaptive
mechanisms are effective in a dynamic context and the
proposed static tree construction algorithms perform
close to optimal in a static environment.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Dissemination trees; Distributed stream processing;
Streaming data dissemination",
}
@Article{Kim:2008:SOF,
author = "Min-Soo Kim and Kyu-Young Whang and Jae-Gil Lee and
Min-Jae Lee",
title = "Structural optimization of a full-text $n$-gram index
using relational normalization",
journal = j-VLDB-J,
volume = "17",
number = "6",
pages = "1485--1507",
month = nov,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0082-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 22 09:20:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "As the amount of text data grows explosively, an
efficient index structure for large text databases
becomes ever important. The $n$-gram inverted index
(simply, the $n$-gram index) has been widely used in
information retrieval or in approximate string matching
due to its two major advantages: language-neutral and
error-tolerant. Nevertheless, the $n$-gram index also
has drawbacks: the size tends to be very large, and the
performance of queries tends to be bad. In this paper,
we propose the two-level $n$-gram inverted index
(simply, the $n$-gram/2L index) that significantly
reduces the size and improves the query performance by
using the relational normalization theory. We first
identify that, in the (full-text) $n$-gram index, there
exists redundancy in the position information caused by
a non-trivial multivalued dependency. The proposed
index eliminates such redundancy by constructing the
index in two levels: the front-end index and the
back-end index. We formally prove that this two-level
construction is identical to the relational
normalization process. We call this process structural
optimization of the $n$-gram index. The $n$-gram/2L
index has excellent properties: (1) it significantly
reduces the size and improves the performance compared
with the $n$-gram index with these improvements
becoming more marked as the database size gets larger;
(2) the query processing time increases only very
slightly as the query length gets longer. Experimental
results using real databases of 1~GB show that the size
of the $n$-gram/2L index is reduced by up to 1.9---2.4
times and, at the same time, the query performance is
improved by up to 13.1 times compared with those of the
$n$-gram index. We also compare the $n$-gram/2L index
with Makinen's compact suffix array (CSA) (Proc. 11th
Annual Symposium on Combinatorial Pattern Matching
pp.~305---319, 2000) stored in disk. Experimental
results show that the $n$-gram/2L index outperforms the
CSA when the query length is short (i.e., less than
15---20), and the CSA is similar to or better than the
$n$-gram/2L index when the query length is long (i.e.,
more than 15---20).",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "$n$-gram; Inverted index; Multivalued dependency; Text
search",
}
@Article{Guha:2008:STO,
author = "Sudipto Guha",
title = "On the space--time of optimal, approximate and
streaming algorithms for synopsis construction
problems",
journal = j-VLDB-J,
volume = "17",
number = "6",
pages = "1509--1535",
month = nov,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0083-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 22 09:20:08 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Synopses construction algorithms have been found to be
of interest in query optimization, approximate query
answering and mining, and over the last few years
several good synopsis construction algorithms have been
proposed. These algorithms have mostly focused on the
running time of the synopsis construction vis-a-vis the
synopsis quality. However the space complexity of
synopsis construction algorithms has not been
investigated as thoroughly. Many of the optimum
synopsis construction algorithms are expensive in
space. For some of these algorithms the space required
to construct the synopsis is significantly larger than
the space required to store the input. These algorithms
rely on the fact that they require a smaller 'working
space' and most of the data can be resident on disc.
The large space complexity of synopsis construction
algorithms is a handicap in several scenarios. In the
case of streaming algorithms, space is a fundamental
constraint. In case of offline optimal or approximate
algorithms, a better space complexity often makes these
algorithms much more attractive by allowing them to run
in main memory and not use disc, or alternately allows
us to scale to significantly larger problems without
running out of space. In this paper, we propose a
simple and general technique that reduces space
complexity of synopsis construction algorithms. As a
consequence we show that the notion of 'working space'
proposed in these contexts is redundant. This technique
can be easily applied to many existing algorithms for
synopsis construction problems. We demonstrate the
performance benefits of our proposal through
experiments on real-life and synthetic data. We believe
that our algorithm also generalizes to a broader range
of dynamic programs beyond synopsis construction.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lakhal:2009:FFE,
author = "Neila Ben Lakhal and Takashi Kobayashi and Haruo
Yokota",
title = "{FENECIA}: failure endurable nested-transaction based
execution of composite {Web} services with incorporated
state analysis",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "1--56",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0076-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Interest in the Web services (WS) composition (WSC)
paradigm is increasing tremendously. A real shift in
distributed computing history is expected to occur when
the dream of implementing Service-Oriented Architecture
(SOA) is realized. However, there is a long way to go
to achieve such an ambitious goal. In this paper, we
support the idea that, when challenging the WSC issue,
the earlier that the inevitability of failures is
recognized and proper failure-handling mechanisms are
defined, from the very early stage of the composite WS
(CWS) specification, the greater are the chances of
achieving a significant gain in dependability. To
formalize this vision, we present the FENECIA (Failure
Endurable Nested-transaction based Execution of
Composite Web services with Incorporated state
Analysis) framework. Our framework approaches the WSC
issue from different points of view to guarantee a high
level of dependability. In particular, it aims at being
simultaneously a failure-handling-devoted CWS
specification, execution, and quality of service (QoS)
assessment approach. In the first section of our
framework, we focus on answering the need for a
specification model tailored for the WS architecture.
To this end, we introduce WS-SAGAS, a new transaction
model. WS-SAGAS introduces key concepts that are not
part of the WS architecture pillars, namely, arbitrary
nesting, state, vitality degree, and compensation, to
specify failure-endurable CWS as a hierarchy of
recursively nested transactions. In addition, to define
the CWS execution semantics, without suffering from the
hindrance of an XML-based notation, we describe a
textual notation that describes a WSC in terms of
definition rules, composability rules, and ordering
rules, and we introduce graphical and formal notations.
These rules provide the solid foundation needed to
formulate the execution semantics of a CWS in terms of
execution correctness verification dependencies. To
ensure dependable execution of the CWS, we present in
the second section of FENECIA our architecture THROWS,
in which the execution control of the resulting CWS is
distributed among engines, discovered dynamically, that
communicate in a peer-to-peer fashion. A dependable
execution is guaranteed in THROWS by keeping track of
the execution progress of a CWS and by enforcing
forward and backward recovery. We concentrate in the
third section of our approach on showing how the
failure consideration is trivial in acquiring more
accurate CWS QoS estimations. We propose a model that
assesses several QoS properties of CWS, which are
specified as WS-SAGAS transactions and executed in
THROWS. We validate our proposal and show its
feasibility and broad applicability by describing an
implemented prototype and a case study.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Composition; Dependability; Distributed execution;
Failure; QoS; Transaction model; Web services",
}
@Article{Sharifzadeh:2009:AVC,
author = "Mehdi Sharifzadeh and Cyrus Shahabi",
title = "Approximate {Voronoi} cell computation on spatial data
streams",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "57--75",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0081-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Several studies have exploited the properties of
Voronoi diagrams to improve the efficiency of
variations of the nearest neighbor search on stored
datasets. However, the significance of Voronoi diagrams
and their basic building blocks, Voronoi cells, has
been neglected when the geometry data is incrementally
becoming available as a data stream. In this paper, we
study the problem of Voronoi cell computation for fixed
2-d site points when the locations of the neighboring
sites arrive as a spatial data stream. We show that the
non-streaming solution to the problem does not meet the
memory requirements of many realistic scenarios over a
sliding window. Hence, we propose AVC-SW, an
approximate streaming algorithm that computes $ (1 +
\epsilon)$-approximations to the actual exact Voronoi
cell in $ O(\kappa)$ where $ \kappa $ is its sample
size. With the sliding window model and random arrival
of points, we show both analytically and experimentally
that for given window size $w$ and parameter $k$,
AVC-SW reduces the expected memory requirements of the
classic algorithm from $ O(w)$ to $ O(k \log (\frac
{w}{k} + 1))$ regardless of the distribution of the
points in the 2-d space. This is a significant
improvement for most of the real-world scenarios where
$ w \gg k$.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Approximation; Sliding window; Spatial data stream;
Voronoi cell",
}
@Article{Vlachos:2009:OPV,
author = "Michail Vlachos and Aris Anagnostopoulos and Olivier
Verscheure and Philip S. Yu",
title = "Online pairing of {VoIP} conversations",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "77--98",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0087-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper answers the following question; given a
multiplicity of evolving 1-way conversations, can a
machine or an algorithm discern the conversational
pairs in an online fashion, without understanding the
content of the communications? Our analysis indicates
that this is possible, and can be achieved just by
exploiting the temporal dynamics inherent in a
conversation. We also show that our findings are
applicable for anonymous and encrypted conversations
over VoIP networks. We achieve this by exploiting the
aperiodic inter-departure time of VoIP packets, hence
trivializing each VoIP stream into a binary
time-series, indicating the voice activity of each
stream. We propose effective techniques that
progressively pair conversing parties with high
accuracy and in a limited amount of time. Our findings
are verified empirically on a dataset consisting of
1,000 conversations. We obtain very high pairing
accuracy that reaches 97\% after 5 min of voice
conversations. Using a modeling approach we also
demonstrate analytically that our result can be
extended over an unlimited number of conversations.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Binary time-series clustering; Conversation pairing;
Stream clustering; Voice-over-IP",
}
@Article{Yao:2009:LMK,
author = "Yuxia Yao and Xueyan Tang and Ee-Peng Lim",
title = "Localized monitoring of {kNN} queries in wireless
sensor networks",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "99--117",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0089-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Wireless sensor networks have been widely used in
civilian and military applications. Primarily designed
for monitoring purposes, many sensor applications
require continuous collection and processing of sensed
data. Due to the limited power supply for sensor nodes,
energy efficiency is a major performance concern in
query processing. In this paper, we focus on continuous
k NN query processing in object tracking sensor
networks. We propose a localized scheme to monitor
nearest neighbors to a query point. The key idea is to
establish a monitoring area for each query so that only
the updates relevant to the query are collected. The
monitoring area is set up when the k NN query is
initially evaluated and is expanded and shrunk on the
fly upon object movement. We analyze the optimal
maintenance of the monitoring area and develop an
adaptive algorithm to dynamically decide when to shrink
the monitoring area. Experimental results show that
establishing a monitoring area for continuous k NN
query processing greatly reduces energy consumption and
prolongs network lifetime.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Paton:2009:AQP,
author = "Norman W. Paton and Jorge Buenabad-Chavez and Mengsong
Chen and Vijayshankar Raman and Garret Swart and
Inderpal Narang and Daniel M. Yellin and Alvaro A.
Fernandes",
title = "Autonomic query parallelization using non-dedicated
computers: an evaluation of adaptivity options",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "119--140",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0090-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Writing parallel programs that can take advantage of
non-dedicated processors is much more difficult than
writing such programs for networks of dedicated
processors. In a non-dedicated environment such
programs must use autonomic techniques to respond to
the unpredictable load fluctuations that prevail in the
computational environment. In adaptive query processing
(AQP), several techniques have been proposed for
dynamically redistributing processor load assignments
throughout a computation to take account of varying
resource capabilities, but we know of no previous study
that compares their performance. This paper presents a
simulation-based evaluation of these autonomic
parallelization techniques in a uniform environment and
compares how well they improve the performance of the
computation. Four published strategies are compared
with a new algorithm that seeks to overcome some
weaknesses identified in the existing approaches. In
addition, we explore the use of techniques from online
algorithms to provide a firm foundation for determining
when to adapt in two of the existing algorithms. The
evaluations identify situations in which each strategy
may be used effectively and in which it should be
avoided.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Park:2009:ESR,
author = "Laurence A. Park and Kotagiri Ramamohanarao",
title = "Efficient storage and retrieval of probabilistic
latent semantic information for information retrieval",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "141--155",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0093-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Probabilistic latent semantic analysis (PLSA) is a
method for computing term and document relationships
from a document set. The probabilistic latent semantic
index (PLSI) has been used to store PLSA information,
but unfortunately the PLSI uses excessive storage space
relative to a simple term frequency index, which causes
lengthy query times. To overcome the storage and speed
problems of PLSI, we introduce the probabilistic latent
semantic thesaurus (PLST); an efficient and effective
method of storing the PLSA information. We show that
through methods such as document thresholding and term
pruning, we are able to maintain the high precision
results found using PLSA while using a very small
percent (0.15\%) of the storage space of PLSI.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Probabilistic latent semantic analysis; Query
expansion; Thesaurus",
}
@Article{Askitis:2009:BTD,
author = "Nikolas Askitis and Justin Zobel",
title = "{B}-tries for disk-based string management",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "157--179",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0094-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A wide range of applications require that large
quantities of data be maintained in sort order on disk.
The B-tree, and its variants, are an efficient
general-purpose disk-based data structure that is
almost universally used for this task. The B-trie has
the potential to be a competitive alternative for the
storage of data where strings are used as keys, but has
not previously been thoroughly described or tested. We
propose new algorithms for the insertion, deletion, and
equality search of variable-length strings in a
disk-resident B-trie, as well as novel splitting
strategies which are a critical element of a practical
implementation. We experimentally compare the B-trie
against variants of B-tree on several large sets of
strings with a range of characteristics. Our results
demonstrate that, although the B-trie uses more memory,
it is faster, more scalable, and requires less disk
space.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "B-tree; Burst trie; Data structures; Secondary
storage; Vocabulary accumulation; Word-level indexing",
}
@Article{Joshi:2009:SBE,
author = "Shantanu Joshi and Christopher Jermaine",
title = "Sampling-based estimators for subset-based queries",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "181--202",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0095-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We consider the problem of using sampling to estimate
the result of an aggregation operation over a
subset-based SQL query, where a subquery is correlated
to an outer query by a NOT EXISTS, NOT IN, EXISTS or IN
clause. We design an unbiased estimator for our query
and prove that it is indeed unbiased. We then provide a
second, biased estimator that makes use of the
superpopulation concept from statistics to minimize the
mean squared error of the resulting estimate. The two
estimators are tested over an extensive set of
experiments.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Aggregate query processing; Approximate query
processing; Sampling",
}
@Article{Sacharidis:2009:HCW,
author = "Dimitris Sacharidis and Antonios Deligiannakis and
Timos Sellis",
title = "Hierarchically compressed wavelet synopses",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "203--231",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0096-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The wavelet decomposition is a proven tool for
constructing concise synopses of large data sets that
can be used to obtain fast approximate answers.
Existing research studies focus on selecting an optimal
set of wavelet coefficients to store so as to minimize
some error metric, without however seeking to reduce
the size of the wavelet coefficients themselves. In
many real data sets the existence of large spikes in
the data values results in many large coefficient
values lying on paths of a conceptual tree structure
known as the error tree. To exploit this fact, we
introduce in this paper a novel compression scheme for
wavelet synopses, termed hierarchically compressed
wavelet synopses, that fully exploits hierarchical
relationships among coefficients in order to reduce
their storage. Our proposed compression scheme allows
for a larger number of coefficients to be stored for a
given space constraint thus resulting in increased
accuracy of the produced synopsis. We propose optimal,
approximate and greedy algorithms for constructing
hierarchically compressed wavelet synopses that
minimize the sum squared error while not exceeding a
given space budget. Extensive experimental results on
both synthetic and real-world data sets validate our
novel compression scheme and demonstrate the
effectiveness of our algorithms against existing
synopsis construction algorithms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Compression; Data streams; Wavelet synopsis",
}
@Article{Theodoratos:2009:CPS,
author = "Dimitri Theodoratos and Pawel Placek and Theodore
Dalamagas and Stefanos Souldatos and Timos Sellis",
title = "Containment of partially specified tree-pattern
queries in the presence of dimension graphs",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "233--254",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0097-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Nowadays, huge volumes of data are organized or
exported in tree-structured form. Querying capabilities
are provided through tree-pattern queries. The need for
querying tree-structured data sources when their
structure is not fully known, and the need to integrate
multiple data sources with different tree structures
have driven, recently, the suggestion of query
languages that relax the complete specification of a
tree pattern. In this paper, we consider a query
language that allows the partial specification of a
tree pattern. Queries in this language range from
structureless keyword-based queries to completely
specified tree patterns. To support the evaluation of
partially specified queries, we use semantically rich
constructs, called dimension graphs, which abstract
structural information of the tree-structured data. We
address the problem of query containment in the
presence of dimension graphs and we provide necessary
and sufficient conditions for query containment. As
checking query containment can be expensive, we suggest
two heuristic approaches for query containment in the
presence of dimension graphs. Our approaches are based
on extracting structural information from the dimension
graph that can be added to the queries while preserving
equivalence with respect to the dimension graph. We
considered both cases: extracting and storing different
types of structural information in advance, and
extracting information on-the-fly (at query time). Both
approaches are implemented, validated, and compared
through experimental evaluation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Partial tree-pattern query; Query containment;
Tree-structured data; XML",
}
@Article{Benjelloun:2009:SGA,
author = "Omar Benjelloun and Hector Garcia-Molina and David
Menestrina and Qi Su and Steven Euijong Whang and
Jennifer Widom",
title = "{Swoosh}: a generic approach to entity resolution",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "255--276",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0098-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We consider the entity resolution (ER) problem (also
known as deduplication, or merge---purge), in which
records determined to represent the same real-world
entity are successively located and merged. We
formalize the generic ER problem, treating the
functions for comparing and merging records as
black-boxes, which permits expressive and extensible ER
solutions. We identify four important properties that,
if satisfied by the match and merge functions, enable
much more efficient ER algorithms. We develop three
efficient ER algorithms: G-Swoosh for the case where
the four properties do not hold, and R-Swoosh and
F-Swoosh that exploit the four properties. F-Swoosh in
addition assumes knowledge of the 'features' (e.g.,
attributes) used by the match function. We
experimentally evaluate the algorithms using comparison
shopping data from Yahoo! Shopping and hotel
information data from Yahoo! Travel. We also show that
R-Swoosh (and F-Swoosh) can be used even when the four
match and merge properties do not hold, if an
'approximate' result is acceptable.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Data cleaning; Entity resolution; Generic entity
resolution",
}
@Article{Ratprasartporn:2009:CBL,
author = "Nattakarn Ratprasartporn and Jonathan Po and Ali
Cakmak and Sulieman Bani-Ahmad and Gultekin Ozsoyoglu",
title = "Context-based literature digital collection search",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "277--301",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0099-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We identify two issues with searching literature
digital collections within digital libraries: (a) there
are no effective paper-scoring and ranking mechanisms.
Without a scoring and ranking system, users are often
forced to scan a large and diverse set of publications
listed as search results and potentially miss the
important ones. (b) Topic diffusion is a common
problem: publications returned by a keyword-based
search query often fall into multiple topic areas, not
all of which are of interest to users. This paper
proposes a new literature digital collection search
paradigm that effectively ranks search outputs, while
controlling the diversity of keyword-based search query
output topics. Our approach is as follows. First,
during pre-querying, publications are assigned into
pre-specified ontology-based contexts, and
query-independent context scores are attached to papers
with respect to the assigned contexts. When a query is
posed, relevant contexts are selected, search is
performed within the selected contexts, context scores
of publications are revised into relevancy scores with
respect to the query at hand and the context that they
are in, and query outputs are ranked within each
relevant context. This way, we (1) minimize query
output topic diversity, (2) reduce query output size,
(3) decrease user time spent scanning query results,
and (4) increase query output ranking accuracy. Using
genomics-oriented PubMed publications as the testbed
and Gene Ontology terms as contexts, our experiments
indicate that the proposed context-based search
approach produces search results with up to 50\% higher
precision, and reduces the query output size by up to
70\%.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Context score; Context-based search; Digital
collections; Ontology; Ranking",
}
@Article{Chiu:2009:EFS,
author = "Ding-Ying Chiu and Yi-Hung Wu and Arbee L. Chen",
title = "Efficient frequent sequence mining by a dynamic
strategy switching algorithm",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "303--327",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0100-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Mining frequent sequences in large databases has been
an important research topic. The main challenge of
mining frequent sequences is the high processing cost
due to the large amount of data. In this paper, we
propose a novel strategy to find all the frequent
sequences without having to compute the support counts
of non-frequent sequences. The previous works prune
candidate sequences based on the frequent sequences
with shorter lengths, while our strategy prunes
candidate sequences according to the non-frequent
sequences with the same lengths. As a result, our
strategy can cooperate with the previous works to
achieve a better performance. We then identify three
major strategies used in the previous works and combine
them with our strategy into an efficient algorithm. The
novelty of our algorithm lies in its ability to
dynamically switch from a previous strategy to our new
strategy in the mining process for a better
performance. Experiment results show that our algorithm
outperforms the previous ones under various parameter
settings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Data mining; Frequent sequence; Sequence comparison;
Strategy switching",
}
@Article{Shen:2009:SII,
author = "Heng Tao Shen and Shouxu Jiang and Kian-Lee Tan and Zi
Huang and Xiaofang Zhou",
title = "Speed up interactive image retrieval",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "329--343",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0101-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In multimedia retrieval, a query is typically
interactively refined towards the 'optimal' answers by
exploiting user feedback. However, in existing work, in
each iteration, the refined query is re-evaluated. This
is not only inefficient but fails to exploit the
answers that may be common between iterations.
Furthermore, it may also take too many iterations to
get the 'optimal' answers. In this paper, we introduce
a new approach called OptRFS (optimizing relevance
feedback search by query prediction) for iterative
relevance feedback search. OptRFS aims to take users to
view the 'optimal' results as fast as possible. It
optimizes relevance feedback search by both shortening
the searching time during each iteration and reducing
the number of iterations. OptRFS predicts the potential
candidates for the next iteration and maintains this
small set for efficient sequential scan. By doing so,
repeated candidate accesses (i.e., random accesses) can
be saved, hence reducing the searching time for the
next iteration. In addition, efficient scan on the
overlap before the next search starts also tightens the
search space with smaller pruning radius. As a step
forward, OptRFS also predicts the 'optimal' query,
which corresponds to 'optimal' answers, based on the
early executed iterations' queries. By doing so, some
intermediate iterations can be saved, hence reducing
the total number of iterations. By taking the
correlations among the early executed iterations into
consideration, OptRFS investigates linear regression,
exponential smoothing and linear exponential smoothing
to predict the next refined query so as to decide the
overlap of candidates between two consecutive
iterations. Considering the special features of
relevance feedback, OptRFS further introduces adaptive
linear exponential smoothing to self-adjust the
parameters for more accurate prediction. We implemented
OptRFS and our experimental study on real life data
sets show that it can reduce the total cost of
relevance feedback search significantly. Some
interesting features of relevance feedback search are
also discovered and discussed.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Image retrieval; Indexing; Query processing; Relevance
feedback",
}
@Article{Wang:2009:SFS,
author = "Shiyuan Wang and Quang Hieu Vu and Beng Chin Ooi and
Anthony K. Tung and Lizhen Xu",
title = "{Skyframe}: a framework for skyline query processing
in peer-to-peer systems",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "345--362",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0104-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper looks at the processing of skyline queries
on peer-to-peer (P2P) networks. We propose Skyframe, a
framework for efficient skyline query processing in P2P
systems, which addresses the challenges of quick
response time, low network communication cost and query
load balancing among peers. Skyframe consists of two
querying methods: one is optimized for network
communication while the other focuses on query response
time. These methods are different in the way in which
the query search space is defined. In particular, the
first method uses a high dominating point that has a
large dominating region to prune the search space to
achieve a low cost in network communication. On the
other hand, the second method relaxes the search space
in order to allow parallel query processing to speed up
query response. Skyframe achieves query load balancing
by both query load conscious data space
splitting/merging during the join/departure of nodes
and dynamic load migration. We further show how to
apply Skyframe to both the P2P systems supporting
multi-dimensional indexing and the P2P systems
supporting single-dimensional indexing. Finally, we
have conducted extensive experiments on both real and
synthetic data sets over two existing P2P systems: CAN
(Ratnasamy in A scalable content-addressable network.
In: Proceedings of SIGCOMM Conference, pp. 161---172,
2001) and BATON (Jagadish et al. in A balanced tree
structure for peer-to-peer networks. In: Proceedings of
VLDB Conference, pp. 661---672, 2005) to evaluate the
effectiveness and scalability of Skyframe.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Framework; Load balancing; Optimization; Peer-to-peer
systems; Skyline query processing",
}
@Article{Mouratidis:2009:PMD,
author = "Kyriakos Mouratidis and Dimitris Sacharidis and
Hweehwa Pang",
title = "Partially materialized digest scheme: an efficient
verification method for outsourced databases",
journal = j-VLDB-J,
volume = "18",
number = "1",
pages = "363--381",
month = jan,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0108-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 15:49:59 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In the outsourced database model, a data owner
publishes her database through a third-party server;
i.e., the server hosts the data and answers user
queries on behalf of the owner. Since the server may
not be trusted, or may be compromised, users need a
means to verify that answers received are both
authentic and complete, i.e., that the returned data
have not been tampered with, and that no qualifying
results have been omitted. We propose a result
verification approach for one-dimensional queries,
called Partially Materialized Digest scheme (PMD), that
applies to both static and dynamic databases. PMD uses
separate indexes for the data and for their associated
verification information, and only partially
materializes the latter. In contrast with previous
work, PMD avoids unnecessary costs when processing
queries that do not request verification, achieving the
performance of an ordinary index (e.g., a B$^+$ -tree).
On the other hand, when an authenticity and
completeness proof is required, PMD outperforms the
existing state-of-the-art technique by a wide margin,
as we demonstrate analytically and experimentally.
Furthermore, we design two verification methods for
spatial queries. The first, termed Merkle R-tree
(MR-tree), extends the conventional approach of
embedding authentication information into the data
index (i.e., an R-tree). The second, called Partially
Materialized KD-tree (PMKD), follows the PMD paradigm
using separate data and verification indexes. An
empirical evaluation with real data shows that the PMD
methodology is superior to the traditional approach for
spatial queries too.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Authentication in outsourced databases; Query result
verification",
}
@Article{Garofalakis:2009:SIB,
author = "Minos Garofalakis and Johannes Gehrke and Divesh
Srivastava",
title = "Special issue: best papers of {VLDB 2007}",
journal = j-VLDB-J,
volume = "18",
number = "2",
pages = "383--384",
month = apr,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0132-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 13 09:15:13 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Abadi:2009:SSV,
author = "Daniel J. Abadi and Adam Marcus and Samuel R. Madden
and Kate Hollenbach",
title = "{SW-Store}: a vertically partitioned {DBMS} for
{Semantic Web} data management",
journal = j-VLDB-J,
volume = "18",
number = "2",
pages = "385--406",
month = apr,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0125-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 13 09:15:13 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Efficient management of RDF data is an important
prerequisite for realizing the Semantic Web vision.
Performance and scalability issues are becoming
increasingly pressing as Semantic Web technology is
applied to real-world applications. In this paper, we
examine the reasons why current data management
solutions for RDF data scale poorly, and explore the
fundamental scalability limitations of these
approaches. We review the state of the art for
improving performance of RDF databases and consider a
recent suggestion, 'property tables'. We then discuss
practically and empirically why this solution has
undesirable features. As an improvement, we propose an
alternative solution: vertically partitioning the RDF
data. We compare the performance of vertical
partitioning with prior art on queries generated by a
Web-based RDF browser over a large-scale (more than 50
million triples) catalog of library data. Our results
show that a vertically partitioned schema achieves
similar performance to the property table technique
while being much simpler to design. Further, if a
column-oriented DBMS (a database architected specially
for the vertically partitioned case) is used instead of
a row-oriented DBMS, another order of magnitude
performance improvement is observed, with query times
dropping from minutes to several seconds. Encouraged by
these results, we describe the architecture of
SW-Store, a new DBMS we are actively building that
implements these techniques to achieve high performance
RDF data management.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Arai:2009:AMT,
author = "Benjamin Arai and Gautam Das and Dimitrios Gunopulos
and Nick Koudas",
title = "Anytime measures for top-$k$ algorithms on exact and
fuzzy data sets",
journal = j-VLDB-J,
volume = "18",
number = "2",
pages = "407--427",
month = apr,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0127-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 13 09:15:13 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Top- k queries on large multi-attribute data sets are
fundamental operations in information retrieval and
ranking applications. In this article, we initiate
research on the anytime behavior of top- k algorithms
on exact and fuzzy data. In particular, given specific
top- k algorithms (TA and TA-Sorted) we are interested
in studying their progress toward identification of the
correct result at any point during the algorithms'
execution. We adopt a probabilistic approach where we
seek to report at any point of operation of the
algorithm the confidence that the top- k result has
been identified. Such a functionality can be a valuable
asset when one is interested in reducing the runtime
cost of top- k computations. We present a thorough
experimental evaluation to validate our techniques
using both synthetic and real data sets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Anytime; Approximate query; Fuzzy data; Top-k",
}
@Article{Chen:2009:AKD,
author = "Bee-Chung Chen and Kristen Lefevre and Raghu
Ramakrishnan",
title = "Adversarial-knowledge dimensions in data privacy",
journal = j-VLDB-J,
volume = "18",
number = "2",
pages = "429--467",
month = apr,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0118-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 13 09:15:13 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Privacy is an important issue in data publishing. Many
organizations distribute non-aggregate personal data
for research, and they must take steps to ensure that
an adversary cannot predict sensitive information
pertaining to individuals with high confidence. This
problem is further complicated by the fact that, in
addition to the published data, the adversary may also
have access to other resources (e.g., public records
and social networks relating individuals), which we
call adversarial knowledge. A robust privacy framework
should allow publishing organizations to analyze data
privacy by means of not only data dimensions (data that
a publishing organization has), but also
adversarial-knowledge dimensions (information not in
the data). In this paper, we first describe a general
framework for reasoning about privacy in the presence
of adversarial knowledge. Within this framework, we
propose a novel multidimensional approach to
quantifying adversarial knowledge. This approach allows
the publishing organization to investigate privacy
threats and enforce privacy requirements in the
presence of various types and amounts of adversarial
knowledge. Our main technical contributions include a
multidimensional privacy criterion that is more
intuitive and flexible than previous approaches to
modeling background knowledge. In addition, we identify
an important congregation property of the
adversarial-knowledge dimensions. Based on this
property, we provide algorithms for measuring
disclosure and sanitizing data that improve
computational efficiency several orders of magnitude
over the best known techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Anonymization; Knowledge expression;
Privacy-preserving data publishing; Probabilistic
inference; Skyline; Worst-case privacy",
}
@Article{Dong:2009:DIU,
author = "Xin Luna Dong and Alon Halevy and Cong Yu",
title = "Data integration with uncertainty",
journal = j-VLDB-J,
volume = "18",
number = "2",
pages = "469--500",
month = apr,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0119-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 13 09:15:13 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper reports our first set of results on
managing uncertainty in data integration. We posit that
data-integration systems need to handle uncertainty at
three levels and do so in a principled fashion. First,
the semantic mappings between the data sources and the
mediated schema may be approximate because there may be
too many of them to be created and maintained or
because in some domains (e.g., bioinformatics) it is
not clear what the mappings should be. Second, the data
from the sources may be extracted using information
extraction techniques and so may yield erroneous data.
Third, queries to the system may be posed with keywords
rather than in a structured form. As a first step to
building such a system, we introduce the concept of
probabilistic schema mappings and analyze their formal
foundations. We show that there are two possible
semantics for such mappings: by-table semantics assumes
that there exists a correct mapping but we do not know
what it is; by-tuple semantics assumes that the correct
mapping may depend on the particular tuple in the
source data. We present the query complexity and
algorithms for answering queries in the presence of
probabilistic schema mappings, and we describe an
algorithm for efficiently computing the top- k answers
to queries in such a setting. Finally, we consider
using probabilistic mappings in the scenario of data
exchange.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Data exchange; Data integration; Probabilistic schema
mapping",
}
@Article{Gedik:2009:CPS,
author = "Bu{\u{g}}ra Gedik and Rajesh R. Bordawekar and Philip
S. Yu",
title = "{CellJoin}: a parallel stream join operator for the
{Cell} processor",
journal = j-VLDB-J,
volume = "18",
number = "2",
pages = "501--519",
month = apr,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0116-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 13 09:15:13 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Low-latency and high-throughput processing are key
requirements of data stream management systems (DSMSs).
Hence, multi-core processors that provide high
aggregate processing capacity are ideal matches for
executing costly DSMS operators. The recently developed
Cell processor is a good example of a heterogeneous
multi-core architecture and provides a powerful
platform for executing data stream operators with
high-performance. On the down side, exploiting the full
potential of a multi-core processor like Cell is often
challenging, mainly due to the heterogeneous nature of
the processing elements, the software managed local
memory at the co-processor side, and the unconventional
programming model in general. In this paper, we study
the problem of scalable execution of windowed stream
join operators on multi-core processors, and
specifically on the Cell processor. By examining
various aspects of join execution flow, we determine
the right set of techniques to apply in order to
minimize the sequential segments and maximize
parallelism. Concretely, we show that basic windows
coupled with low-overhead pointer-shifting techniques
can be used to achieve efficient join window
partitioning, column-oriented join window organization
can be used to minimize scattered data transfers,
delay-optimized double buffering can be used for
effective pipelining, rate-aware batching can be used
to balance join throughput and tuple delay, and finally
single-instruction multiple-data (SIMD) optimized
operator code can be used to exploit data parallelism.
Our experimental results show that, following the
design guidelines and implementation techniques
outlined in this paper, windowed stream joins can
achieve high scalability (linear in the number of
co-processors) by making efficient use of the extensive
hardware parallelism provided by the Cell processor
(reaching data processing rates of $ \approx $ 13 GB/s)
and significantly surpass the performance obtained form
conventional high-end processors (supporting a combined
input stream rate of 2,000 tuples/s using 15 min
windows and without dropping any tuples, resulting in $
\approx $ 8.3 times higher output rate compared to an
SSE implementation on dual 3.2 GHz Intel Xeon).",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Schnaitter:2009:DER,
author = "Karl Schnaitter and Joshua Spiegel and Neoklis
Polyzotis",
title = "Depth estimation for ranking query optimization",
journal = j-VLDB-J,
volume = "18",
number = "2",
pages = "521--542",
month = apr,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0124-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 13 09:15:13 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A relational ranking query uses a scoring function to
limit the results of a conventional query to a small
number of the most relevant answers. The increasing
popularity of this query paradigm has led to the
introduction of specialized rank join operators that
integrate the selection of top tuples with join
processing. These operators access just 'enough' of the
input in order to generate just 'enough' output and can
offer significant speed-ups for query evaluation. The
number of input tuples that an operator accesses is
called the input depth of the operator, and this is the
driving cost factor in rank join processing. This
introduces the important problem of depth estimation,
which is crucial for the costing of rank join operators
during query compilation and thus for their integration
in optimized physical plans. We introduce an estimation
methodology, termed deep, for approximating the input
depths of rank join operators in a physical execution
plan. At the core of deep lies a general, principled
framework that formalizes depth computation in terms of
the joint distribution of scores in the base tables.
This framework results in a systematic estimation
methodology that takes the characteristics of the data
directly into account and thus enables more accurate
estimates. We develop novel estimation algorithms that
provide an efficient realization of the formal deep
framework, and describe their integration on top of the
statistics module of an existing query optimizer. We
validate the performance of deep with an extensive
experimental study on data sets of varying
characteristics. The results verify the effectiveness
of deep as an estimation method and demonstrate its
advantages over previously proposed techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Data statistics; DEEP; Depth estimation; Query
optimization; Relational ranking query; Top-k",
}
@Article{Shao:2009:EKS,
author = "Feng Shao and Lin Guo and Chavdar Botev and Anand
Bhaskar and Muthiah Chettiar and Fan Yang and Jayavel
Shanmugasundaram",
title = "Efficient keyword search over virtual {XML} views",
journal = j-VLDB-J,
volume = "18",
number = "2",
pages = "543--570",
month = apr,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0126-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 13 09:15:13 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Emerging applications such as personalized portals,
enterprise search, and web integration systems often
require keyword search over semi-structured views.
However, traditional information retrieval techniques
are likely to be expensive in this context because they
rely on the assumption that the set of documents being
searched is materialized. In this paper, we present a
system architecture and algorithm that can efficiently
evaluate keyword search queries over virtual
(unmaterialized) XML views. An interesting aspect of
our approach is that it exploits indices present on the
base data and thereby avoids materializing large parts
of the view that are not relevant to the query results.
Another feature of the algorithm is that by solely
using indices, we can still score the results of
queries over the virtual view, and the resulting scores
are the same as if the view was materialized. Our
performance evaluation using the INEX data set in the
Quark (Bhaskar et al. in Quark: an efficient XQuery
full-text implementation. In: SIGMOD, 2006) open-source
XML database system indicates that the proposed
approach is scalable and efficient.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Document projections; Document pruning; Keyword
search; Top-K; XML views",
}
@Article{Wu:2009:GEV,
author = "Mingxi Wu and Chris Jermaine",
title = "Guessing the extreme values in a data set: a
{Bayesian} method and its applications",
journal = j-VLDB-J,
volume = "18",
number = "2",
pages = "571--597",
month = apr,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0133-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 13 09:15:13 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "For a large number of data management problems, it
would be very useful to be able to obtain a few samples
from a data set, and to use the samples to guess the
largest (or smallest) value in the entire data set.
Min/max online aggregation, Top-k query processing,
outlier detection, and distance join are just a few
possible applications. This paper details a
statistically rigorous, Bayesian approach to attacking
this problem. Just as importantly, we demonstrate the
utility of our approach by showing how it can be
applied to four specific problems that arise in the
context of data management.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Bayesian; Extreme values; Monte Carlo; Online
aggregation; Sampling",
}
@Article{Hill:2009:ROJ,
author = "Gerhard Hill and Andrew Ross",
title = "Reducing outer joins",
journal = j-VLDB-J,
volume = "18",
number = "3",
pages = "599--610",
month = jun,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0110-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:55:19 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We present a method for transforming some outer joins
to inner joins and describe a generalized semijoin
reduction technique. The first part of the paper shows
how to transform a given outer join query whose join
graph is a tree to an equivalent inner join query. The
method uses derived relations and join predicates.
Derived relations contain columns corresponding to join
conditions and may have virtual row identifiers, rows
and attribute values. The constructed inner join query,
after elimination of virtual row identifiers, has the
same join tuples as the outer join query. Both the
theoretical maximum number of virtual rows and the
average number in practice are shown to be low. The
method confines consideration of the non-associativity
of outer joins to a single step. The second part of the
paper generalizes to outer joins the well known
technique of semijoin reduction of inner joins. It does
so by defining the notions of influencing and needing,
and using them to define full reduction and reduction
plans. The technique is applied here to perform one
step of the method presented in the first part.
Semijoin reduction is useful in practice for executing
join queries in distributed databases.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Efficient join evaluation; Join transformation; Outer
join evaluation; Semijoin reduction; Virtual row
method",
}
@Article{Keogh:2009:SEI,
author = "Eamonn Keogh and Li Wei and Xiaopeng Xi and Michail
Vlachos and Sang-Hee Lee and Pavlos Protopapas",
title = "Supporting exact indexing of arbitrarily rotated
shapes and periodic time series under {Euclidean} and
warping distance measures",
journal = j-VLDB-J,
volume = "18",
number = "3",
pages = "611--630",
month = jun,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0111-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:55:19 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Shape matching and indexing is important topic in its
own right, and is a fundamental subroutine in most
shape data mining algorithms. Given the ubiquity of
shape, shape matching is an important problem with
applications in domains as diverse as biometrics,
industry, medicine, zoology and anthropology. The
distance/similarity measure for used for shape matching
must be invariant to many distortions, including scale,
offset, noise, articulation, partial occlusion, etc.
Most of these distortions are relatively easy to
handle, either in the representation of the data or in
the similarity measure used. However, rotation
invariance is noted in the literature as being an
especially difficult challenge. Current approaches
typically try to achieve rotation invariance in the
representation of the data, at the expense of
discrimination ability, or in the distance measure, at
the expense of efficiency. In this work, we show that
we can take the slow but accurate approaches and
dramatically speed them up. On real world problems our
technique can take current approaches and make them
four orders of magnitude faster without false
dismissals. Moreover, our technique can be used with
any of the dozens of existing shape representations and
with all the most popular distance measures including
Euclidean distance, dynamic time warping and Longest
Common Subsequence. We further show that our indexing
technique can be used to index star light curves, an
important type of astronomical data, without
modification.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Dynamic time warping; Indexing; Shape",
}
@Article{Yang:2009:AIO,
author = "Yin Yang and Stavros Papadopoulos and Dimitris
Papadias and George Kollios",
title = "Authenticated indexing for outsourced spatial
databases",
journal = j-VLDB-J,
volume = "18",
number = "3",
pages = "631--648",
month = jun,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0113-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:55:19 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In spatial database outsourcing, a data owner
delegates its data management tasks to a location-based
service (LBS), which indexes the data with an
authenticated data structure (ADS). The LBS receives
queries (ranges, nearest neighbors) originating from
several clients/subscribers. Each query initiates the
computation of a verification object (VO) based on the
ADS. The VO is returned to the client that can verify
the result correctness using the public key of the
owner. Our first contribution is the MR-tree, a
space-efficient ADS that supports fast query processing
and verification. Our second contribution is the
MR*-tree, a modified version of the MR-tree, which
significantly reduces the VO size through a novel
embedding technique. Finally, whereas most ADSs must be
constructed and maintained by the owner, we outsource
the MR- and MR*-tree construction and maintenance to
the LBS, thus relieving the owner from this
computationally intensive task.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Authenticated index; Database outsourcing; Mobile
computing; Spatial database",
}
@Article{Quiane-Ruiz:2009:SAQ,
author = "Jorge-Arnulfo Quian{\'e}-Ruiz and Philippe Lamarre and
Patrick Valduriez",
title = "A self-adaptable query allocation framework for
distributed information systems",
journal = j-VLDB-J,
volume = "18",
number = "3",
pages = "649--674",
month = jun,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0114-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:55:19 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In large-scale distributed information systems, where
participants are autonomous and have special interests
for some queries, query allocation is a challenge. Much
work in this context has focused on distributing
queries among providers in a way that maximizes overall
performance (typically throughput and response time).
However, preserving the participants' interests is also
important. In this paper, we make the following
contributions. First, we provide a model to define the
participants' perception of the system regarding their
interests and propose measures to evaluate the quality
of query allocation methods. Then, we propose a
framework for query allocation called
Satisfaction-based Query Load Balancing (SQLB, for
short), which dynamically trades consumers' interests
for providers' interests based on their satisfaction.
Finally, we compare SQLB, through experimentation, with
two important baseline query allocation methods, namely
Capacity based and Mariposa-like. The results
demonstrate that SQLB yields high efficiency while
satisfying the participants' interests and
significantly outperforms the baseline methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Distributed information systems; Query allocation;
Queryload balancing; Satisfaction",
}
@Article{Deng:2009:IOQ,
author = "Ke Deng and Xiaofang Zhou and Heng Tao Shen and Shazia
Sadiq and Xue Li",
title = "Instance optimal query processing in spatial
networks",
journal = j-VLDB-J,
volume = "18",
number = "3",
pages = "675--693",
month = jun,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0115-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:55:19 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The performance optimization of query processing in
spatial networks focuses on minimizing network data
accesses and the cost of network distance calculations.
This paper proposes algorithms for network k -NN
queries, range queries, closest-pair queries and
multi-source skyline queries based on a novel
processing framework, namely, incremental lower bound
constraint. By giving high processing priority to the
query associated data points and utilizing the
incremental nature of the lower bound, the performance
of our algorithms is better optimized in contrast to
the corresponding algorithms based on known framework
incremental Euclidean restriction and incremental
network expansion. More importantly, the proposed
algorithms are proven to be instance optimal among
classes of algorithms. Through experiments on real road
network datasets, the superiority of the proposed
algorithms is demonstrated.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Incremental lower bound constraint; Instance
optimality; Spatial networks; Spatial queries",
}
@Article{Yiu:2009:MDT,
author = "Man Lung Yiu and Nikos Mamoulis",
title = "Multi-dimensional top-$k$ dominating queries",
journal = j-VLDB-J,
volume = "18",
number = "3",
pages = "695--718",
month = jun,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0117-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:55:19 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The top- k dominating query returns k data objects
which dominate the highest number of objects in a
dataset. This query is an important tool for decision
support since it provides data analysts an intuitive
way for finding significant objects. In addition, it
combines the advantages of top- k and skyline queries
without sharing their disadvantages: (i) the output
size can be controlled, (ii) no ranking functions need
to be specified by users, and (iii) the result is
independent of the scales at different dimensions.
Despite their importance, top- k dominating queries
have not received adequate attention from the research
community. This paper is an extensive study on the
evaluation of top- k dominating queries. First, we
propose a set of algorithms that apply on indexed
multi-dimensional data. Second, we investigate query
evaluation on data that are not indexed. Finally, we
study a relaxed variant of the query which considers
dominance in dimensional subspaces. Experiments using
synthetic and real datasets demonstrate that our
algorithms significantly outperform a previous
skyline-based approach. We also illustrate the
applicability of this multi-dimensional analysis query
by studying the meaningfulness of its results on real
data.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Preference dominance; Score counting; Top-k
retrieval",
}
@Article{Silva:2009:RTS,
author = "Yasin N. Silva and Xiaopeng Xiong and Walid G. Aref",
title = "The {RUM-tree}: supporting frequent updates in
{R-trees} using memos",
journal = j-VLDB-J,
volume = "18",
number = "3",
pages = "719--738",
month = jun,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0120-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:55:19 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The problem of frequently updating multi-dimensional
indexes arises in many location-dependent applications.
While the R-tree and its variants are the dominant
choices for indexing multi-dimensional objects, the
R-tree exhibits inferior performance in the presence of
frequent updates. In this paper, we present an R-tree
variant, termed the RUM-tree (which stands for R-tree
with update memo) that reduces the cost of object
updates. The RUM-tree processes updates in a memo-based
approach that avoids disk accesses for purging old
entries during an update process. Therefore, the cost
of an update operation in the RUM-tree is reduced to
the cost of only an insert operation. The removal of
old object entries is carried out by a garbage cleaner
inside the RUM-tree. In this paper, we present the
details of the RUM-tree and study its properties. We
also address the issues of crash recovery and
concurrency control for the RUM-tree. Theoretical
analysis and comprehensive experimental evaluation
demonstrate that the RUM-tree outperforms other R-tree
variants by up to one order of magnitude in scenarios
with frequent updates.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Frequent updates; Indexing techniques; Performance;
Spatio-temporal databases",
}
@Article{Kriakov:2009:STM,
author = "Vassil Kriakov and George Kollios and Alex Delis",
title = "Self-tuning management of update-intensive
multidimensional data in clusters of workstations",
journal = j-VLDB-J,
volume = "18",
number = "3",
pages = "739--764",
month = jun,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0121-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:55:19 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Contemporary applications continuously modify large
volumes of multidimensional data that must be accessed
efficiently and, more importantly, must be updated in a
timely manner. Single-server storage approaches are
insufficient when managing such volumes of data, while
the high frequency of data modification render
classical indexing methods inefficient. To address
these two problems we introduce a distributed storage
manager for multidimensional data based on a
Cluster-of-Workstations. The manager addresses the
above challenges through a set of mechanisms that,
through selective on-line data reorganization,
collectively maintain a balanced load across a cluster
of workstations. With the help of both a highly
efficient and speedy self-tuning mechanism, based on a
new data structure called stat -index, as well as a
query aggregation and clustering algorithm, our storage
manager attains short query response times even in the
presence of massive modifications and highly skewed
access patterns. Furthermore, we provide a data
migration cost model used to determine the best data
redistribution strategy. Through extensive
experimentation with our prototype, we establish that
our storage manager can sustain significant update
rates with minimal overhead.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Cluster of workstations; Multi-dimensional data;
Self-tuning storage",
}
@Article{Cohen:2009:EQS,
author = "Sara Cohen",
title = "Equivalence of queries that are sensitive to
multiplicities",
journal = j-VLDB-J,
volume = "18",
number = "3",
pages = "765--785",
month = jun,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0122-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:55:19 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The query equivalence problem has been studied
extensively for set-semantics and, more recently, for
bag and bag-set semantics. However, SQL queries often
combine set, bag and bag-set semantics. For example, an
SQL query that returns a multiset of elements may call
a subquery or view that returns a set of elements.
Queries may access both relations that do not contain
duplicates, as well as relations with duplicates. As
another example, in SQL one can compute a
multiset-union of queries, each of which returns a set
of answers. This paper presents combined semantics,
which formally models query evaluation combining set,
bag and bag-set semantics. The equivalence problem for
queries evaluated under combined semantics is studied.
A sufficient condition for equivalence is presented.
For several important common classes of queries
necessary and sufficient conditions for equivalence are
presented.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Bag semantics; Combined semantics; Datalog; Query
equivalence; Set semantics",
}
@Article{Lian:2009:EPP,
author = "Xiang Lian and Lei Chen",
title = "Efficient processing of probabilistic reverse nearest
neighbor queries over uncertain data",
journal = j-VLDB-J,
volume = "18",
number = "3",
pages = "787--808",
month = jun,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0123-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:55:19 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Reverse nearest neighbor (RNN) search is very crucial
in many real applications. In particular, given a
database and a query object, an RNN query retrieves all
the data objects in the database that have the query
object as their nearest neighbors. Often, due to
limitation of measurement devices, environmental
disturbance, or characteristics of applications (for
example, monitoring moving objects), data obtained from
the real world are uncertain (imprecise). Therefore,
previous approaches proposed for answering an RNN query
over exact (precise) database cannot be directly
applied to the uncertain scenario. In this paper, we
re-define the RNN query in the context of uncertain
databases, namely probabilistic reverse nearest
neighbor (PRNN) query, which obtains data objects with
probabilities of being RNNs greater than or equal to a
user-specified threshold. Since the retrieval of a PRNN
query requires accessing all the objects in the
database, which is quite costly, we also propose an
effective pruning method, called geometric pruning
(GP), that significantly reduces the PRNN search space
yet without introducing any false dismissals.
Furthermore, we present an efficient PRNN query
procedure that seamlessly integrates our pruning
method. Extensive experiments have demonstrated the
efficiency and effectiveness of our proposed GP-based
PRNN query processing approach, under various
experimental settings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Geometric pruning; Probabilistic reverse nearest
neighbor; Uncertain databases",
}
@Article{Hua:2009:TTQ,
author = "Ming Hua and Jian Pei and Ada W. Fu and Xuemin Lin and
Ho-Fung Leung",
title = "Top-$k$ typicality queries and efficient query
answering methods on large databases",
journal = j-VLDB-J,
volume = "18",
number = "3",
pages = "809--835",
month = jun,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0128-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:55:19 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Finding typical instances is an effective approach to
understand and analyze large data sets. In this paper,
we apply the idea of typicality analysis from
psychology and cognitive science to database query
answering, and study the novel problem of answering
top- k typicality queries. We model typicality in large
data sets systematically. Three types of top- k
typicality queries are formulated. To answer questions
like 'Who are the top- k most typical NBA players?',
the measure of simple typicality is developed. To
answer questions like 'Who are the top- k most typical
guards distinguishing guards from other players?', the
notion of discriminative typicality is proposed.
Moreover, to answer questions like 'Who are the best k
typical guards in whole representing different types of
guards?', the notion of representative typicality is
used. Computing the exact answer to a top- k typicality
query requires quadratic time which is often too costly
for online query answering on large databases. We
develop a series of approximation methods for various
situations: (1) the randomized tournament algorithm has
linear complexity though it does not provide a
theoretical guarantee on the quality of the answers;
(2) the direct local typicality approximation using
VP-trees provides an approximation quality guarantee;
(3) a local typicality tree data structure can be
exploited to index a large set of objects. Then,
typicality queries can be answered efficiently with
quality guarantees by a tournament method based on a
Local Typicality Tree. An extensive performance study
using two real data sets and a series of synthetic data
sets clearly shows that top- k typicality queries are
meaningful and our methods are practical.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Efficient query answering; Top-k query; Typicality
analysis",
}
@Article{Bawa:2009:PPI,
author = "Mayank Bawa and Roberto J. {Bayardo, Jr.} and Rakesh
Agrawal and Jaideep Vaidya",
title = "Privacy-preserving indexing of documents on the
network",
journal = j-VLDB-J,
volume = "18",
number = "4",
pages = "837--856",
month = aug,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0129-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:56:20 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "With the ubiquitous collection of data and creation of
large distributed repositories, enabling search over
this data while respecting access control is critical.
A related problem is that of ensuring privacy of the
content owners while still maintaining an efficient
index of distributed content. We address the problem of
providing privacy-preserving search over distributed
access-controlled content. Indexed documents can be
easily reconstructed from conventional (inverted)
indexes used in search. Currently, the need to avoid
breaches of access-control through the index requires
the index hosting site to be fully secured and trusted
by all participating content providers. This level of
trust is impractical in the increasingly common case
where multiple competing organizations or individuals
wish to selectively share content. We propose a
solution that eliminates the need of such a trusted
authority. The solution builds a centralized
privacy-preserving index in conjunction with a
distributed access-control enforcing search protocol.
Two alternative methods to build the centralized index
are proposed, allowing trade offs of efficiency and
security. The new index provides strong and
quantifiable privacy guarantees that hold even if the
entire index is made public. Experiments on a real-life
dataset validate performance of the scheme. The appeal
of our solution is twofold: (a) content providers
maintain complete control in defining access groups and
ensuring its compliance, and (b) system implementors
retain tunable knobs to balance privacy and efficiency
concerns for their particular domains.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Distributed search; Indexing; Privacy",
}
@Article{Fan:2009:QTX,
author = "Wenfei Fan and Jeffrey Xu Yu and Jianzhong Li and
Bolin Ding and Lu Qin",
title = "Query translation from {XPath} to {SQL} in the
presence of recursive {DTDs}",
journal = j-VLDB-J,
volume = "18",
number = "4",
pages = "857--883",
month = aug,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-008-0131-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:56:20 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We study the problem of evaluating xpath queries over
xml data that is stored in an rdbms via schema-based
shredding. The interaction between recursion
(descendants-axis) in xpath queries and recursion in
dtds makes it challenging to answer xpath queries using
rdbms. We present a new approach to translating xpath
queries into sql queries based on a notion of
extended\par
XP ath expressions and a simple least fixpoint (lfp)
operator. Extended xpath expressions are a mild
extension of xpath, and the lfp operator takes a single
input relation and is already supported by most
commercial rdbms. We show that extended xpath
expressions are capable of capturing both dtd recursion
and xpath queries in a uniform framework. Furthermore,
they can be translated into an equivalent sequence of
sql queries with the lfp operator. We present
algorithms for rewriting xpath queries over a (possibly
recursive) dtd into extended xpath expressions and for
translating extended xpath expressions to sql queries,
as well as optimization techniques. The novelty of our
approach consists in its capability to answer a large
class of xpath queries by means of only low-end rdbms
features already available in most rdbms, as well as
its flexibility to accommodate existing relational
query optimization techniques. In addition, these
translation algorithms provide a solution to query
answering for certain (possibly recursive) xml views of
xml data. Our experimental results verify the
effectiveness of our techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Query translation; Recursive DTD; SQL; XML database;
XPath",
}
@Article{Malik:2009:RRA,
author = "Zaki Malik and Athman Bouguettaya",
title = "{RATEWeb}: {Reputation Assessment} for {Trust
Establishment} among {Web} services",
journal = j-VLDB-J,
volume = "18",
number = "4",
pages = "885--911",
month = aug,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0138-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:56:20 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We introduce RATEWeb, a framework for establishing
trust in service-oriented environments. RATEWeb
supports a cooperative model in which Web services
share their experiences of the service providers with
their peers through feedback ratings. The different
ratings are aggregated to derive a service provider's
reputation. This in turn is used to evaluate trust. The
overall goal of RATEWeb is to facilitate trust-based
selection and composition of Web services. We propose a
set of decentralized techniques that aim at accurately
aggregating the submitted ratings for reputation
assessment. We conduct experiments to assess the
fairness and accuracy of the proposed techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Reputation; Trust; Web service",
}
@Article{Wang:2009:CRE,
author = "Fusheng Wang and Shaorong Liu and Peiya Liu",
title = "Complex {RFID} event processing",
journal = j-VLDB-J,
volume = "18",
number = "4",
pages = "913--931",
month = aug,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0139-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:56:20 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Advances of sensor and radio frequency identification
(RFID) technology provide significant new power for
humans to sense, understand and manage the world. RFID
provides fast data collection with precise
identification of objects with unique IDs without line
of sight, thus it can be used for identifying,
locating, tracking and monitoring physical objects.
Despite these benefits, RFID poses many challenges for
data processing and management: (i) RFID observations
have implicit meanings, which have to be transformed
and aggregated into semantic data represented in their
data models; and (ii) RFID data are temporal,
streaming, and in high volume, and have to be processed
on the fly. Thus, a general RFID data processing
framework is needed to automate the transformation of
physical RFID observations into the virtual
counterparts in the virtual world linked to business
applications. In this paper, we take an event-oriented
approach to process RFID data, by devising RFID
application logic into complex events. We then
formalize the specification and semantics of RFID
events and rules. We discover that RFID events are
highly temporal constrained, and include
non-spontaneous events, and develop an RFID event
detection engine that can effectively process complex
RFID events. The declarative event-based approach
greatly simplifies the work of RFID data processing,
and can significantly reduce the cost of RFID data
integration.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Complex event; ECA rules; RFID; Temporal",
}
@Article{DuMouza:2009:LSI,
author = "C{\'e}dric {Du Mouza} and Witold Litwin and Philippe
Rigaux",
title = "Large-scale indexing of spatial data in distributed
repositories: the {SD}-Rtree",
journal = j-VLDB-J,
volume = "18",
number = "4",
pages = "933--958",
month = aug,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0135-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:56:20 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We propose a scalable distributed data structure
(SDDS) called SD-Rtree. We intend our structure for
point, window and k NN queries over large spatial
datasets distributed on clusters of interconnected
servers. The structure balances the storage and
processing load over the available resources, and aims
at minimizing the size of the cluster. SD-Rtree
generalizes the well-known Rtree structure. It uses a
distributed balanced binary tree that scales with
insertions to potentially any number of storage servers
through splits of the overloaded ones. A
user/application manipulates the structure from a
client node. The client addresses the tree through its
image that can be possibly outdated due to later split.
This may generate addressing errors, solved by the
forwarding among the servers. Specific messages towards
the clients incrementally correct the outdated images.
We present the building of an SD-Rtree through
insertions, focusing on the split and rotation
algorithms. We follow with the query algorithms. We
describe then a flexible allocation protocol which
allows to cope with a temporary shortage of storage
resources through data storage balancing. Experiments
show additional aspects of SD-Rtree and compare its
behavior with a distributed quadtree. The results
justify our various design choices and the overall
utility of the structure.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Distributed structure; Spatial indexing",
}
@Article{Zheng:2009:DSI,
author = "Baihua Zheng and Wang-Chien Lee and Ken C. Lee and Dik
Lun Lee and Min Shao",
title = "A distributed spatial index for error-prone wireless
data broadcast",
journal = j-VLDB-J,
volume = "18",
number = "4",
pages = "959--986",
month = aug,
year = "2009",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0137-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Sep 15 14:56:20 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Information is valuable to users when it is available
not only at the right time but also at the right place.
To support efficient location-based data access in
wireless data broadcast systems, a distributed spatial
index (called DSI) is presented in this paper. DSI is
highly efficient because it has a linear yet fully
distributed structure that naturally shares links in
different search paths. DSI is very resilient to the
error-prone wireless communication environment because
interrupted search operations based on DSI can be
resumed easily. It supports search algorithms for
classical location-based queries such as window queries
and k NN queries in both of the snapshot and continuous
query modes. In-depth analysis and simulation-based
evaluation have been conducted. The results show that
DSI significantly out-performs a variant of R-trees
tailored for wireless data broadcast environments.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Error resilience; Location-based query; Mobile
computing; Wireless broadcast",
}
@Article{Haas:2009:SIU,
author = "Peter J. Haas and Dan Suciu",
title = "Special issue on uncertain and probabilistic
databases",
journal = j-VLDB-J,
volume = "18",
number = "5",
pages = "987--988",
month = oct,
year = "2009",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:40 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sarma:2009:RUD,
author = "Anish Das Sarma and Omar Benjelloun and Alon Halevy
and Shubha Nabar and Jennifer Widom",
title = "Representing uncertain data: models, properties, and
algorithms",
journal = j-VLDB-J,
volume = "18",
number = "5",
pages = "989--1019",
month = oct,
year = "2009",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:40 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Antova:2009:WBE,
author = "Lyublena Antova and Christoph Koch and Dan Olteanu",
title = "$ 10^{(10^6)} $ worlds and beyond: efficient
representation and processing of incomplete
information",
journal = j-VLDB-J,
volume = "18",
number = "5",
pages = "1021--1040",
month = oct,
year = "2009",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:40 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Abiteboul:2009:EPX,
author = "Serge Abiteboul and Benny Kimelfeld and Yehoshua Sagiv
and Pierre Senellart",
title = "On the expressiveness of probabilistic {XML} models",
journal = j-VLDB-J,
volume = "18",
number = "5",
pages = "1041--1064",
month = oct,
year = "2009",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:40 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sen:2009:PME,
author = "Prithviraj Sen and Amol Deshpande and Lise Getoor",
title = "{PrDB}: managing and exploiting rich correlations in
probabilistic databases",
journal = j-VLDB-J,
volume = "18",
number = "5",
pages = "1065--1090",
month = oct,
year = "2009",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:40 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Re:2009:THQ,
author = "Christopher R{\'e} and Dan Suciu",
title = "The trichotomy of {HAVING} queries on a probabilistic
database",
journal = j-VLDB-J,
volume = "18",
number = "5",
pages = "1091--1116",
month = oct,
year = "2009",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:40 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kimelfeld:2009:QEP,
author = "Benny Kimelfeld and Yuri Kosharovsky and Yehoshua
Sagiv",
title = "Query evaluation over probabilistic {XML}",
journal = j-VLDB-J,
volume = "18",
number = "5",
pages = "1117--1140",
month = oct,
year = "2009",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:40 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hassanzadeh:2009:CPD,
author = "Oktie Hassanzadeh and Ren{\'e}e J. Miller",
title = "Creating probabilistic databases from duplicated
data",
journal = j-VLDB-J,
volume = "18",
number = "5",
pages = "1141--1166",
month = oct,
year = "2009",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:40 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wolf:2009:QPI,
author = "Garrett Wolf and Aravind Kalavagattu and Hemal Khatri
and Raju Balakrishnan and Bhaumik Chokshi and Jianchun
Fan and Yi Chen and Subbarao Kambhampati",
title = "Query processing over incomplete autonomous databases:
query rewriting using learned data dependencies",
journal = j-VLDB-J,
volume = "18",
number = "5",
pages = "1167--1190",
month = oct,
year = "2009",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:40 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Keulen:2009:QEK,
author = "Maurice Keulen and Ander Keijzer",
title = "Qualitative effects of knowledge rules and user
feedback in probabilistic data integration",
journal = j-VLDB-J,
volume = "18",
number = "5",
pages = "1191--1217",
month = oct,
year = "2009",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:40 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2009:SPS,
author = "Jinchuan Chen and Reynold Cheng and Mohamed Mokbel and
Chi-Yin Chow",
title = "Scalable processing of snapshot and continuous
nearest-neighbor queries over one-dimensional uncertain
data",
journal = j-VLDB-J,
volume = "18",
number = "5",
pages = "1219--1240",
month = oct,
year = "2009",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:40 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2010:TFD,
author = "Keke Chen and Ling Liu",
title = "{HE-Tree}: a framework for detecting changes in
clustering structure for categorical data streams",
journal = j-VLDB-J,
volume = "18",
number = "6",
pages = "1241--1260",
month = dec,
year = "2010",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:44 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Whang:2010:GER,
author = "Steven Euijong Whang and Omar Benjelloun and Hector
Garcia-Molina",
title = "Generic entity resolution with negative rules",
journal = j-VLDB-J,
volume = "18",
number = "6",
pages = "1261--1277",
month = dec,
year = "2010",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:44 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ntarmos:2010:SSI,
author = "Nikos Ntarmos and Peter Triantafillou and Gerhard
Weikum",
title = "Statistical structures for {Internet}-scale data
management",
journal = j-VLDB-J,
volume = "18",
number = "6",
pages = "1279--1312",
month = dec,
year = "2010",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:44 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bramandia:2010:OUR,
author = "Ramadhana Bramandia and Jiefeng Cheng and Byron Choi
and Jeffrey Xu Yu",
title = "Optimizing updates of recursive {XML} views of
relations",
journal = j-VLDB-J,
volume = "18",
number = "6",
pages = "1313--1333",
month = dec,
year = "2010",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:44 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Duntgen:2010:BBM,
author = "Christian D{\"u}ntgen and Thomas Behr and Ralf Hartmut
G{\"u}ting",
title = "{BerlinMOD}: a benchmark for moving object databases",
journal = j-VLDB-J,
volume = "18",
number = "6",
pages = "1335--1368",
month = dec,
year = "2010",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:44 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mandreoli:2010:PHS,
author = "Federica Mandreoli and Riccardo Martoglia and Pavel
Zezula",
title = "Principles of {Holism} for sequential twig pattern
matching",
journal = j-VLDB-J,
volume = "18",
number = "6",
pages = "1369--1392",
month = dec,
year = "2010",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:44 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Buneman:2010:SIB,
author = "Peter Buneman and Volker Markl and Beng Chin Ooi and
Kenneth Ross",
title = "Special issue: best papers of {VLDB 2008}",
journal = j-VLDB-J,
volume = "19",
number = "1",
pages = "1--2",
month = feb,
year = "2010",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:46 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Cormode:2010:MFF,
author = "Graham Cormode and Marios Hadjieleftheriou",
title = "Methods for finding frequent items in data streams",
journal = j-VLDB-J,
volume = "19",
number = "1",
pages = "3--20",
month = feb,
year = "2010",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:46 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bruno:2010:CPD,
author = "Nicolas Bruno and Surajit Chaudhuri",
title = "Constrained physical design tuning",
journal = j-VLDB-J,
volume = "19",
number = "1",
pages = "21--44",
month = feb,
year = "2010",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:46 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lizorkin:2010:AEO,
author = "Dmitry Lizorkin and Pavel Velikhov and Maxim Grinev
and Denis Turdakov",
title = "Accuracy estimate and optimization techniques for
{SimRank} computation",
journal = j-VLDB-J,
volume = "19",
number = "1",
pages = "45--66",
month = feb,
year = "2010",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:46 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Nath:2010:OMV,
author = "Suman Nath and Phillip B. Gibbons",
title = "Online maintenance of very large random samples on
flash storage",
journal = j-VLDB-J,
volume = "19",
number = "1",
pages = "67--90",
month = feb,
year = "2010",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:46 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Neumann:2010:RES,
author = "Thomas Neumann and Gerhard Weikum",
title = "The {RDF-3X} engine for scalable management of {RDF}
data",
journal = j-VLDB-J,
volume = "19",
number = "1",
pages = "91--113",
month = feb,
year = "2010",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:46 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Cormode:2010:ABG,
author = "Graham Cormode and Divesh Srivastava and Ting Yu and
Qing Zhang",
title = "Anonymizing bipartite graph data using safe
groupings",
journal = j-VLDB-J,
volume = "19",
number = "1",
pages = "115--139",
month = feb,
year = "2010",
CODEN = "VLDBFR",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 16 08:21:46 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{U:2010:CSA,
author = "Leong Hou U. and Kyriakos Mouratidis and Nikos
Mamoulis",
title = "Continuous spatial assignment of moving users",
journal = j-VLDB-J,
volume = "19",
number = "2",
pages = "141--160",
month = apr,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0144-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 21 16:41:50 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Consider a set of servers and a set of users, where
each server has a coverage region (i.e., an area of
service) and a capacity (i.e., a maximum number of
users it can serve). Our task is to assign every user
to one server subject to the coverage and capacity
constraints. To offer the highest quality of service,
we wish to minimize the average distance between users
and their assigned server. This is an instance of a
well-studied problem in operations research, termed
optimal assignment. Even though there exist several
solutions for the static case (where user locations are
fixed), there is currently no method for dynamic
settings. In this paper, we consider the continuous
assignment problem (CAP), where an optimal assignment
must be constantly maintained between mobile users and
a set of servers. The fact that the users are mobile
necessitates real-time reassignment so that the quality
of service remains high (i.e., their distance from
their assigned servers is minimized). The large scale
and the time-critical nature of targeted applications
require fast CAP solutions. We propose an algorithm
that utilizes the geometric characteristics of the
problem and significantly accelerates the initial
assignment computation and its subsequent maintenance.
Our method applies to different cost functions (e.g.,
average squared distance) and to any Minkowski distance
metric (e.g., Euclidean, L$_1$ norm, etc.).",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Continuous query; Optimal assignment; Spatial
monitoring",
}
@Article{Papadopoulos:2010:CAR,
author = "Stavros Papadopoulos and Yin Yang and Dimitris
Papadias",
title = "Continuous authentication on relational streams",
journal = j-VLDB-J,
volume = "19",
number = "2",
pages = "161--180",
month = apr,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0145-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 21 16:41:50 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "According to the database outsourcing model, a data
owner delegates database functionality to a third-party
service provider, which answers queries received from
clients. Authenticated query processing enables the
clients to verify the correctness of query results.
Despite the abundance of methods for authenticated
processing in conventional databases, there is limited
work on outsourced data streams. Stream environments
pose new challenges such as the need for fast structure
updating, support for continuous query processing and
authentication, and provision for temporal
completeness. Specifically, in addition to the
correctness of individual results, the client must be
able to verify that there are no missing results in
between data updates. This paper presents a
comprehensive set of methods covering relational
streams. We first describe REF, a technique that
achieves correctness and temporal completeness but
incurs false transmissions, i.e., the provider has to
inform the clients whenever there is a data update,
even if their results are not affected. Then, we
propose CADS, which minimizes the processing and
transmission overhead through an elaborate indexing
scheme and a virtual caching mechanism. In addition, we
present an analytical study to determine the optimal
indexing granularity, and extend CADS for the case that
the data distribution changes over time. Finally, we
evaluate the effectiveness of our techniques through
extensive experiments.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Authentication; Continuous monitoring; Data streams;
Database outsourcing",
}
@Article{Zhang:2010:UMS,
author = "Zhenjie Zhang and Hua Lu and Beng Chin Ooi and Anthony
K. Tung",
title = "Understanding the meaning of a shifted sky: a general
framework on extending skyline query",
journal = j-VLDB-J,
volume = "19",
number = "2",
pages = "181--201",
month = apr,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0148-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 21 16:41:50 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Skyline queries are often used on data sets in
multi-dimensional space for many decision-making
applications. Traditionally, an object p is said to
dominate another object q if, for all dimensions, it is
no worse than q and is better on at least one
dimension. Therefore, the skyline of a data set
consists of all objects not dominated by any other
object. To better cater to application requirements
such as controlling the size of the skyline or handling
data sets that are not well-structured, various works
have been proposed to extend the definition of skyline
based on variants of the dominance relationship. In
view of the proliferation of variants, in this paper, a
generalized framework is proposed to guide the
extension of skyline query from conventional definition
to different variants. Our framework explicitly and
carefully examines the various properties that should
be preserved in a variant of the dominance relationship
so that: (1) maintaining original advantages, while
extending adaptivity to application semantics, and (2)
keeping computational complexity almost unaffected. We
prove that traditional dominance is the only
relationship satisfying all desirable properties, and
present some new dominance relationships by relaxing
some of the properties. These relationships are general
enough for us to design new top- k skyline queries that
return robust results of a controllable size. We
analyze the existing skyline algorithms based on their
minimum requirements on dominance properties. We also
extend our analysis to data sets with missing values,
and present extensive experimental results on the
combinations of new dominance relationships and skyline
algorithms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "General framework; Skyline query",
}
@Article{Lo:2010:FTD,
author = "Eric Lo and Carsten Binnig and Donald Kossmann and M.
Tamer {\"O}zsu and Wing-Kai Hon",
title = "A framework for testing {DBMS} features",
journal = j-VLDB-J,
volume = "19",
number = "2",
pages = "203--230",
month = apr,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0157-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 21 16:41:50 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Testing a specific feature of a DBMS requires
controlling the inputs and outputs of the operators in
the query execution plan. However, that is practically
difficult to achieve because the inputs/outputs of a
query depend on the content of the test database. In
this paper, we propose a framework to test DBMS
features. The framework includes a database generator
called QAGen so that the generated test databases are
able to meet the test requirements defined on the test
queries. The framework also includes a set of tools to
automate test case constructions and test executions. A
wide range of DBMS feature testing tasks can be
facilitated by the proposed framework.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Data generation; Database testing; Symbolic execution;
Symbolic query processing",
}
@Article{Bonifati:2010:SMQ,
author = "Angela Bonifati and Elaine Chang and Terence Ho and
Laks V. Lakshmanan and Rachel Pottinger and Yongik
Chung",
title = "Schema mapping and query translation in heterogeneous
{P2P XML} databases",
journal = j-VLDB-J,
volume = "19",
number = "2",
pages = "231--256",
month = apr,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0159-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 21 16:41:50 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Peers in a peer-to-peer data management system often
have heterogeneous schemas and no mediated global
schema. To translate queries across peers, we assume
each peer provides correspondences between its schema
and a small number of other peer schemas. We focus on
query reformulation in the presence of heterogeneous
XML schemas, including data---metadata conflicts. We
develop an algorithm for inferring precise mapping
rules from informal schema correspondences. We define
the semantics of query answering in this setting and
develop query translation algorithm. Our translation
handles an expressive fragment of XQuery and works both
along and against the direction of mapping rules. We
describe the HePToX heterogeneous P2P XML data
management system which incorporates our results. We
report the results of extensive experiments on HePToX
on both synthetic and real datasets. We demonstrate our
system utility and scalability on different P2P
distributions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Heterogeneous Peer-to-Peer XML databases; Schema
mapping; XML query translation",
}
@Article{Morfonios:2010:RCL,
author = "Konstantinos Morfonios and Yannis Ioannidis",
title = "Revisiting the cube lifecycle in the presence of
hierarchies",
journal = j-VLDB-J,
volume = "19",
number = "2",
pages = "257--282",
month = apr,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0160-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 21 16:41:50 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "On-line analytical processing (OLAP) typically
involves complex aggregate queries over large datasets.
The data cube has been proposed as a structure that
materializes the results of such queries in order to
accelerate OLAP. A significant fraction of the related
work has been on Relational-OLAP (ROLAP) techniques,
which are based on relational technology. Existing
ROLAP cubing solutions mainly focus on 'flat' datasets,
which do not include hierarchies in their dimensions.
Nevertheless, as shown in this paper, the nature of
hierarchies introduces several complications into the
entire lifecycle of a data cube including the
operations of construction, storage, indexing, query
processing, and incremental maintenance. This fact
renders existing techniques essentially inapplicable in
a significant number of real-world applications and
mandates revisiting the entire cube lifecycle under the
new perspective. In order to overcome this problem, the
CURE algorithm has been recently proposed as an
efficient mechanism to construct complete cubes over
large datasets with arbitrary hierarchies and store
them in a highly compressed format, compatible with the
relational model. In this paper, we study the remaining
phases in the cube lifecycle and introduce
query-processing and incremental-maintenance algorithms
for CURE cubes. These are significantly different from
earlier approaches, which have been proposed for flat
cubes constructed by other techniques and are
inadequate for CURE due to its high compression rate
and the presence of hierarchies. Our methods address
issues such as cube indexing, query optimization, and
lazy update policies. Especially regarding updates,
such lazy approaches are applied for the first time on
cubes. We demonstrate the effectiveness of CURE in all
phases of the cube lifecycle through experiments on
both real-world and synthetic datasets. Among the
experimental results, we distinguish those that have
made CURE the first ROLAP technique to complete the
construction and usage of the cube of the
highest-density dataset in the APB-1 benchmark (12 GB).
CURE was in fact quite efficient on this, showing great
promise with respect to the potential of the technique
overall.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Data cube; Incremental maintenance; Lazy update; Query
processing",
}
@Article{Zhang:2010:TBP,
author = "Wenjie Zhang and Xuemin Lin and Ying Zhang and Jian
Pei and Wei Wang",
title = "Threshold-based probabilistic top-$k$ dominating
queries",
journal = j-VLDB-J,
volume = "19",
number = "2",
pages = "283--305",
month = apr,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0162-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 21 16:41:50 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Recently, due to intrinsic characteristics in many
underlying data sets, a number of probabilistic queries
on uncertain data have been investigated. Top-$k$
dominating queries are very important in many
applications including decision making in a
multidimensional space. In this paper, we study the
problem of efficiently computing top-$k$ dominating
queries on uncertain data. We first formally define the
problem. Then, we develop an efficient, threshold-based
algorithm to compute the exact solution. To overcome
some inherent computational deficiency in an exact
computation, we develop an efficient randomized
algorithm with an accuracy guarantee. Our extensive
experiments demonstrate that both algorithms are quite
efficient, while the randomized algorithm is quite
scalable against data set sizes, object areas, $k$
values, etc. The randomized algorithm is also highly
accurate in practice.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Dominating relation; Top $k$; Uncertain objects",
}
@Article{Nutanong:2010:AEV,
author = "Sarana Nutanong and Rui Zhang and Egemen Tanin and
Lars Kulik",
title = "Analysis and evaluation of {V*-kNN}: an efficient
algorithm for moving {kNN} queries",
journal = j-VLDB-J,
volume = "19",
number = "3",
pages = "307--332",
month = jun,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0163-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Aug 18 12:05:52 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The moving $k$ nearest neighbor (M k NN) query
continuously finds the $k$ nearest neighbors of a
moving query point. M k NN queries can be efficiently
processed through the use of safe regions. In general,
a safe region is a region within which the query point
can move without changing the query answer. This paper
presents an incremental safe-region-based technique for
answering M k NN queries, called the V*-Diagram, as
well as analysis and evaluation of its associated
algorithm, V*-kNN. Traditional safe-region approaches
compute a safe region based on the data objects but
independent of the query location. Our approach
exploits the knowledge of the query location and the
boundary of the search space in addition to the data
objects. As a result, V*-kNN has much smaller I/O and
computation costs than existing methods. We further
provide cost models to estimate the number of data
accesses for V*-kNN and a competitive technique,
RIS-kNN. The V*-Diagram and V*-kNN are also applicable
to the domain of spatial networks and we present
algorithms to construct a spatial-network V*-Diagram.
Our experimental results show that V*-kNN significantly
outperforms the competitive technique. The results also
verify the accuracy of the cost models.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Nearest neighbor search; Spatial databases",
}
@Article{Lee:2010:ZSE,
author = "Ken C. Lee and Wang-Chien Lee and Baihua Zheng and
Huajing Li and Yuan Tian",
title = "{Z-SKY}: an efficient skyline query processing
framework based on {Z}-order",
journal = j-VLDB-J,
volume = "19",
number = "3",
pages = "333--362",
month = jun,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0166-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Aug 18 12:05:52 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Given a set of data points in a multidimensional
space, a skyline query retrieves those data points that
are not dominated by any other point in the same
dataset. Observing that the properties of Z-order space
filling curves (or Z-order curves) perfectly match with
the dominance relationships among data points in a
geometrical data space, we, in this paper, develop and
present a novel and efficient processing framework to
evaluate skyline queries and their variants, and to
support skyline result updates based on Z-order curves.
This framework consists of ZBtree, i.e., an index
structure to organize a source dataset and skyline
candidates, and a suite of algorithms, namely, (1)
ZSearch, which processes skyline queries, (2) ZInsert,
ZDelete and ZUpdate, which incrementally maintain
skyline results in presence of source dataset updates,
(3) ZBand, which answers skyband queries, (4) ZRank,
which returns top-ranked skyline points, (5) k-ZSearch,
which evaluates k -dominant skyline queries, and (6)
ZSubspace, which supports skyline queries on a subset
of dimensions. While derived upon coherent ideas and
concepts, our approaches are shown to outperform the
state-of-the-art algorithms that are specialized to
address particular skyline problems, especially when a
large number of skyline points are resulted, via
comprehensive experiments.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Index; Search algorithm; Skyline query; Skyline query
result update; Z-order space filling curve",
}
@Article{Yiu:2010:ESS,
author = "Man Lung Yiu and Gabriel Ghinita and Christian S.
Jensen and Panos Kalnis",
title = "Enabling search services on outsourced private spatial
data",
journal = j-VLDB-J,
volume = "19",
number = "3",
pages = "363--384",
month = jun,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0169-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Aug 18 12:05:52 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Cloud computing services enable organizations and
individuals to outsource the management of their data
to a service provider in order to save on hardware
investments and reduce maintenance costs. Only
authorized users are allowed to access the data. Nobody
else, including the service provider, should be able to
view the data. For instance, a real-estate company that
owns a large database of properties wants to allow its
paying customers to query for houses according to
location. On the other hand, the untrusted service
provider should not be able to learn the property
locations and, e.g., selling the information to a
competitor. To tackle the problem, we propose to
transform the location datasets before uploading them
to the service provider. The paper develops a spatial
transformation that re-distributes the locations in
space, and it also proposes a cryptographic-based
transformation. The data owner selects the
transformation key and shares it with authorized users.
Without the key, it is infeasible to reconstruct the
original data points from the transformed points. The
proposed transformations present distinct trade-offs
between query efficiency and data confidentiality. In
addition, we describe attack models for studying the
security properties of the transformations. Empirical
studies demonstrate that the proposed methods are
efficient and applicable in practice.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Data outsourcing; Spatial query processing",
}
@Article{Hintoglu:2010:SMP,
author = "Ay{\c{c}}a Azgin Hintoglu and Y{\"u}cel Sayg{\i}n",
title = "Suppressing microdata to prevent classification based
inference",
journal = j-VLDB-J,
volume = "19",
number = "3",
pages = "385--410",
month = jun,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0170-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Aug 18 12:05:52 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The revolution of the Internet together with the
progression in computer technology makes it easy for
institutions to collect an unprecedented amount of
personal data. This pervasive data collection rally
coupled with the increasing necessity of dissemination
and sharing of non-aggregated data, i.e., microdata,
raised a lot of concerns about privacy. One method to
ensure privacy is to selectively hide the confidential,
i.e. sensitive, information before disclosure. However,
with data mining techniques, it is now possible for an
adversary to predict the hidden confidential
information from the disclosed data sets. In this
paper, we concentrate on one such data mining technique
called classification. We extend our previous work on
microdata suppression to prevent both probabilistic and
decision tree classification based inference. We also
provide experimental results showing the effectiveness
of not only the proposed methods but also the hybrid
methods, i.e., methods suppressing microdata against
both classification models, on real-life data sets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Data mining; Data perturbation; Data suppression;
Disclosure protection; Privacy",
}
@Article{Jin:2010:SWT,
author = "Cheqing Jin and Ke Yi and Lei Chen and Jeffrey Xu Yu
and Xuemin Lin",
title = "Sliding-window top-$k$ queries on uncertain streams",
journal = j-VLDB-J,
volume = "19",
number = "3",
pages = "411--435",
month = jun,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0171-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Aug 18 12:05:52 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Recently, due to the imprecise nature of the data
generated from a variety of streaming applications,
such as sensor networks, query processing on uncertain
data streams has become an important problem. However,
all the existing works on uncertain data streams study
unbounded streams. In this paper, we take the first
step towards the important and challenging problem of
answering sliding-window queries on uncertain data
streams, with a focus on one of the most important
types of queries--top- k queries. It is nontrivial to
find an efficient solution for answering sliding-window
top- k queries on uncertain data streams, because
challenges not only stem from the strict space and time
requirements of processing both arriving and expiring
tuples in high-speed streams, but also rise from the
exponential blowup in the number of possible worlds
induced by the uncertain data model. In this paper, we
design a unified framework for processing
sliding-window top- k queries on uncertain streams. We
show that all the existing top- k definitions in the
literature can be plugged into our framework, resulting
in several succinct synopses that use space much
smaller than the window size, while they are also
highly efficient in terms of processing time. We also
extend our framework to answering multiple top- k
queries. In addition to the theoretical space and time
bounds that we prove for these synopses, we present a
thorough experimental report to verify their practical
efficiency on both synthetic and real data.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Sliding-window; Top-k query; Uncertain stream",
}
@Article{Pang:2010:EPE,
author = "Hweehwa Pang and Xuhua Ding and Baihua Zheng",
title = "Efficient processing of exact top-$k$ queries over
disk-resident sorted lists",
journal = j-VLDB-J,
volume = "19",
number = "3",
pages = "437--456",
month = jun,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0174-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Aug 18 12:05:52 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The top- k query is employed in a wide range of
applications to generate a ranked list of data that
have the highest aggregate scores over certain
attributes. As the pool of attributes for selection by
individual queries may be large, the data are indexed
with per-attribute sorted lists, and a threshold
algorithm (TA) is applied on the lists involved in each
query. The TA executes in two phases--find a cut-off
threshold for the top- k result scores, then evaluate
all the records that could score above the threshold.
In this paper, we focus on exact top- k queries that
involve monotonic linear scoring functions over
disk-resident sorted lists. We introduce a model for
estimating the depths to which each sorted list needs
to be processed in the two phases, so that (most of)
the required records can be fetched efficiently through
sequential or batched I/Os. We also devise a mechanism
to quickly rank the data that qualify for the query
answer and to eliminate those that do not, in order to
reduce the computation demand of the query processor.
Extensive experiments with four different datasets
confirm that our schemes achieve substantial
performance speed-up of between two times and two
orders of magnitude over existing TAs, at the expense
of a memory overhead of 4.8 bits per attribute value.
Moreover, our scheme is robust to different data
distributions and query characteristics.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Bloom filter; Threshold algorithm; Top-k query
processing",
}
@Article{Murugesan:2010:EPP,
author = "Mummoorthy Murugesan and Wei Jiang and Chris Clifton
and Luo Si and Jaideep Vaidya",
title = "Efficient privacy-preserving similar document
detection",
journal = j-VLDB-J,
volume = "19",
number = "4",
pages = "457--475",
month = aug,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0175-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Aug 18 12:06:22 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Similar document detection plays important roles in
many applications, such as file management, copyright
protection, plagiarism prevention, and duplicate
submission detection. The state of the art protocols
assume that the contents of files stored on a server
(or multiple servers) are directly accessible. However,
this makes such protocols unsuitable for any
environment where the documents themselves are
sensitive and cannot be openly read. Essentially, this
assumption limits more practical applications, e.g.,
detecting plagiarized documents between two
conferences, where submissions are confidential. We
propose novel protocols to detect similar documents
between two entities where documents cannot be openly
shared with each other. The similarity measure used can
be a simple cosine similarity on entire documents or on
document fragments, enabling detection of partial
copying. We conduct extensive experiments to show the
practical value of the proposed protocols. While the
proposed base protocols are much more efficient than
the general secure multiparty computation based
solutions, they are still slow for large document sets.
We then investigate a clustering based approach that
significantly reduces the running time and achieves
over 90\% of accuracy in our experiments. This makes
secure similar document detection both practical and
feasible.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Information retrieval; Privacy",
}
@Article{Soliman:2010:SRQ,
author = "Mohamed A. Soliman and Ihab F. Ilyas and Shalev
Ben-David",
title = "Supporting ranking queries on uncertain and incomplete
data",
journal = j-VLDB-J,
volume = "19",
number = "4",
pages = "477--501",
month = aug,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0176-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Aug 18 12:06:22 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Large databases with uncertain information are
becoming more common in many applications including
data integration, location tracking, and Web search. In
these applications, ranking records with uncertain
attributes introduces new problems that are
fundamentally different from conventional ranking.
Specifically, uncertainty in records' scores induces a
partial order over records, as opposed to the total
order that is assumed in the conventional ranking
settings. In this paper, we present a new probabilistic
model, based on partial orders, to encapsulate the
space of possible rankings originating from score
uncertainty. Under this model, we formulate several
ranking query types with different semantics. We
describe and analyze a set of efficient query
evaluation algorithms. We show that our techniques can
be used to solve the problem of rank aggregation in
partial orders under two widely adopted distance
metrics. In addition, we design sampling techniques
based on Markov chains to compute approximate query
answers. Our experimental evaluation uses both real and
synthetic data. The experimental study demonstrates the
efficiency and effectiveness of our techniques under
various configurations.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Kendall tau; Partial orders; Probabilistic data; Rank
aggregation; Ranking; Top-k; Uncertain data",
}
@Article{Lee:2010:SCE,
author = "Ki-Hoon Lee and Kyu-Young Whang and Wook-Shin Han and
Min-Soo Kim",
title = "Structural consistency: enabling {XML} keyword search
to eliminate spurious results consistently",
journal = j-VLDB-J,
volume = "19",
number = "4",
pages = "503--529",
month = aug,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-009-0177-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Aug 18 12:06:22 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "XML keyword search is a user-friendly way to query XML
data using only keywords. In XML keyword search, to
achieve high precision without sacrificing recall, it
is important to remove spurious results not intended by
the user. Efforts to eliminate spurious results have
enjoyed some success using the concepts of LCA or its
variants, SLCA and MLCA. However, existing methods
still could find many spurious results. The fundamental
cause for the occurrence of spurious results is that
the existing methods try to eliminate spurious results
locally without global examination of all the query
results and, accordingly, some spurious results are not
consistently eliminated. In this paper, we propose a
novel keyword search method that removes spurious
results consistently by exploiting the new concept of
structural consistency. We define structural
consistency as a property that is preserved if there is
no query result having an ancestor-descendant
relationship at the schema level with any other query
results. A naive solution to obtain structural
consistency would be to compute all the LCAs (or
variants) and then to remove spurious results according
to structural consistency. Obviously, this approach
would always be slower than existing LCA-based ones. To
speed up structural consistency checking, we must be
able to examine the query results at the schema level
without generating all the LCAs. However, this is a
challenging problem since the schema-level query
results do not homomorphically map to the
instance-level query results, causing serious false
dismissal. We present a comprehensive and practical
solution to this problem and formally prove that this
solution preserves structural consistency at the schema
level without incurring false dismissal. We also
propose a relevance-feedback-based solution for the
problem where our method has low recall, which occurs
when it is not the user's intention to find more
specific results. This solution has been prototyped in
a full-fledged object-relational DBMS Odysseus
developed at KAIST. Experimental results using real and
synthetic data sets show that, compared with the
state-of-the-art methods, our solution significantly
(1) improves precision while providing comparable
recall for most queries and (2) enhances the query
performance by removing spurious results early.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Keyword search; Odysseus DBMS; Spurious results;
Structural consistency; Structural summary; XML",
}
@Article{Lucchese:2010:RPT,
author = "Claudio Lucchese and Michail Vlachos and Deepak Rajan
and Philip S. Yu",
title = "Rights protection of trajectory datasets with
nearest-neighbor preservation",
journal = j-VLDB-J,
volume = "19",
number = "4",
pages = "531--556",
month = aug,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0178-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Aug 18 12:06:22 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Companies frequently outsource datasets to mining
firms, and academic institutions create repositories or
share datasets in the interest of promoting research
collaboration. Still, many practitioners have
reservations about sharing or outsourcing datasets,
primarily because of fear of losing the principal
rights over the dataset. This work presents a way of
convincingly claiming ownership rights over a
trajectory dataset, without, at the same time,
destroying the salient dataset characteristics, which
are important for accurate search operations and
data-mining tasks. The digital watermarking methodology
that we present distorts imperceptibly a collection of
sequences, effectively embedding a secret key, while
retaining as well as possible the neighborhood of each
object, which is vital for operations such as
similarity search, classification, or clustering. A key
contribution in this methodology is a technique for
discovering the maximum distortion that still maintains
such desirable properties. We demonstrate both
analytically and empirically that the proposed dataset
marking techniques can withstand a number of attacks
(such a translation, rotation, noise addition, etc) and
therefore can provide a robust framework for
facilitating the secure dissemination of trajectory
datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Nearest neighbors; Rights protection; Time-series;
Trajectories; Watermarking",
}
@Article{Zhang:2010:SMA,
author = "Rui Zhang and Nick Koudas and Beng Chin Ooi and Divesh
Srivastava and Pu Zhou",
title = "Streaming multiple aggregations using phantoms",
journal = j-VLDB-J,
volume = "19",
number = "4",
pages = "557--583",
month = aug,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0180-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Aug 18 12:06:22 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Data streams characterize the high speed and large
volume input of a new class of applications such as
network monitoring, web content analysis and sensor
networks. Among these applications, network monitoring
may be the most compelling one--the backbone of a large
internet service provider can generate 1 petabyte of
data per day. For many network monitoring tasks such as
traffic analysis and statistics collection, aggregation
is a primitive operation. Various analytical and
statistical needs naturally lead to related aggregate
queries. In this article, we address the problem of
efficiently computing multiple aggregations over
high-speed data streams based on the two-level query
processing architecture of GS, a real data stream
management system deployed in AT \& T. We discern that
additionally computing and maintaining fine-granularity
aggregations (called phantoms) has the benefit of
supporting shared computation. Based on a thorough
analysis, we propose algorithms to identify the best
set of phantoms to maintain and determine allocation of
resources (particularly, space) to compute the
aggregations. Experiments show that our algorithm
achieves near-optimal computation costs, which
outperforms the best adapted algorithm by more than an
order of magnitude.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Aggregation; Data stream; GS; Multiple-query
optimization; Phantom",
}
@Article{Jeung:2010:PPP,
author = "Hoyoung Jeung and Man Lung Yiu and Xiaofang Zhou and
Christian S. Jensen",
title = "Path prediction and predictive range querying in road
network databases",
journal = j-VLDB-J,
volume = "19",
number = "4",
pages = "585--602",
month = aug,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0181-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Aug 18 12:06:22 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In automotive applications, movement-path prediction
enables the delivery of predictive and relevant
services to drivers, e.g., reporting traffic conditions
and gas stations along the route ahead. Path prediction
also enables better results of predictive range queries
and reduces the location update frequency in vehicle
tracking while preserving accuracy. Existing
moving-object location prediction techniques in
spatial-network settings largely target short-term
prediction that does not extend beyond the next road
junction. To go beyond short-term prediction, we
formulate a network mobility model that offers a
concise representation of mobility statistics extracted
from massive collections of historical object
trajectories. The model aims to capture the turning
patterns at junctions and the travel speeds on road
segments at the level of individual objects. Based on
the mobility model, we present a maximum likelihood and
a greedy algorithm for predicting the travel path of an
object (for a time duration h into the future). We also
present a novel and efficient server-side indexing
scheme that supports predictive range queries on the
mobility statistics of the objects. Empirical studies
with real data suggest that our proposals are effective
and efficient.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "Mobility statistics; Path prediction; Predictive range
query; Road network database",
}
@Article{Ali:2010:MAA,
author = "Mohammed Eunus Ali and Egemen Tanin and Rui Zhang and
Lars Kulik",
title = "A motion-aware approach for efficient evaluation of
continuous queries on {$3$D} object databases",
journal = j-VLDB-J,
volume = "19",
number = "5",
pages = "603--632",
month = oct,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1145/1873117.1873119",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Oct 29 17:56:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Askitis:2010:ESC,
author = "Nikolas Askitis and Ranjan Sinha",
title = "Engineering scalable, cache and space efficient tries
for strings",
journal = j-VLDB-J,
volume = "19",
number = "5",
pages = "633--660",
month = oct,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1145/1873117.1873121",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Oct 29 17:56:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wu:2010:EEG,
author = "Xiaoying Wu and Dimitri Theodoratos and Calisto
Zuzarte",
title = "Efficient evaluation of generalized tree-pattern
queries on {XML} streams",
journal = j-VLDB-J,
volume = "19",
number = "5",
pages = "661--686",
month = oct,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1145/1873117.1873120",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Oct 29 17:56:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Guting:2010:ENN,
author = "Ralf Hartmut G{\"u}ting and Thomas Behr and Jianqiu
Xu",
title = "Efficient $k$-nearest neighbor search on moving object
trajectories",
journal = j-VLDB-J,
volume = "19",
number = "5",
pages = "687--714",
month = oct,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1145/1873117.1873123",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Oct 29 17:56:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2010:TQT,
author = "Feifei Li and Ke Yi and Wangchao Le",
title = "Top-$k$ queries on temporal data",
journal = j-VLDB-J,
volume = "19",
number = "5",
pages = "715--733",
month = oct,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1145/1873117.1873122",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Oct 29 17:56:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Duda:2010:PBI,
author = "Cristian Duda and Donald Kossmann and Chong Zhou",
title = "Predicate-based indexing for desktop search",
journal = j-VLDB-J,
volume = "19",
number = "5",
pages = "735--758",
month = oct,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1145/1873117.1873124",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Oct 29 17:56:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bohm:2010:F,
author = "Klemens B{\"o}hm and Laks V. Lakshmanan",
title = "Foreword",
journal = j-VLDB-J,
volume = "19",
number = "6",
pages = "759--760",
month = dec,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0201-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:41 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Carmel:2010:SBW,
author = "David Carmel and Haggai Roitman and Elad Yom-Tov",
title = "Social bookmark weighting for search and
recommendation",
journal = j-VLDB-J,
volume = "19",
number = "6",
pages = "761--775",
month = dec,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0211-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:41 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Social bookmarking enables knowledge sharing and
efficient discovery on the web, where users can
collaborate together by tagging documents of interests.
A lot of attention was given lately for utilizing
social bookmarking data to enhance traditional IR
tasks. Yet, much less attention was given to the
problem of estimating the effectiveness of an
individual bookmark for the specific tasks. In this
work, we propose a novel framework for social bookmark
weighting which allows us to estimate the effectiveness
of each of the bookmarks individually for several IR
tasks. We show that by weighting bookmarks according to
their estimated quality, we can significantly improve
social search effectiveness.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Squicciarini:2010:PPS,
author = "Anna C. Squicciarini and Mohamed Shehab and Joshua
Wede",
title = "Privacy policies for shared content in social network
sites",
journal = j-VLDB-J,
volume = "19",
number = "6",
pages = "777--796",
month = dec,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0193-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:41 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Social networking is one of the major technological
phenomena of the Web 2.0, with hundreds of millions of
subscribed users. Social networks enable a form of
self-expression for users and help them to socialize
and share content with other users. In spite of the
fact that content sharing represents one of the
prominent features of existing Social network sites,
they do not provide any mechanisms for collective
management of privacy settings for shared content. In
this paper, using game theory, we model the problem of
collective enforcement of privacy policies on shared
data. In particular, we propose a solution that offers
automated ways to share images based on an extended
notion of content ownership.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hay:2010:RSR,
author = "Michael Hay and Gerome Miklau and David Jensen and Don
Towsley and Chao Li",
title = "Resisting structural re-identification in anonymized
social networks",
journal = j-VLDB-J,
volume = "19",
number = "6",
pages = "797--823",
month = dec,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0210-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:41 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We identify privacy risks associated with releasing
network datasets and provide an algorithm that
mitigates those risks. A network dataset is a graph
representing entities connected by edges representing
relations such as friendship, communication or shared
activity. Maintaining privacy when publishing a network
dataset is uniquely challenging because an individual's
network context can be used to identify them even if
other identifying information is removed. In this
paper, we introduce a parameterized model of structural
knowledge available to the adversary and quantify the
success of attacks on individuals in anonymized
networks. We show that the risks of these attacks vary
based on network structure and size and provide
theoretical results that explain the anonymity risk in
random networks.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gruhl:2010:MSI,
author = "Daniel Gruhl and Meena Nagarajan and Jan Pieper and
Christine Robson and Amit Sheth",
title = "Multimodal social intelligence in a real-time
dashboard system",
journal = j-VLDB-J,
volume = "19",
number = "6",
pages = "825--848",
month = dec,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0207-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:41 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Social Networks provide one of the most rapidly
evolving data sets in existence today. Traditional
Business Intelligence applications struggle to take
advantage of such data sets in a timely manner. The BBC
SoundIndex, developed by the authors and others,
enabled real-time analytics of music popularity using
data from a variety of Social Networks. We present this
system as a grounding example of how to overcome the
challenges of working with this data from social
networks. We discuss a variety of technologies to
implement near real-time data analytics to transform
Social Intelligence into Business Intelligence and
evaluate their effectiveness in the music domain.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Benz:2010:SBP,
author = "Dominik Benz and Andreas Hotho and Robert J{\"a}schke
and Beate Krause and Folke Mitzlaff and Christoph
Schmitz and Gerd Stumme",
title = "The social bookmark and publication management system
bibsonomy",
journal = j-VLDB-J,
volume = "19",
number = "6",
pages = "849--875",
month = dec,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0208-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:41 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Social resource sharing systems are central elements
of the Web 2.0 and use the same kind of lightweight
knowledge representation, called folksonomy. Their
large user communities and ever-growing networks of
user-generated content have made them an attractive
object of investigation for researchers from different
disciplines like Social Network Analysis, Data Mining,
Information Retrieval or Knowledge Discovery. In this
paper, we summarize and extend our work on different
aspects of this branch of Web 2.0 research,
demonstrated and evaluated within our own social
bookmark and publication sharing system BibSonomy,
which is currently among the three most popular systems
of its kind. We structure this presentation along the
different interaction phases of a user with our system,
coupling the relevant research questions of each phase
with the corresponding implementation issues.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Roy:2010:SEG,
author = "Senjuti Basu Roy and Sihem Amer-Yahia and Ashish
Chawla and Gautam Das and Cong Yu",
title = "Space efficiency in group recommendation",
journal = j-VLDB-J,
volume = "19",
number = "6",
pages = "877--900",
month = dec,
year = "2010",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0209-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:41 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Imagine a system that gives you satisfying
recommendations when you want to rent a movie with
friends or find a restaurant to celebrate a colleague's
farewell: at the core of such a system is what we call
group recommendation. While computing individual
recommendations have received lots of attention (e.g.,
Netflix prize), group recommendation has been confined
to studying users' satisfaction with different
aggregation strategies. In this paper (Some results are
published in an earlier conference paper (Amer-Yahia et
al. in VLDB, 2009). See Sect. ``Paper contributions and
outline'' for details.) , we describe the challenges
and desiderata of group recommendation and formalize
different group consensus semantics that account for
both an item's predicted ratings to the group members
and the disagreements among them.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2011:PBK,
author = "Guoliang Li and Jianhua Feng and Xiaofang Zhou and
Jianyong Wang",
title = "Providing built-in keyword search capabilities in
{RDBMS}",
journal = j-VLDB-J,
volume = "20",
number = "1",
pages = "1--19",
month = feb,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0188-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:36 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A common approach to performing keyword search over
relational databases is to find the minimum Steiner
trees in database graphs transformed from relational
data. These methods, however, are rather expensive as
the minimum Steiner tree problem is known to be
NP-hard. Further, these methods are independent of the
underlying relational database management system
(RDBMS), thus cannot benefit from the capabilities of
the RDBMS. As an alternative, in this paper we propose
a new concept called Compact Steiner Tree (CSTree),
which can be used to approximate the Steiner tree
problem for answering top-$k$ keyword queries
efficiently. We propose a novel structure-aware index,
together with an effective ranking mechanism for fast,
progressive and accurate retrieval of top-$k$ highest
ranked CSTrees.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Cai:2011:SKD,
author = "Deng Cai and Xiaofei He and Jiawei Han",
title = "Speed up kernel discriminant analysis",
journal = j-VLDB-J,
volume = "20",
number = "1",
pages = "21--33",
month = feb,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0189-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:36 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Linear discriminant analysis (LDA) has been a popular
method for dimensionality reduction, which preserves
class separability. The projection vectors are commonly
obtained by maximizing the between-class covariance and
simultaneously minimizing the within-class covariance.
LDA can be performed either in the original input space
or in the reproducing kernel Hilbert space (RKHS) into
which data points are mapped, which leads to kernel
discriminant analysis (KDA). When the data are highly
nonlinear distributed, KDA can achieve better
performance than LDA. However, computing the projective
functions in KDA involves eigen-decomposition of kernel
matrix, which is very expensive when a large number of
training samples exist.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Qin:2011:SKS,
author = "Lu Qin and Jeffrey Xu Yu and Lijun Chang",
title = "Scalable keyword search on large data streams",
journal = j-VLDB-J,
volume = "20",
number = "1",
pages = "35--57",
month = feb,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0190-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:36 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "It is widely recognized that the integration of
information retrieval (IR) and database (DB) techniques
provides users with a broad range of high quality
services. Along this direction, IR-styled $m$-keyword
query processing over a relational database in an rdbms
framework has been well studied. It finds all hidden
interconnected tuple structures, for example connected
trees that contain keywords and are interconnected by
sequences of primary/foreign key relationships among
tuples. A new challenging issue is how to monitor
events that are implicitly interrelated over an
open-ended relational data stream for a user-given
$m$-keyword query. Such a relational data stream is a
sequence of tuple insertion/deletion operations.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Cao:2011:SSA,
author = "Jianneng Cao and Panagiotis Karras and Panos Kalnis
and Kian-Lee Tan",
title = "{SABRE}: a {Sensitive Attribute Bucketization and
REdistribution} framework for $t$-closeness",
journal = j-VLDB-J,
volume = "20",
number = "1",
pages = "59--81",
month = feb,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0191-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:36 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Today, the publication of microdata poses a privacy
threat: anonymous personal records can be re-identified
using third data sources. Past research has tried to
develop a concept of privacy guarantee that an
anonymized data set should satisfy before publication,
culminating in the notion of $t$-closeness. To satisfy
$t$-closeness, the records in a data set need to be
grouped into Equivalence Classes (ECs), such that each
EC contains records of indistinguishable
quasi-identifier values, and its local distribution of
sensitive attribute (SA) values conforms to the global
table distribution of SA values. However, despite this
progress, previous research has not offered an
anonymization algorithm tailored for $t$-closeness.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Terrovitis:2011:LGR,
author = "Manolis Terrovitis and Nikos Mamoulis and Panos
Kalnis",
title = "Local and global recoding methods for anonymizing
set-valued data",
journal = j-VLDB-J,
volume = "20",
number = "1",
pages = "83--106",
month = feb,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0192-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:36 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we study the problem of protecting
privacy in the publication of set-valued data. Consider
a collection of supermarket transactions that contains
detailed information about items bought together by
individuals. Even after removing all personal
characteristics of the buyer, which can serve as links
to his identity, the publication of such data is still
subject to privacy attacks from adversaries who have
partial knowledge about the set. Unlike most previous
works, we do not distinguish data as sensitive and
non-sensitive, but we consider them both as potential
quasi-identifiers and potential sensitive data,
depending on the knowledge of the adversary.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lian:2011:PIR,
author = "Xiang Lian and Lei Chen",
title = "Probabilistic inverse ranking queries in uncertain
databases",
journal = j-VLDB-J,
volume = "20",
number = "1",
pages = "107--127",
month = feb,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0195-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:36 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Query processing in the uncertain database has become
increasingly important due to the wide existence of
uncertain data in many real applications. Different
from handling precise data, the uncertain query
processing needs to consider the data uncertainty and
answer queries with confidence guarantees. In this
paper, we formulate and tackle an important query,
namely probabilistic inverse ranking (PIR) query, which
retrieves possible ranks of a given query object in an
uncertain database with confidence above a probability
threshold. We present effective pruning methods to
reduce the PIR search space, which can be seamlessly
integrated into an efficient query procedure. Moreover,
we tackle the problem of PIR query processing in high
dimensional spaces, which reduces high dimensional
uncertain data to a lower dimensional space.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hua:2011:RQU,
author = "Ming Hua and Jian Pei and Xuemin Lin",
title = "Ranking queries on uncertain data",
journal = j-VLDB-J,
volume = "20",
number = "1",
pages = "129--153",
month = feb,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0196-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Feb 7 10:43:36 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Uncertain data is inherent in a few important
applications. It is far from trivial to extend ranking
queries (also known as top-$k$ queries), a popular type
of queries on certain data, to uncertain data. In this
paper, we cast ranking queries on uncertain data using
three parameters: rank threshold $k$, probability
threshold $p$, and answer set size threshold $l$.
Systematically, we identify four types of ranking
queries on uncertain data. First, a probability
threshold top-$k$ query computes the uncertain records
taking a probability of at least $p$ to be in the top-k
list. Second, a top-$ (k, l)$ query returns the top-$l$
uncertain records whose probabilities of being ranked
among top-$k$ are the largest.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Abiteboul:2011:SIB,
author = "Serge Abiteboul and Volker Markl and Tova Milo and
Jignesh Patel",
title = "Special issue: best papers of {VLDB} 2009",
journal = j-VLDB-J,
volume = "20",
number = "2",
pages = "155--156",
month = apr,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0222-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 13 17:51:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mindolin:2011:PEP,
author = "Denis Mindolin and Jan Chomicki",
title = "Preference elicitation in prioritized skyline
queries",
journal = j-VLDB-J,
volume = "20",
number = "2",
pages = "157--182",
month = apr,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0227-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 13 17:51:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Preference queries incorporate the notion of binary
preference relation into relational database querying.
Instead of returning all the answers, such queries
return only the best answers, according to a given
preference relation. Preference queries are a fast
growing area of database research. Skyline queries
constitute one of the most thoroughly studied classes
of preference queries. A well-known limitation of
skyline queries is that skyline preference relations
assign the same importance to all attributes. In this
work, we study p-skyline queries that generalize
skyline queries by allowing varying attribute
importance in preference relations. We perform an
in-depth study of the properties of p-skyline
preference relations.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Denev:2011:SFD,
author = "Dimitar Denev and Arturas Mazeika and Marc Spaniol and
Gerhard Weikum",
title = "The {SHARC} framework for data quality in {Web}
archiving",
journal = j-VLDB-J,
volume = "20",
number = "2",
pages = "183--207",
month = apr,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0219-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 13 17:51:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Web archives preserve the history of born-digital
content and offer great potential for sociologists,
business analysts, and legal experts on intellectual
property and compliance issues. Data quality is crucial
for these purposes. Ideally, crawlers should gather
coherent captures of entire Web sites, but the
politeness etiquette and completeness requirement
mandate very slow, long-duration crawling while Web
sites undergo changes. This paper presents the SHARC
framework for assessing the data quality in Web
archives and for tuning capturing strategies toward
better quality with given resources. We define data
quality measures, characterize their properties, and
develop a suite of quality-conscious scheduling
strategies for archive crawling.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Elmeleegy:2011:HRT,
author = "Hazem Elmeleegy and Jayant Madhavan and Alon Halevy",
title = "Harvesting relational tables from lists on the {Web}",
journal = j-VLDB-J,
volume = "20",
number = "2",
pages = "209--226",
month = apr,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0223-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 13 17:51:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A large number of web pages contain data structured in
the form of ``lists''. Many such lists can be further
split into multi-column tables, which can then be used
in more semantically meaningful tasks. However,
harvesting relational tables from such lists can be a
challenging task. The lists are manually generated and
hence need not have well-defined templates--they have
inconsistent delimiters (if any) and often have missing
information. We propose a novel technique for
extracting tables from lists. The technique is domain
independent and operates in a fully unsupervised
manner. We first use multiple sources of information to
split individual lines into multiple fields and then,
compare the splits across multiple lines to identify
and fix incorrect splits and bad alignments.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Candea:2011:PPH,
author = "George Candea and Neoklis Polyzotis and Radek
Vingralek",
title = "Predictable performance and high query concurrency for
data analytics",
journal = j-VLDB-J,
volume = "20",
number = "2",
pages = "227--248",
month = apr,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0221-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 13 17:51:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Conventional data warehouses employ the
query-at-a-time model, which maps each query to a
distinct physical plan. When several queries execute
concurrently, this model introduces contention and
thrashing, because the physical plans--unaware of each
other--compete for access to the underlying I/O and
computation resources. As a result, while modern
systems can efficiently optimize and evaluate a single
complex data analysis query, their performance suffers
significantly and can be highly erratic when multiple
complex queries run at the same time. We present in
this paper Cjoin, a new design that substantially
improves throughput in large-scale data analytics
systems processing many concurrent join queries.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2011:UAR,
author = "Jian Li and Barna Saha and Amol Deshpande",
title = "A unified approach to ranking in probabilistic
databases",
journal = j-VLDB-J,
volume = "20",
number = "2",
pages = "249--275",
month = apr,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0220-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 13 17:51:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Ranking is a fundamental operation in data analysis
and decision support and plays an even more crucial
role if the dataset being explored exhibits
uncertainty. This has led to much work in understanding
how to rank the tuples in a probabilistic dataset in
recent years. In this article, we present a unified
approach to ranking and top-k query processing in
probabilistic databases by viewing it as a
multi-criterion optimization problem and by deriving a
set of features that capture the key properties of a
probabilistic dataset that dictate the ranked result.
We contend that a single, specific ranking function may
not suffice for probabilistic databases, and we instead
propose two parameterized ranking functions, called PRF
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gottlob:2011:NOS,
author = "Georg Gottlob and Reinhard Pichler and Vadim
Savenkov",
title = "Normalization and optimization of schema mappings",
journal = j-VLDB-J,
volume = "20",
number = "2",
pages = "277--302",
month = apr,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0226-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 13 17:51:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Schema mappings are high-level specifications that
describe the relationship between database schemas.
They are an important tool in several areas of database
research, notably in data integration and data
exchange. However, a concrete theory of schema mapping
optimization including the formulation of optimality
criteria and the construction of algorithms for
computing optimal schema mappings is completely lacking
to date. The goal of this work is to fill this gap. We
start by presenting a system of rewrite rules to
minimize sets of source-to-target tuple-generating
dependencies. Moreover, we show that the result of this
minimization is unique up to variable renaming. Hence,
our optimization also yields a schema mapping
normalization.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Cho:2011:LRM,
author = "Chung-Wen Cho and Yi-Hung Wu and Show-Jane Yen and
Ying Zheng and Arbee L. Chen",
title = "On-line rule matching for event prediction",
journal = j-VLDB-J,
volume = "20",
number = "3",
pages = "303--334",
month = jun,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0197-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 14 11:27:46 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The prediction of future events has great importance
in many applications. The prediction is based on
episode rules which are composed of events and two time
constraints which require all the events in the episode
rule and in the predicate of the rule to occur in a
time interval, respectively. In an event stream, a
sequence of events which matches the predicate of the
rule satisfying the specified time constraint is called
an occurrence of the predicate. After finding the
occurrence, the consequent event which will occur in a
time interval can be predicted. However, the time
intervals computed from some occurrences for predicting
the event can be contained in the time intervals
computed from other occurrence and become redundant.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Liu:2011:MLD,
author = "Jun Liu and Lu Jiang and Zhaohui Wu and Qinghua Zheng
and Yanan Qian",
title = "Mining learning-dependency between knowledge units
from text",
journal = j-VLDB-J,
volume = "20",
number = "3",
pages = "335--345",
month = jun,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0198-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 14 11:27:46 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Identifying learning-dependency among the knowledge
units (KU) is a preliminary requirement of navigation
learning. Methods based on link mining lack the ability
of discovering such dependencies among knowledge units
that are arranged in a linear way in the text. In this
paper, we propose a method of mining the learning-
dependencies among the KU from text document. This
method is based on two features that we found and
studied from the KU and the learning-dependencies among
them. They are the distributional asymmetry of the
domain terms and the local nature of the
learning-dependency, respectively. Our method consists
of three stages, (1) Build document association
relationship by calculating the distributional
asymmetry of the domain terms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2011:LBM,
author = "Rui Wang and Betty Salzberg and David Lomet",
title = "Log-based middleware server recovery with transaction
support",
journal = j-VLDB-J,
volume = "20",
number = "3",
pages = "347--370",
month = jun,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0199-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 14 11:27:46 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Providing enterprises with reliable and available
Web-based application programs is a challenge.
Applications are traditionally spread over multiple
nodes, from user (client), to middle tier servers, to
back end transaction systems, e.g. databases. It has
proven very difficult to ensure that these applications
persist across system crashes so that ``exactly once''
execution is produced, always important and sometimes
essential, e.g., in the financial area. Our system
provides a framework for exactly once execution of
multi-tier Web applications, built on a commercially
available Web infrastructure. Its capabilities include
low logging overhead, recovery isolation
(independence), and consistency between mid-tier and
transactional back end.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gao:2011:CVN,
author = "Yunjun Gao and Baihua Zheng and Gencai Chen and Qing
Li and Xiaofa Guo",
title = "Continuous visible nearest neighbor query processing
in spatial databases",
journal = j-VLDB-J,
volume = "20",
number = "3",
pages = "371--396",
month = jun,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0200-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 14 11:27:46 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we identify and solve a new type of
spatial queries, called continuous visible nearest
neighbor (CVNN) search. Given a data set P, an obstacle
set O, and a query line segment q in a two-dimensional
space, a CVNN query returns a set of $$ {\langle p, R
\rangle } $$ tuples such that \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Luo:2011:QRI,
author = "Bo Luo and Dongwon Lee and Wang-Chien Lee and Peng
Liu",
title = "{QFilter}: rewriting insecure {XML} queries to secure
ones using non-deterministic finite automata",
journal = j-VLDB-J,
volume = "20",
number = "3",
pages = "397--415",
month = jun,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0202-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 14 11:27:46 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we ask whether XML access control can
be supported when underlying (XML or relational)
storage system does not provide adequate security
features and propose three alternative solutions
--primitive, pre-processing, and post-processing.
Toward that scenario, in particular, we advocate a
scalable and effective pre-processing approach, called
QFilter. QFilter is based on non-deterministic finite
automata (NFA) and rewrites user's queries such that
parts violating access control rules are pre-pruned.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Motahari-Nezhad:2011:ECP,
author = "Hamid Reza Motahari-Nezhad and Regis Saint-Paul and
Fabio Casati and Boualem Benatallah",
title = "Event correlation for process discovery from web
service interaction logs",
journal = j-VLDB-J,
volume = "20",
number = "3",
pages = "417--444",
month = jun,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0203-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 14 11:27:46 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Understanding, analyzing, and ultimately improving
business processes is a goal of enterprises today.
These tasks are challenging as business processes in
modern enterprises are implemented over several
applications and Web services, and the information
about process execution is scattered across several
data sources. Understanding modern business processes
entails identifying the correlation between events in
data sources in the context of business processes
(event correlation is the process of finding
relationships between events that belong to the same
process execution instance). In this paper, we
investigate the problem of event correlation for
business processes that are realized through the
interactions of a set of Web services.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chakrabarti:2011:IDQ,
author = "Soumen Chakrabarti and Amit Pathak and Manish Gupta",
title = "Index design and query processing for graph
conductance search",
journal = j-VLDB-J,
volume = "20",
number = "3",
pages = "445--470",
month = jun,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0204-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 14 11:27:46 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Graph conductance queries, also known as personalized
PageRank and related to random walks with restarts,
were originally proposed to assign a hyperlink-based
prestige score to Web pages. More general forms of such
queries are also very useful for ranking in
entity-relation (ER) graphs used to represent
relational, XML and hypertext data. Evaluation of
PageRank usually involves a global eigen computation.
If the graph is even moderately large, interactive
response times may not be possible. Recently, the need
for interactive PageRank evaluation has increased. The
graph may be fully known only when the query is
submitted. Browsing actions of the user may change some
inputs to the PageRank computation dynamically.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2011:PAD,
author = "Shaoping Chen and Yi-Cheng Tu and Yuni Xia",
title = "Performance analysis of a dual-tree algorithm for
computing spatial distance histograms",
journal = j-VLDB-J,
volume = "20",
number = "4",
pages = "471--494",
month = aug,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0205-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Sep 16 19:01:00 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Many scientific and engineering fields produce large
volume of spatiotemporal data. The storage, retrieval,
and analysis of such data impose great challenges to
database systems design. Analysis of scientific
spatiotemporal data often involves computing functions
of all point-to-point interactions. One such analytics,
the Spatial Distance Histogram (SDH), is of vital
importance to scientific discovery. Recently,
algorithms for efficient SDH processing in large-scale
scientific databases have been proposed. These
algorithms adopt a recursive tree-traversing strategy
to process point-to-point distances in the visited tree
nodes in batches, thus require less time when compared
to the brute-force approach where all pairwise
distances have to be computed.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fan:2011:DCR,
author = "Wenfei Fan and Hong Gao and Xibei Jia and Jianzhong Li
and Shuai Ma",
title = "Dynamic constraints for record matching",
journal = j-VLDB-J,
volume = "20",
number = "4",
pages = "495--520",
month = aug,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0206-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Sep 16 19:01:00 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper investigates constraints for matching
records from unreliable data sources. (a) We introduce
a class of matching dependencies (mds) for specifying
the semantics of unreliable data. As opposed to static
constraints for schema design, mds are developed for
record matching, and are defined in terms of similarity
predicates and a dynamic semantics. (b) We identify a
special case of mds, referred to as relative candidate
keys (rcks), to determine what attributes to compare
and how to compare them when matching records across
possibly different relations. (c) We propose a
mechanism for inferring mds, a departure from
traditional implication analysis, such that when we
cannot match records by comparing attributes that
contain errors, we may still find matches by using
other, more reliable attributes.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Cheng:2011:FGQ,
author = "James Cheng and Yiping Ke and Ada Wai-Chee Fu and
Jeffrey Xu Yu",
title = "Fast graph query processing with a low-cost index",
journal = j-VLDB-J,
volume = "20",
number = "4",
pages = "521--539",
month = aug,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0212-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Sep 16 19:01:00 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper studies the problem of processing
supergraph queries, that is, given a database
containing a set of graphs, find all the graphs in the
database of which the query graph is a supergraph.
Existing works usually construct an index and performs
a filtering-and-verification process, which still
requires many subgraph isomorphism testings. There are
also significant overheads in both index construction
and maintenance. In this paper, we design a graph
querying system that achieves both fast indexing and
efficient query processing. The index is constructed by
a simple but fast method of extracting the commonality
among the graphs, which does not involve any costly
operation such as graph mining.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mascetti:2011:PGS,
author = "Sergio Mascetti and Dario Freni and Claudio Bettini
and X. Sean Wang and Sushil Jajodia",
title = "Privacy in geo-social networks: proximity notification
with untrusted service providers and curious buddies",
journal = j-VLDB-J,
volume = "20",
number = "4",
pages = "541--566",
month = aug,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0213-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Sep 16 19:01:00 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A major feature of the emerging geo-social networks is
the ability to notify a user when any of his friends
(also called buddies) happens to be geographically in
proximity. This proximity service is usually offered by
the network itself or by a third party service provider
(SP) using location data acquired from the users. This
paper provides a rigorous theoretical and experimental
analysis of the existing solutions for the location
privacy problem in proximity services. This is a
serious problem for users who do not trust the SP to
handle their location data and would only like to
release their location information in a generalized
form to participating buddies.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mohammed:2011:AMG,
author = "Noman Mohammed and Benjamin C. Fung and Mourad
Debbabi",
title = "Anonymity meets game theory: secure data integration
with malicious participants",
journal = j-VLDB-J,
volume = "20",
number = "4",
pages = "567--588",
month = aug,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0214-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Sep 16 19:01:00 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Data integration methods enable different data
providers to flexibly integrate their expertise and
deliver highly customizable services to their
customers. Nonetheless, combining data from different
sources could potentially reveal person-specific
sensitive information. In VLDBJ 2006, Jiang and Clifton
(Very Large Data Bases J (VLDBJ) 15(4):316---333, 2006)
propose a secure Distributed k-Anonymity (DkA)
framework for integrating two private data tables to a
k-anonymous table in which each private table is a
vertical partition on the same set of records. Their
proposed DkA framework is not scalable to large data
sets. Moreover, DkA is limited to a two-party scenario
and the parties are assumed to be semi-honest.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ahmad:2011:IAS,
author = "Mumtaz Ahmad and Ashraf Aboulnaga and Shivnath Babu
and Kamesh Munagala",
title = "Interaction-aware scheduling of report-generation
workloads",
journal = j-VLDB-J,
volume = "20",
number = "4",
pages = "589--615",
month = aug,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0217-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Sep 16 19:01:00 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The typical workload in a database system consists of
a mix of multiple queries of different types that run
concurrently. Interactions among the different queries
in a query mix can have a significant impact on
database performance. Hence, optimizing database
performance requires reasoning about query mixes rather
than considering queries individually. Current database
systems lack the ability to do such reasoning. We
propose a new approach based on planning experiments
and statistical modeling to capture the impact of query
interactions. Our approach requires no prior
assumptions about the internal workings of the database
system or the nature and cause of query interactions,
making it portable across systems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2011:EFF,
author = "Guoliang Li and Shengyue Ji and Chen Li and Jianhua
Feng",
title = "Efficient fuzzy full-text type-ahead search",
journal = j-VLDB-J,
volume = "20",
number = "4",
pages = "617--640",
month = aug,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0218-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Sep 16 19:01:00 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Traditional information systems return answers after a
user submits a complete query. Users often feel ``left
in the dark'' when they have limited knowledge about
the underlying data and have to use a try-and-see
approach for finding information. A recent trend of
supporting autocomplete in these systems is a first
step toward solving this problem. In this paper, we
study a new information-access paradigm, called
``type-ahead search'' in which the system searches the
underlying data ``on the fly'' as the user types in
query keywords. It extends autocomplete interfaces by
allowing keywords to appear at different places in the
underlying data.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Guting:2011:SID,
author = "Ralf Hartmut G{\"u}ting and Nikos Mamoulis",
title = "Special issue on data management for mobile services",
journal = j-VLDB-J,
volume = "20",
number = "5",
pages = "641--642",
month = oct,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0250-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Dec 15 07:28:25 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Popa:2011:INT,
author = "Iulian Sandu Popa and Karine Zeitouni and Vincent Oria
and Dominique Barth and Sandrine Vial",
title = "Indexing in-network trajectory flows",
journal = j-VLDB-J,
volume = "20",
number = "5",
pages = "643--669",
month = oct,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0236-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Dec 15 07:28:25 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lange:2011:ERT,
author = "Ralph Lange and Frank D{\"u}rr and Kurt Rothermel",
title = "Efficient real-time trajectory tracking",
journal = j-VLDB-J,
volume = "20",
number = "5",
pages = "671--694",
month = oct,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0237-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Dec 15 07:28:25 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Giannotti:2011:UCH,
author = "Fosca Giannotti and Mirco Nanni and Dino Pedreschi and
Fabio Pinelli and Chiara Renso and Salvatore Rinzivillo
and Roberto Trasarti",
title = "Unveiling the complexity of human mobility by querying
and mining massive trajectory data",
journal = j-VLDB-J,
volume = "20",
number = "5",
pages = "695--719",
month = oct,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0244-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Dec 15 07:28:25 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Timko:2011:SSA,
author = "Igor Timko and Michael B{\"o}hlen and Johann Gamper",
title = "Sequenced spatiotemporal aggregation for coarse query
granularities",
journal = j-VLDB-J,
volume = "20",
number = "5",
pages = "721--741",
month = oct,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0247-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Dec 15 07:28:25 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Guo:2011:DBS,
author = "Xi Guo and Baihua Zheng and Yoshiharu Ishikawa and
Yunjun Gao",
title = "Direction-based surrounder queries for mobile
recommendations",
journal = j-VLDB-J,
volume = "20",
number = "5",
pages = "743--766",
month = oct,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0241-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Dec 15 07:28:25 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Trajcevski:2011:RCN,
author = "Goce Trajcevski and Roberto Tamassia and Isabel F.
Cruz and Peter Scheuermann and David Hartglass and
Christopher Zamierowski",
title = "Ranking continuous nearest neighbors for uncertain
trajectories",
journal = j-VLDB-J,
volume = "20",
number = "5",
pages = "767--791",
month = oct,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0249-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Dec 15 07:28:25 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Rao:2011:STE,
author = "Weixiong Rao and Lei Chen and Ada Wai-Chee Fu",
title = "{STAIRS}: {Towards} efficient full-text filtering and
dissemination in {DHT} environments",
journal = j-VLDB-J,
volume = "20",
number = "6",
pages = "793--817",
month = dec,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0224-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Dec 15 07:28:26 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Nowadays ``live'' content, such as weblog, wikipedia,
and news, is ubiquitous in the Internet. Providing
users with relevant content in a timely manner becomes
a challenging problem. Differing from Web search
technologies and RSS feeds/reader applications, this
paper envisions a personalized full-text content
filtering and dissemination system in a highly
distributed environment such as a Distributed Hash
Table (DHT) based Peer-to-Peer (P2P) Network. Users
subscribe to their interested content by specifying
input keywords and thresholds as filters. Then, content
is disseminated to those users having interest in it.
In the literature, full-text document publishing in
DHTs has suffered for a long time from the high cost of
forwarding a document to home nodes of all distinct
terms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lian:2011:STS,
author = "Xiang Lian and Lei Chen",
title = "Shooting top-$k$ stars in uncertain databases",
journal = j-VLDB-J,
volume = "20",
number = "6",
pages = "819--840",
month = dec,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0225-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Dec 15 07:28:26 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Query processing in the uncertain database has played
an important role in many real-world applications due
to the wide existence of uncertain data. Although many
previous techniques can correctly handle precise data,
they are not directly applicable to the uncertain
scenario. In this article, we investigate and propose a
novel query, namely probabilistic top-k star (PTkS)
query, which aims to retrieve k objects in an uncertain
database that are ``closest'' to a static/dynamic query
point, considering both distance and probability
aspects. In order to efficiently answer PTkS queries
with a static/moving query point, we propose effective
pruning methods to reduce the PTkS search space, which
can be seamlessly integrated into an efficient query
procedure.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Perez-Sorrosal:2011:ESC,
author = "Francisco Perez-Sorrosal and Marta Pati{\~n}o-Martinez
and Ricardo Jimenez-Peris and Bettina Kemme",
title = "Elastic {SI-Cache}: consistent and scalable caching in
multi-tier architectures",
journal = j-VLDB-J,
volume = "20",
number = "6",
pages = "841--865",
month = dec,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0228-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Dec 15 07:28:26 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The new vision of cloud computing demands scalable,
available and autonomic software platforms in order to
deploy applications and services accessible anywhere
and anytime. Multi-tier architectures are an important
building block for many applications that are deployed
in the cloud. This paper presents a novel caching and
replication infrastructure that facilitates the
scalable and elastic deployment of multi-tier
architectures. Our Elastic SI-Cache is a novel
multi-version cache that attains high performance and
consistency in multi-tier systems. In contrast to most
existing caches, Elastic SI-Cache provides snapshot
isolation coherently across all tiers. Furthermore,
Elastic SI-Cache supports scalable replication of the
different tiers where replicas can be added or removed
dynamically as needed, making the cache amenable for
cloud computing environments.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Moga:2011:USC,
author = "Alexandru Moga and Irina Botan and Nesime Tatbul",
title = "{UpStream}: storage-centric load management for
streaming applications with update semantics",
journal = j-VLDB-J,
volume = "20",
number = "6",
pages = "867--892",
month = dec,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0229-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Dec 15 07:28:26 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper addresses the problem of minimizing the
staleness of query results for streaming applications
with update semantics under overload conditions.
Staleness is a measure of how out-of-date the results
are compared with the latest data arriving on the
input. Real-time streaming applications are subject to
overload due to unpredictably increasing data rates,
while in many of them, we observe that data streams and
queries in fact exhibit ``update semantics'' (i.e., the
latest input data are all that really matters when
producing a query result). Under such semantics,
overload will cause staleness to build up. The key to
avoid this is to exploit the update semantics of
applications as early as possible in the processing
pipeline.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wong:2011:MBR,
author = "Raymond Chi-Wing Wong and M. Tamer {\"O}zsu and Ada
Wai-Chee Fu and Philip S. Yu and Lian Liu and Yubao
Liu",
title = "Maximizing bichromatic reverse nearest neighbor for
{Lp-norm} in two- and three-dimensional spaces",
journal = j-VLDB-J,
volume = "20",
number = "6",
pages = "893--919",
month = dec,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0230-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Dec 15 07:28:26 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Bichromatic reverse nearest neighbor (BRNN) has been
extensively studied in spatial database literature. In
this paper, we study a related problem called MaxBRNN:
find an optimal region that maximizes the size of BRNNs
for L p -norm in two- and three- dimensional spaces.
Such a problem has many real-life applications,
including the problem of finding a new server point
that attracts as many customers as possible by
proximity.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Tiakas:2011:PPS,
author = "Eleftherios Tiakas and Apostolos N. Papadopoulos and
Yannis Manolopoulos",
title = "Progressive processing of subspace dominating
queries",
journal = j-VLDB-J,
volume = "20",
number = "6",
pages = "921--948",
month = dec,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0231-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Dec 15 07:28:26 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A top-k dominating query reports the k items with the
highest domination score. Algorithms for efficient
processing of this query have been recently proposed in
the literature. Those methods, either index based or
index free, apply a series of pruning criteria toward
efficient processing. However, they are characterized
by several limitations, such as (1) they lack
progressiveness (they report the k best items at the
end of the processing), (2) they require a
multi-dimensional index or they build a grid-based
index on-the-fly, which suffers from performance
degradation, especially in high dimensionalities, and
(3) they do not support vertically decomposed data. In
this paper, we design efficient algorithms that can
handle any subset of the dimensions in a progressive
manner.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mueller:2012:SNF,
author = "Rene Mueller and Jens Teubner and Gustavo Alonso",
title = "Sorting networks on {FPGAs}",
journal = j-VLDB-J,
volume = "21",
number = "1",
pages = "1--23",
month = feb,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0232-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jan 31 06:48:57 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Computer architectures are quickly changing toward
heterogeneous many-core systems. Such a trend opens up
interesting opportunities but also raises immense
challenges since the efficient use of heterogeneous
many-core systems is not a trivial problem.
Software-configurable microprocessors and FPGAs add
further diversity but also increase complexity. In this
paper, we explore the use of sorting networks on
field-programmable gate arrays (FPGAs). FPGAs are very
versatile in terms of how they can be used and can also
be added as additional processing units in standard CPU
sockets. Our results indicate that efficient usage of
FPGAs involves non-trivial aspects such as having the
right computation model (a sorting network in this
case); a careful implementation that balances all the
design constraints in an FPGA; and the proper
integration strategy to link the FPGA to the rest of
the system.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Georgoulas:2012:DSE,
author = "Konstantinos Georgoulas and Yannis Kotidis",
title = "Distributed similarity estimation using derived
dimensions",
journal = j-VLDB-J,
volume = "21",
number = "1",
pages = "25--50",
month = feb,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0233-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jan 31 06:48:57 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Computing the similarity between data objects is a
fundamental operation for many distributed applications
such as those on the World Wide Web, in Peer-to-Peer
networks, or even in Sensor Networks. In our work, we
provide a framework based on Random Hyperplane
Projection (RHP) that permits continuous computation of
similarity estimates (using the cosine similarity or
the correlation coefficient as the preferred similarity
metric) between data descriptions that are streamed
from remote sites. These estimates are computed at a
monitoring node, without the need for transmitting the
actual data values. The original RHP framework is data
agnostic and works for arbitrary data sets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Deutch:2012:TIT,
author = "Daniel Deutch and Tova Milo",
title = "Type inference and type checking for queries over
execution traces",
journal = j-VLDB-J,
volume = "21",
number = "1",
pages = "51--68",
month = feb,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0234-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jan 31 06:48:57 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We study here Type Inference and Type Checking for
queries over the execution traces of Business
Processes. We define formal models for such execution
traces, allowing to capture various realistic scenarios
of partial information about these traces. We then
define corresponding notions of types, and the problems
of type inference and type checking in this context. We
further provide a comprehensive study of the
decidability and complexity of these problems, in
various cases, and suggest efficient algorithms where
possible.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Cheema:2012:CRN,
author = "Muhammad Aamir Cheema and Wenjie Zhang and Xuemin Lin
and Ying Zhang and Xuefei Li",
title = "Continuous reverse $k$ nearest neighbors queries in
{Euclidean} space and in spatial networks",
journal = j-VLDB-J,
volume = "21",
number = "1",
pages = "69--95",
month = feb,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0235-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jan 31 06:48:57 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we study the problem of continuous
monitoring of reverse k nearest neighbors queries in
Euclidean space as well as in spatial networks.
Existing techniques are sensitive toward objects and
queries movement. For example, the results of a query
are to be recomputed whenever the query changes its
location. We present a framework for continuous reverse
k nearest neighbor (RkNN) queries by assigning each
object and query with a safe region such that the
expensive recomputation is not required as long as the
query and objects remain in their respective safe
regions. This significantly improves the computation
cost. As a byproduct, our framework also reduces the
communication cost in client---server architectures
because an object does not report its location to the
server unless it leaves its safe region or the server
sends a location update request.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zou:2012:APM,
author = "Lei Zou and Lei Chen and M. Tamer {\"O}zsu and Dongyan
Zhao",
title = "Answering pattern match queries in large graph
databases via graph embedding",
journal = j-VLDB-J,
volume = "21",
number = "1",
pages = "97--120",
month = feb,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0238-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jan 31 06:48:57 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The growing popularity of graph databases has
generated interesting data management problems, such as
subgraph search, shortest path query, reachability
verification, and pattern matching. Among these, a
pattern match query is more flexible compared with a
subgraph search and more informative compared with a
shortest path or a reachability query. In this paper,
we address distance-based pattern match queries over a
large data graph G. Due to the huge search space, we
adopt a filter-and-refine framework to answer a pattern
match query over a large graph. We first find a set of
candidate matches by a graph embedding technique and
then evaluate these to find the exact matches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hartmann:2012:DES,
author = "Sven Hartmann and Markus Kirchberg and Sebastian
Link",
title = "Design by example for {SQL} table definitions with
functional dependencies",
journal = j-VLDB-J,
volume = "21",
number = "1",
pages = "121--144",
month = feb,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0239-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jan 31 06:48:57 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A database is C-Armstrong for a given set of
constraints in a class C if it satisfies every
constraint of the set and violates every constraint in
C not implied by the set. Therefore, Armstrong
databases are test data that perfectly illustrate the
current perceptions about the semantics of a schema. We
extend the existing theory of Armstrong relations to a
toolbox of Armstrong tables. That is, we investigate
structural and computational properties of Armstrong
tables for the class of functional dependencies (FDs)
over SQL tables. Relations are special instances of SQL
tables with no duplicate rows and no null value
occurrences.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Guravannavar:2012:WSO,
author = "Ravindra Guravannavar and S. Sudarshan and Ajit A.
Diwan and Ch. Sobhan Babu",
title = "Which sort orders are interesting?",
journal = j-VLDB-J,
volume = "21",
number = "1",
pages = "145--165",
month = feb,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0240-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jan 31 06:48:57 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Sort orders play an important role in query
evaluation. Algorithms that rely on sorting are widely
used to implement joins, grouping, duplicate
elimination and other set operations. The notion of
interesting orders has allowed query optimizers to
consider plans that could be locally sub-optimal, but
produce ordered output beneficial for other operators,
and thus be part of a globally optimal plan. However,
the number of interesting orders for most operators is
factorial in the number of attributes involved.
Optimizer implementations use heuristics to prune the
number of interesting orders, but the quality of the
heuristics is unclear. Increasingly complex decision
support queries and increasing use of query-covering
indices, which provide multiple alternative sort orders
for relations, motivate us to better address the
problem of choosing interesting orders.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Atzeni:2012:SIB,
author = "Paolo Atzeni and Elisa Bertino and Masaru Kitsuregawa
and Kian-Lee Tan",
title = "Special issue: best papers of {VLDB 2010}",
journal = j-VLDB-J,
volume = "21",
number = "2",
pages = "167--168",
month = apr,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0267-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 23 08:02:21 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bu:2012:HAL,
author = "Yingyi Bu and Bill Howe and Magdalena Balazinska and
Michael D. Ernst",
title = "The {HaLoop} approach to large-scale iterative data
analysis",
journal = j-VLDB-J,
volume = "21",
number = "2",
pages = "169--190",
month = apr,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0269-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 23 08:02:21 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The growing demand for large-scale data mining and
data analysis applications has led both industry and
academia to design new types of highly scalable
data-intensive computing platforms. MapReduce has
enjoyed particular success. However, MapReduce lacks
built-in support for iterative programs, which arise
naturally in many applications including data mining,
web ranking, graph analysis, and model fitting. This
paper (This is an extended version of the VLDB 2010
paper ``HaLoop: Efficient Iterative Data Processing on
Large Clusters'' PVLDB 3(1):285---296, 2010.) presents
HaLoop, a modified version of the Hadoop MapReduce
framework, that is designed to serve these
applications. HaLoop allows iterative applications to
be assembled from existing Hadoop programs without
modification, and significantly improves their
efficiency by providing inter-iteration caching
mechanisms and a loop-aware scheduler to exploit these
caches. HaLoop retains the fault-tolerance properties
of MapReduce through automatic cache recovery and task
re-execution. We evaluated HaLoop on a variety of real
applications and real datasets. Compared with Hadoop,
on average, HaLoop improved runtimes by a factor of
1.85 and shuffled only 4 \% as much data between
mappers and reducers in the applications that we
tested.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Alexe:2012:MCI,
author = "Bogdan Alexe and Mauricio Hern{\'a}ndez and Lucian
Popa and Wang-Chiew Tan",
title = "{MapMerge}: correlating independent schema mappings",
journal = j-VLDB-J,
volume = "21",
number = "2",
pages = "191--211",
month = apr,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0264-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 23 08:02:21 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "One of the main steps toward integration or exchange
of data is to design the mappings that describe the
(often complex) relationships between the source
schemas or formats and the desired target schema. In
this paper, we introduce a new operator, called
MapMerge, that can be used to correlate multiple,
independently designed schema mappings of smaller scope
into larger schema mappings. This allows a more modular
construction of complex mappings from various types of
smaller mappings such as schema correspondences
produced by a schema matcher or pre-existing mappings
that were designed by either a human user or via
mapping tools. In particular, the new operator also
enables a new ``divide-and-merge'' paradigm for mapping
creation, where the design is divided (on purpose) into
smaller components that are easier to create and
understand and where MapMerge is used to automatically
generate a meaningful overall mapping. We describe our
MapMerge algorithm and demonstrate the feasibility of
our implementation on several real and synthetic
mapping scenarios. In our experiments, we make use of a
novel similarity measure between two database instances
with different schemas that quantifies the preservation
of data associations. We show experimentally that
MapMerge improves the quality of the schema mappings,
by significantly increasing the similarity between the
input source instance and the generated target
instance. Finally, we provide a new algorithm that
combines MapMerge with schema mapping composition to
correlate flows of schema mappings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fan:2012:TCF,
author = "Wenfei Fan and Jianzhong Li and Shuai Ma and Nan Tang
and Wenyuan Yu",
title = "Towards certain fixes with editing rules and master
data",
journal = j-VLDB-J,
volume = "21",
number = "2",
pages = "213--238",
month = apr,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0253-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 23 08:02:21 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A variety of integrity constraints have been studied
for data cleaning. While these constraints can detect
the presence of errors, they fall short of guiding us
to correct the errors. Indeed, data repairing based on
these constraints may not find certain fixes that are
guaranteed correct, and worse still, may even introduce
new errors when attempting to repair the data. We
propose a method for finding certain fixes, based on
master data, a notion of certain regions, and a class
of editing rules. A certain region is a set of
attributes that are assured correct by the users. Given
a certain region and master data, editing rules tell us
what attributes to fix and how to update them. We show
how the method can be used in data monitoring and
enrichment. We also develop techniques for reasoning
about editing rules, to decide whether they lead to a
unique fix and whether they are able to fix all the
attributes in a tuple, relative to master data and a
certain region. Furthermore, we present a framework and
an algorithm to find certain fixes, by interacting with
the users to ensure that one of the certain regions is
correct. We experimentally verify the effectiveness and
scalability of the algorithm.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Johnson:2012:SWA,
author = "Ryan Johnson and Ippokratis Pandis and Radu Stoica and
Manos Athanassoulis and Anastasia Ailamaki",
title = "Scalability of write-ahead logging on multicore and
multisocket hardware",
journal = j-VLDB-J,
volume = "21",
number = "2",
pages = "239--263",
month = apr,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0260-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 23 08:02:21 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The shift to multi-core and multi-socket hardware
brings new challenges to database systems, as the
software parallelism determines performance. Even
though database systems traditionally accommodate
simultaneous requests, a multitude of synchronization
barriers serialize execution. Write-ahead logging is a
fundamental, omnipresent component in ARIES-style
concurrency and recovery, and one of the most important
yet-to-be addressed potential bottlenecks, especially
in OLTP workloads making frequent small changes to
data. In this paper, we identify four logging-related
impediments to database system scalability. Each issue
challenges different level in the software
architecture: (a) the high volume of small-sized I/O
requests may saturate the disk, (b) transactions hold
locks while waiting for the log flush, (c) extensive
context switching overwhelms the OS scheduler with
threads executing log I/Os, and (d) contention appears
as transactions serialize accesses to in-memory log
data structures. We demonstrate these problems and
address them with techniques that, when combined,
comprise a holistic, scalable approach to logging. Our
solution achieves a 20---69\% speedup over a modern
database system when running log-intensive workloads,
such as the TPC-B and TATP benchmarks, in a
single-socket multiprocessor server. Moreover, it
achieves log insert throughput over 2.2 GB/s for small
log records on the single-socket server, roughly 20
times higher than the traditional way of accessing the
log using a single mutex. Furthermore, we investigate
techniques on scaling the performance of logging to
multi-socket servers. We present a set of optimizations
which partly ameliorate the latency penalty that comes
with multi-socket hardware, and then we investigate the
feasibility of applying a distributed log buffer design
at the socket level.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2012:AUP,
author = "Su Chen and Beng Chin Ooi and Zhenjie Zhang",
title = "An adaptive updating protocol for reducing moving
object database workload",
journal = j-VLDB-J,
volume = "21",
number = "2",
pages = "265--286",
month = apr,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0257-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Apr 23 08:02:21 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In the last decade, spatio-temporal database research
focuses on the design of effective and efficient
indexing structures in support of location-based
queries such as predictive range queries and nearest
neighbor queries. While a variety of indexing
techniques have been proposed to accelerate the
processing of updates and queries, not much attention
has been paid to the updating protocol, which is
another important factor affecting the system
performance. In this paper, we propose a generic and
adaptive updating protocol for moving object databases
with less number of updates between objects and the
database server, thereby reducing the overall workload
of the system. In contrast to the approach adopted by
most conventional moving object database systems where
the exact locations and velocities last disclosed are
used to predict their motions, we propose the concept
of Spatio-temporal safe region to approximate possible
future locations. Spatio-temporal safe regions provide
larger space of tolerance for moving objects, freeing
them from location and velocity updates as long as the
errors remain predictable in the database. To answer
predictive queries accurately, the server is allowed to
probe the latest status of objects when their safe
regions are inadequate in returning the exact query
results. Spatio-temporal safe regions are calculated
and optimized by the database server with two
contradictory objectives: reducing update workload
while guaranteeing query accuracy and efficiency. To
achieve this, we propose a cost model that estimates
the composition of active and passive updates based on
historical motion records and query distribution. More
system performance improvements can be obtained by
cutting more updates from the clients, when the users
of system are comfortable with incomplete but accuracy
bounded query results. We have conducted extensive
experiments to evaluate our proposal on a variety of
popular indexing structures. The results confirm the
viability, robustness, accuracy and efficiency of our
proposed protocol.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fusco:2012:RTC,
author = "Francesco Fusco and Michail Vlachos and Marc Ph.
Stoecklin",
title = "Real-time creation of bitmap indexes on streaming
network data",
journal = j-VLDB-J,
volume = "21",
number = "3",
pages = "287--307",
month = jun,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0242-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 26 17:39:07 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "High-speed archival and indexing solutions of
streaming traffic are growing in importance for
applications such as monitoring, forensic analysis, and
auditing. Many large institutions require fast
solutions to support expedient analysis of historical
network data, particularly in case of security
breaches. However, ``turning back the clock'' is not a
trivial task. The first major challenge is that such a
technology needs to support data archiving under
extremely high-speed insertion rates. Moreover, the
archives created have to be stored in a compressed
format that is still amenable to indexing and search.
The above requirements make general-purpose databases
unsuitable for this task and dedicated solutions are
required. This work describes a solution for high-speed
archival storage, indexing, and data querying on
network flow information. We make the two following
important contributions: (a) we propose a novel
compressed bitmap index approach that significantly
reduces both CPU load and disk consumption and, (b) we
introduce an online stream reordering mechanism that
further reduces space requirements and improves the
time for data retrieval. The reordering methodology is
based on the principles of locality-sensitive hashing
(LSH) and also of interest for other bitmap creation
techniques. Because of the synergy of these two
components, our solution can sustain data insertion
rates that reach 500,000--1 million records per second.
To put these numbers into perspective, typical
commercial network flow solutions can currently process
20,000--60,000 flows per second. In addition, our
system offers interactive query response times that
enable administrators to perform complex analysis tasks
on the fly. Our technique is directly amenable to
parallel execution, allowing its application in domains
that are challenged by large volumes of historical
measurement data, such as network auditing, traffic
behavior analysis, and large-scale data visualization
in service provider networks.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gordevicus:2012:PTA,
author = "Juozas Gordevi{\v{c}}us and Johann Gamper and Michael
B{\"o}hlen",
title = "Parsimonious temporal aggregation",
journal = j-VLDB-J,
volume = "21",
number = "3",
pages = "309--332",
month = jun,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0243-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 26 17:39:07 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Temporal aggregation is an important operation in
temporal databases, and different variants thereof have
been proposed. In this paper, we introduce a novel
temporal aggregation operator, termed parsimonious
temporal aggregation (PTA), that overcomes major
limitations of existing approaches. PTA takes the
result of instant temporal aggregation (ITA) of size n,
which might be up to twice as large as the argument
relation, and merges similar tuples until a given error
( {\epsilon} ) or size ( c ) bound is reached. The new
operator is data-adaptive and allows the user to
control the trade-off between the result size and the
error introduced by merging. For the precise evaluation
of PTA queries, we propose two dynamic
programming---based algorithms for size- and
error-bounded queries, respectively, with a worst-case
complexity that is quadratic in n. We present two
optimizations that take advantage of temporal gaps and
different aggregation groups and achieve a linear
runtime in experiments with real-world data. For the
quick computation of an approximate PTA answer, we
propose an efficient greedy merging strategy with a
precision that is upper bounded by O (log n ). We
present two algorithms that implement this strategy and
begin to merge as ITA tuples are produced. They require
O ( n log ( c + {\ss} )) time and O ( c + {\ss} )
space, where {\ss} is the size of a read-ahead buffer
and is typically very small. An empirical evaluation on
real-world and synthetic data shows that PTA
considerably reduces the size of the aggregation
result, yet introducing only small errors. The greedy
algorithms are scalable for large data sets and
introduce less error than other approximation
techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hore:2012:SMR,
author = "Bijit Hore and Sharad Mehrotra and Mustafa Canim and
Murat Kantarcioglu",
title = "Secure multidimensional range queries over outsourced
data",
journal = j-VLDB-J,
volume = "21",
number = "3",
pages = "333--358",
month = jun,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0245-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 26 17:39:07 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we study the problem of supporting
multidimensional range queries on encrypted data. The
problem is motivated by secure data outsourcing
applications where a client may store his/her data on a
remote server in encrypted form and want to execute
queries using server's computational capabilities. The
solution approach is to compute a secure indexing tag
of the data by applying bucketization (a generic form
of data partitioning) which prevents the server from
learning exact values but still allows it to check if a
record satisfies the query predicate. Queries are
evaluated in an approximate manner where the returned
set of records may contain some false positives. These
records then need to be weeded out by the client which
comprises the computational overhead of our scheme. We
develop a bucketization procedure for answering
multidimensional range queries on multidimensional
data. For a given bucketization scheme, we derive cost
and disclosure-risk metrics that estimate client's
computational overhead and disclosure risk
respectively. Given a multidimensional dataset, its
bucketization is posed as an optimization problem where
the goal is to minimize the risk of disclosure while
keeping query cost (client's computational overhead)
below a certain user-specified threshold value. We
provide a tunable data bucketization algorithm that
allows the data owner to control the trade-off between
disclosure risk and cost. We also study the trade-off
characteristics through an extensive set of experiments
on real and synthetic data.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hose:2012:SSP,
author = "Katja Hose and Akrivi Vlachou",
title = "A survey of skyline processing in highly distributed
environments",
journal = j-VLDB-J,
volume = "21",
number = "3",
pages = "359--384",
month = jun,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0246-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 26 17:39:07 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "During the last decades, data management and storage
have become increasingly distributed. Advanced query
operators, such as skyline queries, are necessary in
order to help users to handle the huge amount of
available data by identifying a set of interesting data
objects. Skyline query processing in highly distributed
environments poses inherent challenges and demands and
requires non-traditional techniques due to the
distribution of content and the lack of global
knowledge. This paper surveys this interesting and
still evolving research area, so that readers can
easily obtain an overview of the state-of-the-art. We
outline the objectives and the main principles that any
distributed skyline approach has to fulfill, leading to
useful guidelines for developing algorithms for
distributed skyline processing. We review in detail
existing approaches that are applicable for highly
distributed environments, clarify the assumptions of
each approach, and provide a comparative performance
analysis. Moreover, we study the skyline variants each
approach supports. Our analysis leads to a taxonomy of
existing approaches. Finally, we present interesting
research topics on distributed skyline computation that
have not yet been explored.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gong:2012:EMU,
author = "Jian Gong and Reynold Cheng and David W. Cheung",
title = "Efficient management of uncertainty in {XML} schema
matching",
journal = j-VLDB-J,
volume = "21",
number = "3",
pages = "385--409",
month = jun,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0248-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 26 17:39:07 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Despite advances in machine learning technologies a
schema matching result between two database schemas
(e.g., those derived from COMA++) is likely to be
imprecise. In particular, numerous instances of
``possible mappings'' between the schemas may be
derived from the matching result. In this paper, we
study problems related to managing possible mappings
between two heterogeneous XML schemas. First, we study
how to efficiently generate possible mappings for a
given schema matching task. While this problem can be
solved by existing algorithms, we show how to improve
the performance of the solution by using a
divide-and-conquer approach. Second, storing and
querying a large set of possible mappings can incur
large storage and evaluation overhead. For XML schemas,
we observe that their possible mappings often exhibit a
high degree of overlap. We hence propose a novel data
structure, called the block tree, to capture the
commonalities among possible mappings. The block tree
is useful for representing the possible mappings in a
compact manner and can be efficiently generated.
Moreover, it facilitates the evaluation of a
probabilistic twig query (PTQ), which returns the
non-zero probability that a fragment of an XML document
matches a given query. For users who are interested
only in answers with k -highest probabilities, we also
propose the top- k PTQ and present an efficient
solution for it. An extensive evaluation on real-world
data sets shows that our approaches significantly
improve the efficiency of generating, storing, and
querying possible mappings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Cao:2012:SSA,
author = "Yu Cao and Ramadhana Bramandia and Chee-Yong Chan and
Kian-Lee Tan",
title = "Sort-sharing-aware query processing",
journal = j-VLDB-J,
volume = "21",
number = "3",
pages = "411--436",
month = jun,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0251-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 26 17:39:07 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Many database applications require sorting a table (or
relation) over multiple sort orders. Some examples
include creation of multiple indices on a relation,
generation of multiple reports from a table, evaluation
of a complex query that involves multiple instances of
a relation, and batch processing of a set of queries.
In this paper, we study how to optimize multiple
sortings of a table. We investigate the correlation
between sort orders and exploit sort-sharing techniques
of reusing the (partial) work done to sort a table on a
particular order for another order. Specifically, we
introduce a novel and powerful evaluation technique,
called cooperative sorting, that enables sort sharing
between seemingly non-related sort orders.
Subsequently, given a specific set of sort orders, we
determine the best combination of various sort-sharing
techniques so as to minimize the total processing cost.
We also develop techniques to make a traditional query
optimizer extensible so that it will not miss the truly
cheapest execution plan with the sort-sharing (post-)
optimization turned on. We demonstrate the efficiency
of our ideas with a prototype implementation in
PostgreSQL and evaluate the performance using both
TPC-DS benchmark and synthetic data. Our experimental
results show significant performance improvement over
the traditional evaluation scheme.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Feng:2012:TJT,
author = "Jianhua Feng and Jiannan Wang and Guoliang Li",
title = "Trie-join: a trie-based method for efficient string
similarity joins",
journal = j-VLDB-J,
volume = "21",
number = "4",
pages = "437--461",
month = aug,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0252-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 20 14:56:19 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A string similarity join finds similar pairs between
two collections of strings. Many applications, e.g.,
data integration and cleaning, can significantly
benefit from an efficient string-similarity-join
algorithm. In this paper, we study string similarity
joins with edit-distance constraints. Existing methods
usually employ a filter-and-refine framework and suffer
from the following limitations: (1) They are
inefficient for the data sets with short strings (the
average string length is not larger than 30); (2) They
involve large indexes; (3) They are expensive to
support dynamic update of data sets. To address these
problems, we propose a novel method called trie-join,
which can generate results efficiently with small
indexes. We use a trie structure to index the strings
and utilize the trie structure to efficiently find
similar string pairs based on subtrie pruning. We
devise efficient trie-join algorithms and pruning
techniques to achieve high performance. Our method can
be easily extended to support dynamic update of data
sets efficiently. We conducted extensive experiments on
four real data sets. Experimental results show that our
algorithms outperform state-of-the-art methods by an
order of magnitude on the data sets with short
strings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Augsten:2012:WGA,
author = "Nikolaus Augsten and Michael B{\"o}hlen and Curtis
Dyreson and Johann Gamper",
title = "Windowed $ p q$-grams for approximate joins of
data-centric {XML}",
journal = j-VLDB-J,
volume = "21",
number = "4",
pages = "463--488",
month = aug,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0254-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 20 14:56:19 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In data integration applications, a join matches
elements that are common to two data sources. Since
elements are represented slightly different in each
source, an approximate join must be used to do the
matching. For XML data, most existing approximate join
strategies are based on some ordered tree matching
technique, such as the tree edit distance. In
data-centric XML, however, the sibling order is
irrelevant, and two elements should match even if their
subelement order varies. Thus, approximate joins for
data-centric XML must leverage unordered tree matching
techniques. This is computationally hard since the
algorithms cannot rely on a predefined sibling order.
In this paper, we give a solution for approximate joins
based on unordered tree matching. The core of our
solution are windowed pq-grams which are small subtrees
of a specific shape. We develop an efficient technique
to generate windowed pq -grams in a three-step process:
sort the tree, extend the sorted tree with dummy nodes,
and decompose the extended tree into windowed pq
-grams. The windowed pq -grams distance between two
trees is the number of pq -grams that are in one tree
decomposition only. We show that our distance is a
pseudo-metric and empirically demonstrate that it
effectively approximates the unordered tree edit
distance. The approximate join using windowed pq -grams
can be efficiently implemented as an equality join on
strings, which avoids the costly computation of the
distance between every pair of input trees. Experiments
with synthetic and real world data confirm the analytic
results and show the effectiveness and efficiency of
our technique.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhou:2012:ESM,
author = "Xiangmin Zhou and Xiaofang Zhou and Lei Chen and
Athman Bouguettaya",
title = "Efficient subsequence matching over large video
databases",
journal = j-VLDB-J,
volume = "21",
number = "4",
pages = "489--508",
month = aug,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0255-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 20 14:56:19 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Video similarity matching has broad applications such
as copyright detection, news tracking and commercial
monitoring, etc. Among these applications, one typical
task is to detect the local similarity between two
videos without the knowledge on positions and lengths
of each matched subclip pair. However, most studies so
far on video detection investigate the global
similarity between two short clips using a pre-defined
distance function. Although there are a few works on
video subsequence detection, all these proposals fail
to provide an effective query processing mechanism. In
this paper, we first generalize the problem of video
similarity matching. Then, a novel solution called
consistent keyframe matching (CKM) is proposed to solve
the problem of subsequence matching based on video
segmentation. CKM is designed with two goals: (1) good
scalability in terms of the query sequence length and
the size of video database and (2) fast video
subsequence matching in terms of processing time. Good
scalability is achieved by employing a batch query
paradigm, where keyframes sharing the same query space
are summarized and ordered. As such, the redundancy of
data access is eliminated, leading to much faster video
query processing. Fast subsequence matching is achieved
by comparing the keyframes of different video
sequences. Specifically, a keyframe matching graph is
first constructed and then divided into matched
candidate subgraphs. We have evaluated our proposed
approach over a very large real video database.
Extensive experiments demonstrate the effectiveness and
efficiency of our approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yildirim:2012:GSI,
author = "Hilmi Yildirim and Vineet Chaoji and Mohammed J.
Zaki",
title = "{GRAIL}: a scalable index for reachability queries in
very large graphs",
journal = j-VLDB-J,
volume = "21",
number = "4",
pages = "509--534",
month = aug,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0256-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 20 14:56:19 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Given a large directed graph, rapidly answering
reachability queries between source and target nodes is
an important problem. Existing methods for reachability
tradeoff indexing time and space versus query time
performance. However, the biggest limitation of
existing methods is that they do not scale to very
large real-world graphs. We present a simple yet
scalable reachability index, called GRAIL, that is
based on the idea of randomized interval labeling and
that can effectively handle very large graphs. Based on
an extensive set of experiments, we show that while
more sophisticated methods work better on small graphs,
GRAIL is the only index that can scale to millions of
nodes and edges. GRAIL has linear indexing time and
space, and the query time ranges from constant time to
being linear in the graph order and size. Our reference
C++ implementations are open source and available for
download at http://www.code.google.com/p/grail/.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xu:2012:EES,
author = "Jia Xu and Zhenjie Zhang and Anthony K. Tung and Ge
Yu",
title = "Efficient and effective similarity search over
probabilistic data based on {Earth Mover's Distance}",
journal = j-VLDB-J,
volume = "21",
number = "4",
pages = "535--559",
month = aug,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0258-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 20 14:56:19 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Advances in geographical tracking, multimedia
processing, information extraction, and sensor networks
have created a deluge of probabilistic data. While
similarity search is an important tool to support the
manipulation of probabilistic data, it raises new
challenges to traditional relational databases. The
problem stems from the limited effectiveness of the
distance metrics employed by existing database systems.
On the other hand, several more complicated distance
operators have proven their values for better
distinguishing ability in specific probabilistic
domains. In this paper, we discuss the similarity
search problem with respect to Earth Mover's Distance
(EMD). EMD is the most successful distance metric for
probability distribution comparison but is an expensive
operator as it has cubic time complexity. We present a
new database indexing approach to answer EMD-based
similarity queries, including range queries and
$k$-nearest neighbor queries on probabilistic data. Our
solution utilizes primal-dual theory from linear
programming and employs a group of B$^+$ trees for
effective candidate pruning. We also apply our
filtering technique to the processing of continuous
similarity queries, especially with applications to
frame copy detection in real-time videos. Extensive
experiments show that our proposals dramatically
improve the usefulness and scalability of probabilistic
data management.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2012:HOA,
author = "Rui Zhang and Jianzhong Qi and Dan Lin and Wei Wang
and Raymond Chi-Wing Wong",
title = "A highly optimized algorithm for continuous
intersection join queries over moving objects",
journal = j-VLDB-J,
volume = "21",
number = "4",
pages = "561--586",
month = aug,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0259-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 20 14:56:19 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Given two sets of moving objects with nonzero extents,
the continuous intersection join query reports every
pair of intersecting objects, one from each of the two
moving object sets, for every timestamp. This type of
queries is important for a number of applications,
e.g., in the multi-billion dollar computer game
industry, massively multiplayer online games like World
of Warcraft need to monitor the intersection among
players' attack ranges and render players' interaction
in real time. The computational cost of a
straightforward algorithm or an algorithm adapted from
another query type is prohibitive, and answering the
query in real time poses a great challenge. Those
algorithms compute the query answer for either too long
or too short a time interval, which results in either a
very large computation cost per answer update or too
frequent answer updates, respectively. This observation
motivates us to optimize the query processing in the
time dimension. In this study, we achieve this
optimization by introducing the new concept of
time-constrained (TC) processing. Further, TC
processing enables a set of effective improvement
techniques on traditional intersection join algorithms.
Finally, we provide a method to find the optimal value
for an important parameter required in our technique,
the maximum update interval. As a result, we achieve a
highly optimized algorithm for processing continuous
intersection join queries on moving objects. With a
thorough experimental study, we show that our algorithm
outperforms the best adapted existing solution by
several orders of magnitude. We also validate the
accuracy of our cost model and its effectiveness in
optimizing the performance.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lehner:2012:SSL,
author = "Wolfgang Lehner and Michael J. Franklin",
title = "Special section on large-scale analytics",
journal = j-VLDB-J,
volume = "21",
number = "5",
pages = "587--588",
month = oct,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0291-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 22 09:44:31 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wolf:2012:OSM,
author = "Joel Wolf and Andrey Balmin and Deepak Rajan and
Kirsten Hildrum and Rohit Khandekar and Sujay Parekh
and Kun-Lung Wu and Rares Vernica",
title = "On the optimization of schedules for {MapReduce}
workloads in the presence of shared scans",
journal = j-VLDB-J,
volume = "21",
number = "5",
pages = "589--609",
month = oct,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0279-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 22 09:44:31 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We consider MapReduce clusters designed to support
multiple concurrent jobs, concentrating on environments
in which the number of distinct datasets is modest
relative to the number of jobs. In such scenarios, many
individual datasets are likely to be scanned
concurrently by multiple Map phase jobs. As has been
noticed previously, this scenario provides an
opportunity for Map phase jobs to cooperate, sharing
the scans of these datasets, and thus reducing the
costs of such scans. Our paper has three main
contributions over previous work. First, we present a
novel and highly general method for sharing scans and
thus amortizing their costs. This concept, which we
call cyclic piggybacking, has a number of advantages
over the more traditional batching scheme described in
the literature. Second, we notice that the various
subjobs generated in this manner can be assumed in an
optimal schedule to respect a natural chain precedence
ordering. Third, we describe a significant but natural
generalization of the recently introduced FLEX
scheduler for optimizing schedules within the context
of this cyclic piggybacking paradigm, which can be
tailored to a variety of cost metrics. Such cost
metrics include average response time, average stretch,
and any minimax-type metric--a total of 11 separate and
standard metrics in all. Moreover, most of this carries
over in the more general case of overlapping rather
than identical datasets as well, employing what we will
call semi-shared scans. In such scenarios, chain
precedence is replaced by arbitrary precedence, but we
can still handle 8 of the original 11 metrics. The
overall approach, including both cyclic piggybacking
and the FLEX scheduling generalization, is called
CIRCUMFLEX. We describe some practical implementation
strategies. And we evaluate the performance of
CIRCUMFLEX via a variety of simulation and real
benchmark experiments.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhou:2012:SPD,
author = "Jingren Zhou and Nicolas Bruno and Ming-Chuan Wu and
Per-Ake Larson and Ronnie Chaiken and Darren Shakib",
title = "{SCOPE}: parallel databases meet {MapReduce}",
journal = j-VLDB-J,
volume = "21",
number = "5",
pages = "611--636",
month = oct,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0280-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 22 09:44:31 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Companies providing cloud-scale data services have
increasing needs to store and analyze massive data
sets, such as search logs, click streams, and web graph
data. For cost and performance reasons, processing is
typically done on large clusters of tens of thousands
of commodity machines. Such massive data analysis on
large clusters presents new opportunities and
challenges for developing a highly scalable and
efficient distributed computation system that is easy
to program and supports complex system optimization to
maximize performance and reliability. In this paper, we
describe a distributed computation system, Structured
Computations Optimized for Parallel Execution (Scope),
targeted for this type of massive data analysis. Scope
combines benefits from both traditional parallel
databases and MapReduce execution engines to allow easy
programmability and deliver massive scalability and
high performance through advanced optimization. Similar
to parallel databases, the system has a SQL-like
declarative scripting language with no explicit
parallelism, while being amenable to efficient parallel
execution on large clusters. An optimizer is
responsible for converting scripts into efficient
execution plans for the distributed computation engine.
A physical execution plan consists of a directed
acyclic graph of vertices. Execution of the plan is
orchestrated by a job manager that schedules execution
on available machines and provides fault tolerance and
recovery, much like MapReduce systems. Scope is being
used daily for a variety of data analysis and data
mining applications over tens of thousands of machines
at Microsoft, powering Bing, and other online
services.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kang:2012:GEA,
author = "U. Kang and Hanghang Tong and Jimeng Sun and
Ching-Yung Lin and Christos Faloutsos",
title = "{{\tt gbase}}: an efficient analysis platform for
large graphs",
journal = j-VLDB-J,
volume = "21",
number = "5",
pages = "637--650",
month = oct,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0283-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 22 09:44:31 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Graphs appear in numerous applications including cyber
security, the Internet, social networks, protein
networks, recommendation systems, citation networks,
and many more. Graphs with millions or even billions of
nodes and edges are common-place. How to store such
large graphs efficiently? What are the core
operations/queries on those graph? How to answer the
graph queries quickly? We propose Gbase, an efficient
analysis platform for large graphs. The key novelties
lie in (1) our storage and compression scheme for a
parallel, distributed settings and (2) the carefully
chosen graph operations and their efficient
implementations. We designed and implemented an
instance of Gbase using MapReduce\slash Hadoop. Gbase
provides a parallel indexing mechanism for graph
operations that both saves storage space, as well as
accelerates query responses. We run numerous
experiments on real and synthetic graphs, spanning
billions of nodes and edges, and we show that our
proposed Gbase is indeed fast, scalable, and nimble,
with significant savings in space and time.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Tran:2012:CMP,
author = "Thanh T. Tran and Liping Peng and Yanlei Diao and
Andrew Mcgregor and Anna Liu",
title = "{CLARO}: modeling and processing uncertain data
streams",
journal = j-VLDB-J,
volume = "21",
number = "5",
pages = "651--676",
month = oct,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0261-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 22 09:44:31 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Uncertain data streams, where data are incomplete and
imprecise, have been observed in many environments.
Feeding such data streams to existing stream systems
produces results of unknown quality, which is of
paramount concern to monitoring applications. In this
paper, we present the claro system that supports stream
processing for uncertain data naturally captured using
continuous random variables. claro employs a unique
data model that is flexible and allows efficient
computation. Built on this model, we develop evaluation
techniques for relational operators by exploring
statistical theory and approximation. We also consider
query planning for complex queries given an accuracy
requirement. Evaluation results show that our
techniques can achieve high performance while
satisfying accuracy requirements and outperform
state-of-the-art sampling methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Helmer:2012:MSS,
author = "Sven Helmer and Nikolaus Augsten and Michael
B{\"o}hlen",
title = "Measuring structural similarity of semistructured data
based on information-theoretic approaches",
journal = j-VLDB-J,
volume = "21",
number = "5",
pages = "677--702",
month = oct,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0263-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 22 09:44:31 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We propose and experimentally evaluate different
approaches for measuring the structural similarity of
semistructured documents based on information-theoretic
concepts. Common to all approaches is a two-step
procedure: first, we extract and linearize the
structural information from documents, and then, we use
similarity measures that are based on, respectively,
Kolmogorov complexity and Shannon entropy to determine
the distance between the documents. Compared to other
approaches, we are able to achieve a linear run-time
complexity and demonstrate in an experimental
evaluation that the results of our technique in terms
of clustering quality are on a par with or even better
than those of other, slower approaches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Cheema:2012:EPS,
author = "Muhammad Aamir Cheema and Wenjie Zhang and Xuemin Lin
and Ying Zhang",
title = "Efficiently processing snapshot and continuous reverse
$k$ nearest neighbors queries",
journal = j-VLDB-J,
volume = "21",
number = "5",
pages = "703--728",
month = oct,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0265-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 22 09:44:31 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Given a set of objects and a query q, a point p is
called the reverse k nearest neighbor (R k NN) of q if
q is one of the k closest objects of p. In this paper,
we introduce the concept of influence zone that is the
area such that every point inside this area is the R k
NN of q and every point outside this area is not the R
k NN. The influence zone has several applications in
location-based services, marketing and decision support
systems. It can also be used to efficiently process R k
NN queries. First, we present efficient algorithm to
compute the influence zone. Then, based on the
influence zone, we present efficient algorithms to
process R k NN queries that significantly outperform
existing best-known techniques for both the snapshot
and continuous R k NN queries. We also present a
detailed theoretical analysis to analyze the area of
the influence zone and IO costs of our R k NN
processing algorithms. Our experiments demonstrate the
accuracy of our theoretical analysis. This paper is an
extended version of our previous work (Cheema et al. in
Proceedings of ICDE, pp. 577---588, 2011). We make the
following new contributions in this extended version:
(1) we conduct a rigorous complexity analysis and show
that the complexity of one of our proposed algorithms
in Cheema et al. (Proceedings of ICDE, pp. 577---588,
2011) can be reduced from O ( m$^2$ ) to O ( km ) where
m {$>$} k is the number of objects used to compute the
influence zone, (2) we show that our techniques can be
applied to dimensionality higher than two, and (3) we
present efficient techniques to handle data updates.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zheng:2012:SQP,
author = "Kai Zheng and Xiaofang Zhou and Pui Cheong Fung and
Kexin Xie",
title = "Spatial query processing for fuzzy objects",
journal = j-VLDB-J,
volume = "21",
number = "5",
pages = "729--751",
month = oct,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0266-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 22 09:44:31 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Range and nearest neighbor queries are the most common
types of spatial queries, which have been investigated
extensively in the last decades due to its broad range
of applications. In this paper, we study this problem
in the context of fuzzy objects that have
indeterministic boundaries. Fuzzy objects play an
important role in many areas, such as biomedical image
databases and GIS communities. Existing research on
fuzzy objects mainly focuses on modeling basic fuzzy
object types and operations, leaving the processing of
more advanced queries largely untouched. In this paper,
we propose two new kinds of spatial queries for fuzzy
objects, namely single threshold query and continuous
threshold query, to determine the query results which
qualify at a certain probability threshold and within a
probability interval, respectively. For efficient
single threshold query processing, we optimize the
classical R-tree-based search algorithm by deriving
more accurate approximations for the distance function
between fuzzy objects and the query object. To enhance
the performance of continuous threshold queries,
effective pruning rules are developed to reduce the
search space and speed up the candidate refinement
process. The efficiency of our proposed algorithms as
well as the optimization techniques is verified with an
extensive set of experiments using both synthetic and
real datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2012:MFS,
author = "Jianzhong Li and Zhaonian Zou and Hong Gao",
title = "Mining frequent subgraphs over uncertain graph
databases under probabilistic semantics",
journal = j-VLDB-J,
volume = "21",
number = "6",
pages = "753--777",
month = dec,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0268-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 5 08:04:46 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Frequent subgraph mining has been extensively studied
on certain graph data. However, uncertainty is
intrinsic in graph data in practice, but there is very
few work on mining uncertain graph data. This paper
focuses on mining frequent subgraphs over uncertain
graph data under the probabilistic semantics.
Specifically, a measure called $ \varphi $-frequent
probability is introduced to evaluate the degree of
recurrence of subgraphs. Given a set of uncertain
graphs and two real numbers $ 0 < \varphi, \tau < 1$,
the goal is to quickly find all subgraphs with $
\varphi $-frequent probability at least $ \tau $. Due
to the NP-hardness of the problem and to the
\#P-hardness of computing the $ \varphi $-frequent
probability of a subgraph, an approximate mining
algorithm is proposed to produce an $ (\varepsilon,
\delta)$-approximate set $ \Pi $ of ``frequent
subgraphs'', where $ 0 < \varepsilon < \tau $ is error
tolerance, and $ 0 < \delta < 1$ is a confidence bound.
The algorithm guarantees that (1) any frequent subgraph
$S$ is contained in $ \Pi $ with probability at least $
((1 - \delta) / 2)^s$, where $s$ is the number of edges
in $S$; (2) any infrequent subgraph with $ \varphi
$-frequent probability less than $ \tau - \varepsilon $
is contained in $ \Pi $ with probability at most $
\delta / 2$. The theoretical analysis shows that to
obtain any frequent subgraph with probability at least
$ 1 - \Delta $, the input parameter \delta of the
algorithm must be set to at most $ 1 - 2 (1 -
\Delta)^{1 / \ell_{\rm max}}$, where $ 0 < \Delta < 1$,
and $ \ell_{\rm max}$ is the maximum number of edges in
frequent subgraphs. Extensive experiments on real
uncertain graph data verify that the proposed algorithm
is practically efficient and has very high
approximation quality. Moreover, the difference between
the probabilistic semantics and the expected semantics
on mining frequent subgraphs over uncertain graph data
has been discussed in this paper for the first time.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Vergoulis:2012:ARS,
author = "Thanasis Vergoulis and Theodore Dalamagas and Dimitris
Sacharidis and Timos Sellis",
title = "Approximate regional sequence matching for genomic
databases",
journal = j-VLDB-J,
volume = "21",
number = "6",
pages = "779--795",
month = dec,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0270-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 5 08:04:46 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Recent advances in computational biology have raised
sequence matching requirements that result in new types
of sequence database problems. In this work, we
introduce an important class of such problems, the
approximate regional sequence matching (ARSM) problem.
Given a data and a pattern sequence, an ARSM result is
an approximate occurrence of a region of the pattern in
the data sequence under two conditions. First, the
region must contain a predetermined area of the pattern
sequence, termed core. Second, the allowable deviation
between the region of the pattern and its occurrence in
the data sequence depends on the length of the region.
We propose the PS-ARSM method that processes
holistically the regions of a pattern, taking advantage
of their overlaps to efficiently identify the ARSM
results. Its performance is evaluated with respect to
existing techniques adapted to the ARSM problem.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wu:2012:FES,
author = "Dingming Wu and Gao Cong and Christian S. Jensen",
title = "A framework for efficient spatial web object
retrieval",
journal = j-VLDB-J,
volume = "21",
number = "6",
pages = "797--822",
month = dec,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0271-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 5 08:04:46 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The conventional Internet is acquiring a geospatial
dimension. Web documents are being geo-tagged and
geo-referenced objects such as points of interest are
being associated with descriptive text documents. The
resulting fusion of geo-location and documents enables
new kinds of queries that take into account both
location proximity and text relevancy. This paper
proposes a new indexing framework for top-$k$ spatial
text retrieval. The framework leverages the inverted
file for text retrieval and the R-tree for spatial
proximity querying. Several indexing approaches are
explored within this framework. The framework
encompasses algorithms that utilize the proposed
indexes for computing location-aware as well as
region-aware top-$k$ text retrieval queries, thus
taking into account both text relevancy and spatial
proximity to prune the search space. Results of
empirical studies with an implementation of the
framework demonstrate that the paper's proposal is
capable of excellent performance.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Arenas:2012:QLB,
author = "Marcelo Arenas and Jorge P{\'e}rez and Juan Reutter
and Cristian Riveros",
title = "Query language-based inverses of schema mappings:
semantics, computation, and closure properties",
journal = j-VLDB-J,
volume = "21",
number = "6",
pages = "823--842",
month = dec,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0272-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 5 08:04:46 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The inversion of schema mappings has been identified
as one of the fundamental operators for the development
of a general framework for metadata management. During
the last few years, three alternative notions of
inversion for schema mappings have been proposed
(Fagin-inverse (Fagin, TODS 32(4), 25:1---25:53, 2007),
quasi-inverse (Fagin et al., TODS 33(2), 11:1---11:52,
2008), and maximum recovery (Arenas et al., TODS 34(4),
22:1---22:48, 2009)). However, these notions lack some
fundamental properties that limit their practical
applicability: most of them are expressed in languages
including features that are difficult to use in
practice, some of these inverses are not guaranteed to
exist for mappings specified with source-to-target
tuple-generating dependencies (st-tgds), and it has
been futile to search for a meaningful mapping language
that is closed under any of these notions of inverse.
In this paper, we develop a framework for the inversion
of schema mappings that fulfills all of the above
requirements. It is based on the notion of $ {\mathcal
{C}}$-maximum recovery, for a query language $
{\mathcal {C}}$, a notion designed to generate inverse
mappings that recover back only the information that
can be retrieved with queries in $ {\mathcal {C}}$. By
focusing on the language of conjunctive queries (CQ),
we are able to find a mapping language that contains
the class of st-tgds, is closed under CQ-maximum
recovery, and for which the chase procedure can be used
to exchange data efficiently. Furthermore, we show that
our choices of inverse notion and mapping language are
optimal, in the sense that choosing a more expressive
inverse operator or mapping language causes the loss of
these properties.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bravo:2012:CRX,
author = "Loreto Bravo and James Cheney and Irini Fundulaki and
Ricardo Segovia",
title = "Consistency and repair for {XML} write-access control
policies",
journal = j-VLDB-J,
volume = "21",
number = "6",
pages = "843--867",
month = dec,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0273-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 5 08:04:46 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "XML access control policies involving updates may
contain security flaws, here called inconsistencies, in
which a forbidden operation may be simulated by
performing a sequence of allowed operations. This
article investigates the problem of deciding whether a
policy is consistent, and if not, how its
inconsistencies can be repaired. We consider total and
partial policies expressed in terms of annotated
schemas defining which operations are allowed or denied
for the XML trees that are instances of the schema. We
show that consistency is decidable in PTIME for such
policies and that consistent partial policies can be
extended to unique least-privilege consistent total
policies. We also consider repair problems based on
deleting privileges to restore consistency, show that
finding minimal repairs is NP-complete, and give
heuristics for finding repairs. Finally, we
experimentally evaluate these algorithms in comparison
with an exact approach based on answer-set
programming.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chang:2012:EDD,
author = "Lijun Chang and Jeffrey Xu Yu and Lu Qin and Hong
Cheng and Miao Qiao",
title = "The exact distance to destination in undirected
world",
journal = j-VLDB-J,
volume = "21",
number = "6",
pages = "869--888",
month = dec,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0274-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 5 08:04:46 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Shortest distance queries are essential not only in
graph analysis and graph mining tasks but also in
database applications, when a large graph needs to be
dealt with. Such shortest distance queries are
frequently issued by end-users or requested as a
subroutine in real applications. For intensive queries
on large graphs, it is impractical to compute shortest
distances on-line from scratch, and impractical to
materialize all-pairs shortest distances. In the
literature, 2-hop distance labeling is proposed to
index the all-pairs shortest distances. It assigns
distance labels to vertices in a large graph in a
pre-computing step off-line and then answers shortest
distance queries on-line by making use of such distance
labels, which avoids exhaustively traversing the large
graph when answering queries. However, the existing
algorithms to generate 2-hop distance labels are not
scalable to large graphs. Finding an optimal 2-hop
distance labeling is NP-hard, and heuristic algorithms
may generate large size distance labels while still
needing to pre-compute all-pairs shortest paths. In
this paper, we propose a multi-hop distance labeling
approach, which generates a subset of the 2-hop
distance labels as index off-line. We can compute the
multi-hop distance labels efficiently by avoiding
pre-computing all-pairs shortest paths. In addition,
our multi-hop distance labeling is small in size to be
stored. To answer a shortest distance query between two
vertices, we first generate the query-specific small
set of 2-hop distance labels for the two vertices based
on our multi-hop distance labels stored and compute the
shortest distance between the two vertices based on the
2-hop distance labels generated on-line. We conducted
extensive performance studies on large real graphs and
confirmed the efficiency of our multi-hop distance
labeling scheme.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Soh:2012:AEE,
author = "Kheng Hong Soh and Ba Quan Truong and Sourav S.
Bhowmick",
title = "{ANDES}: efficient evaluation of {NOT}-twig queries in
relational databases",
journal = j-VLDB-J,
volume = "21",
number = "6",
pages = "889--914",
month = dec,
year = "2012",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0275-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 5 08:04:46 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Despite a large body of work on XPath query processing
in relational environment, systematic study of queries
containing not-predicates have received little
attention in the literature. Particularly, several xml
supports of industrial-strength commercial rdbms fail
to efficiently evaluate such queries. In this paper, we
present an efficient and novel strategy to evaluate
NOT-twig queries in a tree-unaware relational
environment. NOT-twig queries are XPath queries with
ancestor --- descendant and parent --- child axis and
contain one or more not-predicates. We propose a novel
Dewey-based encoding scheme called Andes ( ANcestor
Dewey-based Encoding Scheme), which enables us to
efficiently filter out elements satisfying a
not-predicate by comparing their ancestor group
identifiers. In this approach, a set of elements under
the same common ancestor at a specific level in the xml
tree is assigned same ancestor group identifier. Based
on this scheme, we propose a novel sql translation
algorithm for NOT-twig query evaluation. Experiments
carried out confirm that our proposed approach built on
top of an off-the-shelf commercial rdbms significantly
outperforms state-of-the-art relational and native
approaches. We also explore the query plans selected by
a commercial relational optimizer to evaluate our
translated queries in different input cardinality. Such
exploration further validates the performance benefits
of Andes.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lehner:2013:SIB,
author = "Wolfgang Lehner and Sunita Sarawagi",
title = "Special issue on best papers of {VLDB 2011}",
journal = j-VLDB-J,
volume = "22",
number = "1",
pages = "1--2",
month = feb,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0301-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jan 24 06:07:36 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Tzoumas:2013:EAG,
author = "Kostas Tzoumas and Amol Deshpande and Christian S.
Jensen",
title = "Efficiently adapting graphical models for selectivity
estimation",
journal = j-VLDB-J,
volume = "22",
number = "1",
pages = "3--27",
month = feb,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0293-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jan 24 06:07:36 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Query optimizers rely on statistical models that
succinctly describe the underlying data. Models are
used to derive cardinality estimates for intermediate
relations, which in turn guide the optimizer to choose
the best query execution plan. The quality of the
resulting plan is highly dependent on the accuracy of
the statistical model that represents the data. It is
well known that small errors in the model estimates
propagate exponentially through joins, and may result
in the choice of a highly sub-optimal query execution
plan. Most commercial query optimizers make the
attribute value independence assumption: all attributes
are assumed to be statistically independent. This
reduces the statistical model of the data to a
collection of one-dimensional synopses (typically in
the form of histograms), and it permits the optimizer
to estimate the selectivity of a predicate conjunction
as the product of the selectivities of the constituent
predicates. However, this independence assumption is
more often than not wrong, and is considered to be the
most common cause of sub-optimal query execution plans
chosen by modern query optimizers. We take a step
towards a principled and practical approach to
performing cardinality estimation without making the
independence assumption. By carefully using concepts
from the field of graphical models, we are able to
factor the joint probability distribution over all the
attributes in the database into small, usually
two-dimensional distributions, without a significant
loss in estimation accuracy. We show how to efficiently
construct such a graphical model from the database
using only two-way join queries, and we show how to
perform selectivity estimation in a highly efficient
manner. We integrate our algorithms into the PostgreSQL
DBMS. Experimental results indicate that estimation
errors can be greatly reduced, leading to orders of
magnitude more efficient query execution plans in many
cases. Optimization time is kept in the range of tens
of milliseconds, making this a practical approach for
industrial-strength query optimizers.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Minhas:2013:RTH,
author = "Umar Farooq Minhas and Shriram Rajagopalan and Brendan
Cully and Ashraf Aboulnaga and Kenneth Salem and Andrew
Warfield",
title = "{RemusDB}: transparent high availability for database
systems",
journal = j-VLDB-J,
volume = "22",
number = "1",
pages = "29--45",
month = feb,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0294-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jan 24 06:07:36 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we present a technique for building a
high-availability (HA) database management system
(DBMS). The proposed technique can be applied to any
DBMS with little or no customization, and with
reasonable performance overhead. Our approach is based
on Remus, a commodity HA solution implemented in the
virtualization layer, that uses asynchronous virtual
machine state replication to provide transparent HA and
failover capabilities. We show that while Remus and
similar systems can protect a DBMS, database workloads
incur a performance overhead of up to 32\% as compared
to an unprotected DBMS. We identify the sources of this
overhead and develop optimizations that mitigate the
problems. We present an experimental evaluation using
two popular database systems and industry standard
benchmarks showing that for certain workloads, our
optimized approach provides fast failover ($ \leq 3 $ s
of downtime) with low performance overhead when
compared to an unprotected DBMS. Our approach provides
a practical means for existing, deployed database
systems to be made more reliable with a minimum of
risk, cost, and effort. Furthermore, this paper invites
new discussion about whether the complexity of HA is
best implemented within the DBMS, or as a service by
the infrastructure below it.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Furche:2013:OLS,
author = "Tim Furche and Georg Gottlob and Giovanni Grasso and
Christian Schallhart and Andrew Sellers",
title = "{OXPath}: a language for scalable data extraction,
automation, and crawling on the deep web",
journal = j-VLDB-J,
volume = "22",
number = "1",
pages = "47--72",
month = feb,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0286-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jan 24 06:07:36 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The evolution of the web has outpaced itself: A
growing wealth of information and increasingly
sophisticated interfaces necessitate automated
processing, yet existing automation and data extraction
technologies have been overwhelmed by this very growth.
To address this trend, we identify four key
requirements for web data extraction, automation, and
(focused) web crawling: (1) interact with sophisticated
web application interfaces, (2) precisely capture the
relevant data to be extracted, (3) scale with the
number of visited pages, and (4) readily embed into
existing web technologies. We introduce OXPath as an
extension of XPath for interacting with web
applications and extracting data thus revealed ---
matching all the above requirements. OXPath's
page-at-a-time evaluation guarantees memory use
independent of the number of visited pages, yet remains
polynomial in time. We experimentally validate the
theoretical complexity and demonstrate that OXPath's
resource consumption is dominated by page rendering in
the underlying browser. With an extensive study of
sublanguages and properties of OXPath, we pinpoint the
effect of specific features on evaluation performance.
Our experiments show that OXPath outperforms existing
commercial and academic data extraction tools by a wide
margin.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Curino:2013:ADS,
author = "Carlo Curino and Hyun Jin Moon and Alin Deutsch and
Carlo Zaniolo",
title = "Automating the database schema evolution process",
journal = j-VLDB-J,
volume = "22",
number = "1",
pages = "73--98",
month = feb,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0302-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jan 24 06:07:36 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Supporting database schema evolution represents a
long-standing challenge of practical and theoretical
importance for modern information systems. In this
paper, we describe techniques and systems for
automating the critical tasks of migrating the database
and rewriting the legacy applications. In addition to
labor saving, the benefits delivered by these advances
are many and include reliable prediction of outcome,
minimization of downtime, system-produced
documentation, and support for archiving, historical
queries, and provenance. The PRISM/PRISM++ system
delivers these benefits, by solving the difficult
problem of automating the migration of databases and
the rewriting of queries and updates. In this paper, we
present the PRISM/PRISM++ system and the novel
technology that made it possible. In particular, we
focus on the difficult and previously unsolved problem
of supporting legacy queries and updates under schema
and integrity constraints evolution. The PRISM/PRISM++
approach consists in providing the users with a set of
SQL-based Schema Modification Operators (SMOs), which
describe how the tables in the old schema are modified
into those in the new schema. In order to support
updates, SMOs are extended with integrity constraints
modification operators. By using recent results on
schema mapping, the paper (i) characterizes the impact
on integrity constraints of structural schema changes,
(ii) devises representations that enable the rewriting
of updates, and (iii) develop a unified approach for
query and update rewriting under constraints. We
complement the system with two novel tools: the first
automatically collects and provides statistics on
schema evolution histories, whereas the second derives
equivalent sequences of SMOs from the migration scripts
that were used for schema upgrades. These tools were
used to produce an extensive testbed containing 15
evolution histories of scientific databases and web
information systems, providing over 100 years of
aggregate evolution histories and almost 2,000 schema
evolution steps.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ramesh:2013:KSF,
author = "Aditya Ramesh and S. Sudarshan and Purva Joshi and
Manisha Naik Gaonkar",
title = "Keyword search on form results",
journal = j-VLDB-J,
volume = "22",
number = "1",
pages = "99--123",
month = feb,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0287-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jan 24 06:07:36 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In recent years there has been a good deal of research
in the area of keyword search on structured and
semistructured data. Most of this body of work has a
significant limitation in the context of enterprise
data, since it ignores the application code that has
often been carefully designed to present data in a
meaningful fashion to users. In this work, we consider
how to perform keyword search on enterprise
applications, which provide a number of forms that can
take parameters; parameters may be explicit, or
implicit such as the identifier of the user. In the
context of such applications, the goal of keyword
search is, given a set of keywords, to retrieve forms
along with corresponding parameter values, such that
result of each retrieved form executed on the
corresponding retrieved parameter values will contain
the specified keywords. Some earlier work in this area
was based on creating keyword indices on form results,
but there are problems in maintaining such indices in
the face of updates. In contrast, we propose techniques
based on creating inverted SQL queries from the SQL
queries in the forms. Unlike earlier work, our
techniques do not require any special purpose indices
and instead make use of standard text indices supported
by most database systems. We have implemented our
techniques and show that keyword search can run at
reasonable speeds even on large databases with a
significant number of forms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Dieng:2013:MFC,
author = "Cheikh Tidiane Dieng and Tao-Yuan Jen and Dominique
Laurent and Nicolas Spyratos",
title = "Mining frequent conjunctive queries using functional
and inclusion dependencies",
journal = j-VLDB-J,
volume = "22",
number = "2",
pages = "125--150",
month = apr,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0277-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 29 15:54:45 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We address the issue of mining frequent conjunctive
queries in a relational database, a problem known to be
intractable even for conjunctive queries over a single
table. In this article, we show that mining frequent
projection-selection-join queries becomes tractable if
joins are performed along keys and foreign keys, in a
database satisfying functional and inclusion
dependencies, under certain restrictions. We note that
these restrictions cover most practical cases,
including databases operating over star schemas,
snow-flake schemas and constellation schemas. In our
approach, we define an equivalence relation over
queries using a pre-ordering with respect to which the
support is shown to be anti-monotonic. We propose a
level-wise algorithm for computing all frequent queries
by exploiting the fact that equivalent queries have the
same support. We report on experiments showing that, in
our context, mining frequent projection-selection-join
queries is indeed tractable, even for large data
sets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Tozun:2013:SDB,
author = "Pinar T{\"o}z{\"u}n and Ippokratis Pandis and Ryan
Johnson and Anastasia Ailamaki",
title = "Scalable and dynamically balanced shared-everything
{OLTP} with physiological partitioning",
journal = j-VLDB-J,
volume = "22",
number = "2",
pages = "151--175",
month = apr,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0278-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 29 15:54:45 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Scaling the performance of shared-everything
transaction processing systems to highly parallel
multicore hardware remains a challenge for database
system designers. Recent proposals alleviate locking
and logging bottlenecks in the system, leaving page
latching as the next potential problem. To tackle the
page latching problem, we propose physiological
partitioning (PLP). PLP applies logical-only
partitioning, maintaining the desired properties of
sharedeverything designs, and introduces a multi-rooted
B+Tree index structure (MRBTree) that enables the
partitioning of the accesses at the physical page
level. Logical partitioning and MRBTrees together
ensure that all accesses to a given index page come
from a single thread and, hence, can be entirely latch
free; an extended design makes heap page accesses
thread private as well. Moreover, MRBTrees offer an
infrastructure for easy repartitioning and allow us to
have a lightweight dynamic load balancing mechanism
(DLB) on top of PLP. Profiling a PLP prototype running
on different multicore machines shows that it acquires
85 and 68\%fewer contentious critical sections,
respectively, than an optimized conventional design and
one based on logical-only partitioning. PLP also
improves performance up to almost 50 \% over the
existing systems, while DLB enhances the system with
rapid and robust behavior in both detecting and
handling load imbalances.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wu:2013:SXS,
author = "Xiaoying Wu and Dimitri Theodoratos",
title = "A survey on {XML} streaming evaluation techniques",
journal = j-VLDB-J,
volume = "22",
number = "2",
pages = "177--202",
month = apr,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0281-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 29 15:54:45 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "XML is currently the most popular format for
exchanging and representing data on the web. It is used
in various applications and for different types of data
including structured, semistructured, and unstructured
heterogeneous data types. During the period, XML was
establishing itself, data streaming applications have
gained increased attention and importance. Because of
these developments, the querying and efficient
processing of XML streams has became a central issue.
In this study, we survey the state of the art in XML
streaming evaluation techniques. We focus on both the
streaming evaluation of XPath expressions and of XQuery
queries. We classify the XPath streaming evaluation
approaches according to the main data structure used
for the evaluation into three categories:
automaton-based approach, array-based approach, and
stack-based approach. We review, analyze, and compare
the major techniques proposed for each approach. We
also review multiple query streaming evaluation
techniques. For the XQuery streaming evaluation
problem, we identify and discuss four processing
paradigms adopted by the existing XQuery stream query
engines: the transducer-based paradigm, the
algebra-based paradigm, the automata-algebra paradigm,
and the pull-based paradigm. In addition, we review
optimization techniques for XQuery streaming
evaluation. We address the problem of optimizing XQuery
streaming evaluation as a buffer optimization problem.
For all techniques discussed, we describe the research
issues and the proposed algorithms and we compare them
with other relevant suggested techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lu:2013:ADU,
author = "Wentian Lu and Gerome Miklau and Neil Immerman",
title = "Auditing a database under retention policies",
journal = j-VLDB-J,
volume = "22",
number = "2",
pages = "203--228",
month = apr,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0282-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 29 15:54:45 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Auditing the changes to a database is critical for
identifying malicious behavior, maintaining data
quality, and improving system performance. But an
accurate audit log is an historical record of the past
that can also pose a serious threat to privacy.
Policies that limit data retention conflict with the
goal of accurate auditing, and data owners have to
carefully balance the need for policy compliance with
the goal of accurate auditing. In this paper, we
provide a framework for auditing the changes to a
database system while respecting data retention
policies. Our framework includes an historical data
model that supports flexible audit queries, along with
a language for retention policies that can hide
individual attribute values or remove entire tuples
from the history. Under retention policies, the audit
history is partially incomplete. Thus, audit queries on
the protected history can include imprecise results. We
propose two different models (a tuple-independent model
and a tuple-correlated model) for formalizing the
meaning of audit queries. We implement policy
application and query answering efficiently in a
standard relational system and characterize the cases
where accurate auditing can be achieved under retention
restrictions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yuan:2013:LLB,
author = "Dayu Yuan and Prasenjit Mitra",
title = "{Lindex}: a lattice-based index for graph databases",
journal = j-VLDB-J,
volume = "22",
number = "2",
pages = "229--252",
month = apr,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0284-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 29 15:54:45 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Subgraph querying has wide applications in various
fields such as cheminformatics and bioinformatics.
Given a query graph, q, a subgraph-querying algorithm
retrieves all graphs, D ( q ), which have q as a
subgraph, from a graph database, D. Subgraph querying
is costly because it uses subgraph isomorphism tests,
which are NP-complete. Graph indices are commonly used
to improve the performance of subgraph querying in
graph databases. Subgraph-querying algorithms first
construct a candidate answer set by filtering out a set
of false answers and then verify each candidate graph
using subgraph isomorphism tests. To build graph
indices, various kinds of substructure (subgraph,
subtree, or path) features have been proposed with the
goal of maximizing the filtering rate. Each of them
works with a specifically designed index structure, for
example, discriminative and frequent subgraph features
work with gIndex, `? -TCFG features work with FG-index,
etc. We propose Lindex, a graph index, which indexes
subgraphs contained in database graphs. Nodes in Lindex
represent key-value pairs where the key is a subgraph
in a database and the value is a list of database
graphs containing the key. We propose two heuristics
that are used in the construction of Lindex that allows
us to determine answers to subgraph queries conducting
less subgraph isomorphism tests. Consequently, Lindex
improves subgraph-querying efficiency. In addition,
Lindex is compatible with any choice of features.
Empirically, we demonstrate that Lindex used in
conjunction with subgraph indexing features proposed in
previous works outperforms other specifically designed
index structures. As a novel index structure, Lindex
(1) is effective in filtering false graphs (2) provides
fast index lookups, (3) is fast with respect to index
construction and maintenance, and (4) can be
constructed using any set of substructure index
features. These four properties result in a fast and
scalable subgraph-querying infrastructure. We
substantiate the benefits of Lindex and its
disk-resident variation Lindex+ theoretically and
empirically.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Song:2013:CDH,
author = "Shaoxu Song and Lei Chen and Philip S. Yu",
title = "Comparable dependencies over heterogeneous data",
journal = j-VLDB-J,
volume = "22",
number = "2",
pages = "253--274",
month = apr,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0285-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 29 15:54:45 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "To study the data dependencies over heterogeneous data
in dataspaces, we define a general dependency form,
namely comparable dependencies (CDS), which specifies
constraints on comparable attributes. It covers the
semantics of a broad class of dependencies in
databases, including functional dependencies (FDS),
metric functional dependencies (MFDS), and matching
dependencies (MDS). As we illustrated, comparable
dependencies are useful in real practice of dataspaces,
such as semantic query optimization. Due to
heterogeneous data in dataspaces, the first question,
known as the validation problem, is to tell whether a
dependency (almost) holds in a data instance.
Unfortunately, as we proved, the validation problem
with certain error or confidence guarantee is generally
hard. In fact, the confidence validation problem is
also NP-hard to approximate to within any constant
factor. Nevertheless, we develop several approaches for
efficient approximation computation, such as greedy and
randomized approaches with an approximation bound on
the maximum number of violations that an object may
introduce. Finally, through an extensive experimental
evaluation on real data, we verify the superiority of
our methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Qiao:2013:CWC,
author = "Miao Qiao and Hong Cheng and Lu Qin and Jeffrey Xu Yu
and Philip S. Yu and Lijun Chang",
title = "Computing weight constraint reachability in large
networks",
journal = j-VLDB-J,
volume = "22",
number = "3",
pages = "275--294",
month = jun,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0288-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 17 17:37:10 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Reachability is a fundamental problem on large-scale
networks emerging nowadays in various application
domains, such as social networks, communication
networks, biological networks, road networks, etc. It
has been studied extensively. However, little existing
work has studied reachability with realistic
constraints imposed on graphs with real-valued edge or
node weights. In fact, such weights are very common in
many real-world networks, for example, the bandwidth of
a link in communication networks, the reliability of an
interaction between two proteins in PPI networks, and
the handling capacity of a warehouse/storage point in a
distribution network. In this paper, we formalize a new
yet important reachability query in weighted undirected
graphs, called weight constraint reachability (WCR)
query that asks: is there a path between nodes a and b
, on which each real-valued edge (or node) weight
satisfies a range constraint. We discover an
interesting property of WCR, based on which, we design
a novel edge-based index structure to answer the WCR
query in O(1) time. Furthermore, we consider the case
when the index cannot entirely fit in the memory, which
can be very common for emerging massive networks. An
I/O-efficient index is proposed, which provides
constant I/O (precisely four I/Os) query time with
O(|V|\log |V|) disk-based index size. Extensive
experimental studies on both real and synthetic
datasets demonstrate the efficiency and scalability of
our solutions in answering the WCR query.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Toyoda:2013:PDD,
author = "Machiko Toyoda and Yasushi Sakurai and Yoshiharu
Ishikawa",
title = "Pattern discovery in data streams under the time
warping distance",
journal = j-VLDB-J,
volume = "22",
number = "3",
pages = "295--318",
month = jun,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0289-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 17 17:37:10 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Subsequence matching is a basic problem in the field
of data stream mining. In recent years, there has been
significant research effort spent on efficiently
finding subsequences similar to a query sequence.
Another challenging issue in relation to subsequence
matching is how we identify common local patterns when
both sequences are evolving. This problem arises in
trend detection, clustering, and outlier detection.
Dynamic time warping (DTW) is often used for
subsequence matching and is a powerful similarity
measure. However, the straightforward method using DTW
incurs a high computation cost for this problem. In
this paper, we propose a one-pass algorithm,
CrossMatch, that achieves the above goal. CrossMatch
addresses two important challenges: (1) how can we
identify common local patterns efficiently without any
omission? (2) how can we find common local patterns in
data stream processing? To tackle these challenges,
CrossMatch incorporates three ideas: (1) a scoring
function, which computes the DTW distance indirectly to
reduce the computation cost, (2) a position matrix,
which stores starting positions to keep track of common
local patterns in a streaming fashion, and (3) a
streaming algorithm, which identifies common local
patterns efficiently and outputs them on the fly. We
provide a theoretical analysis and prove that our
algorithm does not sacrifice accuracy. Our experimental
evaluation and case studies show that CrossMatch can
incrementally discover common local patterns in data
streams within constant time (per update) and space.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xie:2013:UDV,
author = "Xike Xie and Reynold Cheng and Man Lung Yiu and Liwen
Sun and Jinchuan Chen",
title = "{UV-diagram}: a {Voronoi} diagram for uncertain
spatial databases",
journal = j-VLDB-J,
volume = "22",
number = "3",
pages = "319--344",
month = jun,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0290-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 17 17:37:10 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The Voronoi diagram is an important technique for
answering nearest-neighbor queries for spatial
databases. We study how the Voronoi diagram can be used
for uncertain spatial data, which are inherent in
scientific and business applications. Specifically, we
propose the Uncertain-Voronoi diagram (or UV-diagram ),
which divides the data space into disjoint
``UV-partitions''. Each UV-partition P is associated
with a set S of objects, such that any point q located
in P has the set S as its nearest neighbor with nonzero
probabilities. The UV-diagram enables queries that
return objects with nonzero chances of being the
nearest neighbor (NN) of a given point q . It supports
``continuous nearest-neighbor search'', which refreshes
the set of NN objects of q , as the position of q
changes. It also allows the analysis of
nearest-neighbor information, for example, to find out
the number of objects that are the nearest neighbors of
any point in a given area. A UV-diagram requires
exponential construction and storage costs. To tackle
these problems, we devise an alternative representation
of a UV-diagram, by using a set of UV-cells. A UV-cell
of an object o is the extent e for which o can be the
nearest neighbor of any point q \in e . We study how to
speed up the derivation of UV-cells by considering its
nearby objects. We also use the UV-cells to design the
UV-index, which supports different queries, and can be
constructed in polynomial time. We have performed
extensive experiments on both real and synthetic data
to validate the efficiency of our approaches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhu:2013:HEQ,
author = "Yuanyuan Zhu and Lu Qin and Jeffrey Xu Yu and Yiping
Ke and Xuemin Lin",
title = "High efficiency and quality: large graphs matching",
journal = j-VLDB-J,
volume = "22",
number = "3",
pages = "345--368",
month = jun,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0292-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 17 17:37:10 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Graph matching plays an essential role in many real
applications. In this paper, we study how to match two
large graphs by maximizing the number of matched edges,
which is known as maximum common subgraph matching and
is NP-hard. To find exact matching, it cannot a graph
with more than 30 nodes. To find an approximate
matching, the quality can be very poor. We propose a
novel two-step approach that can efficiently match two
large graphs over thousands of nodes with high matching
quality. In the first step, we propose an
anchor-selection/expansion approach to compute a good
initial matching. In the second step, we propose a new
approach to refine the initial matching. We give the
optimality of our refinement and discuss how to
randomly refine the matching with different
combinations. We further show how to extend our
solution to handle labeled graphs. We conducted
extensive testing using real and synthetic datasets and
report our findings in this paper.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Baca:2013:OEG,
author = "Radim Baca and Michal Kr{\'a}tk{\'y} and Tok Wang Ling
and Jiaheng Lu",
title = "Optimal and efficient generalized twig pattern
processing: a combination of preorder and postorder
filterings",
journal = j-VLDB-J,
volume = "22",
number = "3",
pages = "369--393",
month = jun,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0295-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 17 17:37:10 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Searching for occurrences of a twig pattern query
(TPQ) in an XML document is a core task of all XML
database query languages. The generalized twig pattern
(GTP) extends the TPQ model to include semantics
related to output nodes, optional nodes, and boolean
expressions which are part of the XQuery language.
Preorder filtering holistic algorithms such as
TwigStack represent a significant class of TPQ
processing approaches with a linear worst-case I/O
complexity with respect to the sum of the input and
output sizes for some query classes. Another important
class of holistic approaches is represented by
postorder filtering holistic algorithms such as \text{
Twig}^2 Stack which introduced a linear output
enumeration time with respect to the result size. In
this article, we introduce a holistic algorithm called
GTPStack which is the first approach capable of
processing a GTP with a linear worst-case I/O
complexity with respect to the GTP result size. This is
achieved by using a combination of the preorder and
postorder filterings before storing nodes in an
intermediate storage. Additionally, another
contribution of this article is an introduction of a
new perspective of holistic algorithm optimality. We
show that the optimality depends not only on a query
class but also on XML document characteristics. This
new view on the optimality extends the general
knowledge about the type of queries for which the
holistic algorithms are optimal. Moreover, it allows us
to determine that GTPStack is optimal for any GTP when
a specific XML document is considered. We present a
comprehensive experimental study of the
state-of-the-art holistic algorithms showing under
which conditions GTPStack outperforms the other
holistic approaches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Silva:2013:SQT,
author = "Yasin N. Silva and Walid G. Aref and Per-Ake Larson
and Spencer S. Pearson and Mohamed H. Ali",
title = "Similarity queries: their conceptual evaluation,
transformations, and processing",
journal = j-VLDB-J,
volume = "22",
number = "3",
pages = "395--420",
month = jun,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0296-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 17 17:37:10 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Many application scenarios can significantly benefit
from the identification and processing of similarities
in the data. Even though some work has been done to
extend the semantics of some operators, for example
join and selection, to be aware of data similarities,
there has not been much study on the role and
implementation of similarity-aware operations as
first-class database operators. Furthermore, very
little work has addressed the problem of evaluating and
optimizing queries that combine several similarity
operations. The focus of this paper is the study of
similarity queries that contain one or multiple
first-class similarity database operators such as
Similarity Selection, Similarity Join, and Similarity
Group-by. Particularly, we analyze the implementation
techniques of several similarity operators, introduce a
consistent and comprehensive conceptual evaluation
model for similarity queries, and present a rich set of
transformation rules to extend cost-based query
optimization to the case of similarity queries.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Dindar:2013:MES,
author = "Nihal Dindar and Nesime Tatbul and Ren{\'e}e J. Miller
and Laura M. Haas and Irina Botan",
title = "Modeling the execution semantics of stream processing
engines with {SECRET}",
journal = j-VLDB-J,
volume = "22",
number = "4",
pages = "421--446",
month = aug,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0297-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 17 17:37:16 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "There are many academic and commercial stream
processing engines (SPEs) today, each of them with its
own execution semantics. This variation may lead to
seemingly inexplicable differences in query results. In
this paper, we present SECRET, a model of the behavior
of SPEs. SECRET is a descriptive model that allows
users to analyze the behavior of systems and understand
the results of window-based queries (with time- and
tuple-based windows) for a broad range of heterogeneous
SPEs. The model is the result of extensive analysis and
experimentation with several commercial and academic
engines. In the paper, we describe the types of
heterogeneity found in existing engines and show with
experiments on real systems that our model can explain
the key differences in windowing behavior.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Elghandour:2013:RXP,
author = "Iman Elghandour and Ashraf Aboulnaga and Daniel C.
Zilio and Calisto Zuzarte",
title = "Recommending {XML} physical designs for {XML}
databases",
journal = j-VLDB-J,
volume = "22",
number = "4",
pages = "447--470",
month = aug,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0298-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 17 17:37:16 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Database systems employ physical structures such as
indexes and materialized views to improve query
performance, potentially by orders of magnitude. It is
therefore important for a database administrator to
choose the appropriate configuration of these physical
structures for a given database. XML database systems
are increasingly being used to manage semi-structured
data, and XML support has been added to commercial
database systems. In this paper, we address the problem
of automatic physical design for XML databases, which
is the process of automatically selecting the best set
of physical structures for a database and a query
workload. We focus on recommending two types of
physical structures: XML indexes and relational
materialized views of XML data. We present a design
advisor for recommending XML indexes, one for
recommending materialized views, and an integrated
design advisor that recommends both indexes and
materialized views. A key characteristic of our
advisors is that they are tightly coupled with the
query optimizer of the database system, and they rely
on the optimizer for enumerating and evaluating
physical designs. We have implemented our advisors in a
prototype version of IBM DB2 V9, and we experimentally
demonstrate the effectiveness of their recommendations
using this implementation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mazuran:2013:EPD,
author = "Mirjana Mazuran and Edoardo Serra and Carlo Zaniolo",
title = "Extending the power of datalog recursion",
journal = j-VLDB-J,
volume = "22",
number = "4",
pages = "471--493",
month = aug,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0299-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 17 17:37:16 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Supporting aggregates in recursive logic rules
represents a very important problem for Datalog. To
solve this problem, we propose a simple extension,
called DatalogFS (Datalog extended with frequency
support goals), that supports queries and reasoning
about the number of distinct variable assignments
satisfying given goals, or conjunctions of goals, in
rules. This monotonic extension greatly enhances the
power of Datalog, while preserving (i) its declarative
semantics and (ii) its amenability to efficient
implementation via differential fixpoint and other
optimization techniques presented in the paper. Thus,
DatalogFS enables the efficient formulation of queries
that could not be expressed efficiently or could not be
expressed at all in Datalog with stratified negation
and aggregates. In fact, using a generalized notion of
multiplicity called frequency, we show that diffusion
models and page rank computations can be easily
expressed and efficiently implemented using DatalogFS
.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Galpin:2013:QAO,
author = "Ixent Galpin and Alvaro A. Fernandes and Norman W.
Paton",
title = "{QoS}-aware optimization of sensor network queries",
journal = j-VLDB-J,
volume = "22",
number = "4",
pages = "495--517",
month = aug,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0300-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 17 17:37:16 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The resource-constrained nature of mote-level wireless
sensor networks (WSNs) poses challenges for the design
of a general-purpose sensor network query processors
(SNQPs). Existing SNQPs tend to generate query
execution plans (QEPs) that are selected on the basis
of a fixed, implicit expectation, for example, that
energy consumption should be kept as small as possible.
However, in WSN applications, the same query may be
subject to several, possibly conflicting,
quality-of-service (QoS) expectations concomitantly
(for example maximizing data acquisition rates subject
to keeping energy consumption low). It is also not
uncommon for the QoS expectations to change over the
lifetime of a deployment (for example from low to high
data acquisition rates). This paper describes
optimization algorithms that respond to stated QoS
expectations (about acquisition rate, delivery time,
energy consumption and lifetime) when making routing,
placement, and timing decisions for in-WSN query
processing. The paper shows experimentally that
QoS-awareness offers significant benefits in responding
to, and reconciling, diverse QoS expectations, thereby
enabling QoS-aware SNQPs to generate efficient QEPs for
a broader range WSN applications than has hitherto been
possible.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Deutch:2013:TQW,
author = "Daniel Deutch and Tova Milo and Neoklis Polyzotis",
title = "Top-$k$ queries over {Web} applications",
journal = j-VLDB-J,
volume = "22",
number = "4",
pages = "519--542",
month = aug,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0303-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 17 17:37:16 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The core logic of web applications that suggest some
particular service, such as online shopping, e-commerce
etc., is typically captured by Business Processes
(BPs). Among all the (maybe infinitely many) possible
execution flows of a BP, analysts are often interested
in identifying flows that are ``most important'',
according to some weight metric. The goal of the
present paper is to provide efficient algorithms for
top-$k$ query evaluation over the possible executions
of Business Processes, under some given weight
function. Unique difficulties in top-$k$ analysis in
this settings stem from (1) the fact that the number of
possible execution flows of a given BP is typically
very large, or even infinite in presence of recursion
and (2) that the weights (e.g., likelihood, monetary
cost, etc.) induced by actions performed during the
execution (e.g., product purchase) may be
inter-dependent (due to probabilistic dependencies,
combined discount deals etc.). We exemplify these
difficulties, and overcome them to provide efficient
algorithms for query evaluation where possible. We also
describe in details an application prototype that we
have developed for recommending optimal navigation in
an online shopping web site that is based on our model
and algorithms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gao:2013:OSD,
author = "Jun Gao and Jeffrey Xu Yu and Ruoming Jin and Jiashuai
Zhou and Tengjiao Wang and Dongqing Yang",
title = "Outsourcing shortest distance computing with privacy
protection",
journal = j-VLDB-J,
volume = "22",
number = "4",
pages = "543--559",
month = aug,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0304-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 17 17:37:16 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "With the advent of cloud computing, it becomes
desirable to outsource graphs into cloud servers to
efficiently perform complex operations without
compromising their sensitive information. In this
paper, we take the shortest distance computation as a
case to investigate the technique issues in outsourcing
graph operations. We first propose a parameter-free,
edge-based 2-HOP delegation security model (shorten as
2-HOP delegation model), which can greatly reduce the
chances of the structural pattern attack and the graph
reconstruction attack. We then transform the original
graph into a link graph $ G_l $ kept locally and a set
of outsourced graphs $ \mathcal G_o $. Our objectives
include (i) ensuring each outsourced graph meeting the
requirement of 2-HOP delegation model, (ii) making
shortest distance queries be answered using $ G_l $ and
$ \mathcal G_o $, (iii) minimizing the space cost of $
G_l $. We devise a greedy method to produce $ G_l $ and
$ \mathcal G_o $, which can exactly answer shortest
distance queries. We also develop an efficient
transformation method to support approximate shortest
distance answering under a given average additive error
bound. The experimental results illustrate the
effectiveness and efficiency of our method.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kalashnikov:2013:SEF,
author = "Dmitri V. Kalashnikov",
title = "{Super-EGO}: fast multi-dimensional similarity join",
journal = j-VLDB-J,
volume = "22",
number = "4",
pages = "561--585",
month = aug,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-012-0305-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 17 17:37:16 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Efficient processing of high-dimensional similarity
joins plays an important role for a wide variety of
data-driven applications. In this paper, we consider
\varepsilon -join variant of the problem. Given two d
-dimensional datasets and parameter \varepsilon , the
task is to find all pairs of points, one from each
dataset that are within \varepsilon distance from each
other. We propose a new \varepsilon -join algorithm,
called Super-EGO, which belongs the EGO family of join
algorithms. The new algorithm gains its advantage by
using novel data-driven dimensionality re-ordering
technique, developing a new EGO-strategy that more
aggressively avoids unnecessary computation, as well as
by developing a parallel version of the algorithm. We
study the newly proposed Super-EGO algorithm on large
real and synthetic datasets. The empirical study
demonstrates significant advantage of the proposed
solution over the existing state of the art
techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Brambilla:2013:SIS,
author = "Marco Brambilla and Stefano Ceri and Alon Halevy",
title = "Special issue on structured and crowd-sourced data on
the {Web}",
journal = j-VLDB-J,
volume = "22",
number = "5",
pages = "587--588",
month = oct,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0327-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Dec 16 16:57:30 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Goasdoue:2013:GTT,
author = "Fran{\c{c}}ois Goasdou{\'e} and Konstantinos Karanasos
and Yannis Katsis and Julien Leblay and Ioana Manolescu
and Stamatis Zampetakis",
title = "Growing triples on trees: an {XML--RDF} hybrid model
for annotated documents",
journal = j-VLDB-J,
volume = "22",
number = "5",
pages = "589--613",
month = oct,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0321-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Dec 16 16:57:30 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Since the beginning of the Semantic Web initiative,
significant efforts have been invested in finding
efficient ways to publish, store, and query metadata on
the Web. RDF and SPARQL have become the standard data
model and query language, respectively, to describe
resources on the Web. Large amounts of RDF data are now
available either as stand-alone datasets or as metadata
over semi-structured (typically XML) documents. The
ability to apply RDF annotations over XML data
emphasizes the need to represent and query data and
metadata simultaneously. We propose XR, a novel hybrid
data model capturing the structural aspects of XML data
and the semantics of RDF, also enabling us to reason
about XML data. Our model is general enough to describe
pure XML or RDF datasets, as well as RDF-annotated XML
data, where any XML node can act as a resource. This
data model comes with the XRQ query language that
combines features of both XQuery and SPARQL. To
demonstrate the feasibility of this hybrid XML-RDF data
management setting, and to validate its interest, we
have developed an XR platform on top of well-known data
management systems for XML and RDF. In particular, the
platform features several XRQ query processing
algorithms, whose performance is experimentally
compared.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Furche:2013:OKA,
author = "Tim Furche and Georg Gottlob and Giovanni Grasso and
Xiaonan Guo and Giorgio Orsi and Christian Schallhart",
title = "The ontological key: automatically understanding and
integrating forms to access the deep {Web}",
journal = j-VLDB-J,
volume = "22",
number = "5",
pages = "615--640",
month = oct,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0323-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Dec 16 16:57:30 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Forms are our gates to the Web. They enable us to
access the deep content of Web sites. Automatic form
understanding provides applications, ranging from
crawlers over meta-search engines to service
integrators, with a key to this content. Yet, it has
received little attention other than as component in
specific applications such as crawlers or meta-search
engines. No comprehensive approach to form
understanding exists, let alone one that produces rich
models for semantic services or integration with linked
open data. In this paper, we present opal, the first
comprehensive approach to form understanding and
integration. We identify form labeling and form
interpretation as the two main tasks involved in form
understanding. On both problems, opal advances the
state of the art: For form labeling, it combines
features from the text, structure, and visual rendering
of a Web page. In extensive experiments on the ICQ and
TEL-8 benchmarks and a set of 200 modern Web forms,
opal outperforms previous approaches for form labeling
by a significant margin. For form interpretation, opal
uses a schema (or ontology) of forms in a given domain.
Thanks to this domain schema, it is able to produce
nearly perfect ($ \gg 97 $ \% accuracy in the
evaluation domains) form interpretations. Yet, the
effort to produce a domain schema is very low, as we
provide a datalog-based template language that eases
the specification of such schemata and a methodology
for deriving a domain schema largely automatically from
an existing domain ontology. We demonstrate the value
of opal's form interpretations through a light-weight
form integration system that successfully translates
and distributes master queries to hundreds of forms
with no error, yet is implemented with only a handful
translation rules.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bozzon:2013:ESF,
author = "Alessandro Bozzon and Marco Brambilla and Stefano Ceri
and Davide Mazza",
title = "Exploratory search framework for {Web} data sources",
journal = j-VLDB-J,
volume = "22",
number = "5",
pages = "641--663",
month = oct,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0326-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Dec 16 16:57:30 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Exploratory search is an information seeking behavior
where users progressively learn about one or more
topics of interest; it departs quite radically from
traditional keyword-based query paradigms, as it
combines querying and browsing of resources, and covers
activities such as investigating, evaluating,
comparing, and synthesizing retrieved information. In
most cases, such activities are enabled by a conceptual
description of information in terms of entities and
their semantic relationships. Customized Web
applications, where few applicative entities and their
relationships are embedded within the application
logics, typically provide some support to exploratory
search, which is, however, specific for a given domain.
In this paper, we describe a general-purpose
exploratory search framework, i.e., a framework which
is neutral to the application logic. Our contribution
consists of the formalization of the exploratory search
paradigm over Web data sources, accessed by means of
services; extracted information is described by means
of an entity-relationship schema, which masks the
service implementations. Exploratory interaction is
supported by a general-purpose user interface including
a set of widgets for data exploration, from big tables
to atomic tables, visual diagrams, and geographic maps;
the user interaction is translated to queries defined
in \mathcal S \hbox {e}\mathcal C \hbox {oQL} S e C oQL
, a SQL-like language and protocol specifically
designed for supporting exploratory search over data
sources. We illustrate the software architecture of our
prototype, which uses the interplay of a query and
result management system with an orchestrator, capable
of incrementally building queries and of walking
through the past navigation history. The distinctive
feature of the framework is the ability to extract top
solutions, which combine top-ranked entity instances.
We evaluate exploratory search from the end-user
perspective in the context of a cognitive model for
search, by studying the user's behavior and the
effectiveness of exploratory search in terms of quality
of results produced by the search process; we also
compare the effectiveness of interaction in using our
multi-domain search system with the use of various
replicas of the system, each acting upon a single
domain, and with the use of conventional search
engines.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Demartini:2013:LSL,
author = "Gianluca Demartini and Djellel Eddine Difallah and
Philippe Cudr{\'e}-Mauroux",
title = "Large-scale linked data integration using
probabilistic reasoning and crowdsourcing",
journal = j-VLDB-J,
volume = "22",
number = "5",
pages = "665--687",
month = oct,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0324-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Dec 16 16:57:30 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We tackle the problems of semiautomatically matching
linked data sets and of linking large collections of
Web pages to linked data. Our system, ZenCrowd, (1)
uses a three-stage blocking technique in order to
obtain the best possible instance matches while
minimizing both computational complexity and latency,
and (2) identifies entities from natural language text
using state-of-the-art techniques and automatically
connects them to the linked open data cloud. First, we
use structured inverted indices to quickly find
potential candidate results from entities that have
been indexed in our system. Our system then analyzes
the candidate matches and refines them whenever deemed
necessary using computationally more expensive queries
on a graph database. Finally, we resort to human
computation by dynamically generating crowdsourcing
tasks in case the algorithmic components fail to come
up with convincing results. We integrate all results
from the inverted indices, from the graph database and
from the crowd using a probabilistic framework in order
to make sensible decisions about candidate matches and
to identify unreliable human workers. In the following,
we give an overview of the architecture of our system
and describe in detail our novel three-stage blocking
technique and our probabilistic decision framework. We
also report on a series of experimental results on a
standard data set, showing that our system can achieve
a 95 \% average accuracy on instance matching (as
compared to the initial 88 \% average accuracy of the
purely automatic baseline) while drastically limiting
the amount of work performed by the crowd. The
experimental evaluation of our system on the entity
linking task shows an average relative improvement of
14 \% over our best automatic approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sagi:2013:SMP,
author = "Tomer Sagi and Avigdor Gal",
title = "Schema matching prediction with applications to data
source discovery and dynamic ensembling",
journal = j-VLDB-J,
volume = "22",
number = "5",
pages = "689--710",
month = oct,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0325-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Dec 16 16:57:30 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Web-scale data integration involves fully automated
efforts which lack knowledge of the exact match between
data descriptions. In this paper, we introduce schema
matching prediction, an assessment mechanism to support
schema matchers in the absence of an exact match. Given
attribute pair-wise similarity measures, a predictor
predicts the success of a matcher in identifying
correct correspondences. We present a comprehensive
framework in which predictors can be defined, designed,
and evaluated. We formally define schema matching
evaluation and schema matching prediction using
similarity spaces and discuss a set of four desirable
properties of predictors, namely correlation,
robustness, tunability, and generalization. We present
a method for constructing predictors, supporting
generalization, and introduce prediction models as
means of tuning prediction toward various quality
measures. We define the empirical properties of
correlation and robustness and provide concrete
measures for their evaluation. We illustrate the
usefulness of schema matching prediction by presenting
three use cases: We propose a method for ranking the
relevance of deep Web sources with respect to given
user needs. We show how predictors can assist in the
design of schema matching systems. Finally, we show how
prediction can support dynamic weight setting of
matchers in an ensemble, thus improving upon current
state-of-the-art weight setting methods. An extensive
empirical evaluation shows the usefulness of predictors
in these use cases and demonstrates the usefulness of
prediction models in increasing the performance of
schema matching.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lee:2013:HEC,
author = "Jongwuk Lee and Hyunsouk Cho and Jin-Woo Park and
Young-Rok Cha and Seung-Won Hwang and Zaiqing Nie and
Ji-Rong Wen",
title = "Hybrid entity clustering using crowds and data",
journal = j-VLDB-J,
volume = "22",
number = "5",
pages = "711--726",
month = oct,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0328-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Dec 16 16:57:30 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Query result clustering has attracted considerable
attention as a means of providing users with a concise
overview of results. However, little research effort
has been devoted to organizing the query results for
entities which refer to real-world concepts, e.g.,
people, products, and locations. Entity-level result
clustering is more challenging because diverse
similarity notions between entities need to be
supported in heterogeneous domains, e.g., image
resolution is an important feature for cameras, but not
for fruits. To address this challenge, we propose a
hybrid relationship clustering algorithm, called Hydra,
using co-occurrence and numeric features. Algorithm
Hydra captures diverse user perceptions from
co-occurrence and disambiguates different senses using
feature-based similarity. In addition, we extend Hydra
into $ \mathsf {Hydra}_\mathsf {gData} $ with different
sources, i.e., entity types and crowdsourcing.
Experimental results show that the proposed algorithms
achieve effectiveness and efficiency in real-life and
synthetic datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhao:2013:EPG,
author = "Xiang Zhao and Chuan Xiao and Xuemin Lin and Wei Wang
and Yoshiharu Ishikawa",
title = "Efficient processing of graph similarity queries with
edit distance constraints",
journal = j-VLDB-J,
volume = "22",
number = "6",
pages = "727--752",
month = dec,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0306-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Feb 13 09:58:45 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Graphs are widely used to model complicated data
semantics in many applications in bioinformatics,
chemistry, social networks, pattern recognition, etc. A
recent trend is to tolerate noise arising from various
sources such as erroneous data entries and find
similarity matches. In this paper, we study graph
similarity queries with edit distance constraints.
Inspired by the q -gram idea for string similarity
problems, our solution extracts paths from graphs as
features for indexing. We establish a lower bound of
common features to generate candidates. Efficient
algorithms are proposed to handle three types of graph
similarity queries by exploiting both matching and
mismatching features as well as degree information to
improve the filtering and verification on candidates.
We demonstrate the proposed algorithms significantly
outperform existing approaches with extensive
experiments on real and synthetic datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gemulla:2013:NUI,
author = "Rainer Gemulla and Peter J. Haas and Wolfgang Lehner",
title = "Non-uniformity issues and workarounds in bounded-size
sampling",
journal = j-VLDB-J,
volume = "22",
number = "6",
pages = "753--772",
month = dec,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0307-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Feb 13 09:58:45 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A variety of schemes have been proposed in the
literature to speed up query processing and analytics
by incrementally maintaining a bounded-size uniform
sample from a dataset in the presence of a sequence of
insertion, deletion, and update transactions. These
algorithms vary according to whether the dataset is an
ordinary set or a multiset and whether the transaction
sequence consists only of insertions or can include
deletions and updates. We report on subtle
non-uniformity issues that we found in a number of
these prior bounded-size sampling schemes, including
some of our own. We provide workarounds that can avoid
the non-uniformity problem; these workarounds are easy
to implement and incur negligible additional cost. We
also consider the impact of non-uniformity in practice
and describe simple statistical tests that can help
detect non-uniformity in new algorithms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Whang:2013:JER,
author = "Steven Euijong Whang and Hector Garcia-Molina",
title = "Joint entity resolution on multiple datasets",
journal = j-VLDB-J,
volume = "22",
number = "6",
pages = "773--795",
month = dec,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0308-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Feb 13 09:58:45 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Entity resolution (ER) is the problem of identifying
which records in a database represent the same entity.
Often, records of different types are involved (e.g.,
authors, publications, institutions, venues), and
resolving records of one type can impact the resolution
of other types of records. In this paper we propose a
flexible, modular resolution framework where existing
ER algorithms developed for a given record type can be
plugged in and used in concert with other ER
algorithms. Our approach also makes it possible to run
ER on subsets of similar records at a time, important
when the full data are too large to resolve together.
We study the scheduling and coordination of the
individual ER algorithms, in order to resolve the full
dataset, and show the scalability of our approach. We
also introduce a ``state-based'' training technique
where each ER algorithm is trained for the particular
execution context (relative to other types of records)
where it will be used.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xu:2013:DPH,
author = "Jia Xu and Zhenjie Zhang and Xiaokui Xiao and Yin Yang
and Ge Yu and Marianne Winslett",
title = "Differentially private histogram publication",
journal = j-VLDB-J,
volume = "22",
number = "6",
pages = "797--822",
month = dec,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0309-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Feb 13 09:58:45 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Differential privacy (DP) is a promising scheme for
releasing the results of statistical queries on
sensitive data, with strong privacy guarantees against
adversaries with arbitrary background knowledge.
Existing studies on differential privacy mostly focus
on simple aggregations such as counts. This paper
investigates the publication of DP-compliant
histograms, which is an important analytical tool for
showing the distribution of a random variable, e.g.,
hospital bill size for certain patients. Compared to
simple aggregations whose results are purely numerical,
a histogram query is inherently more complex, since it
must also determine its structure, i.e., the ranges of
the bins. As we demonstrate in the paper, a
DP-compliant histogram with finer bins may actually
lead to significantly lower accuracy than a coarser
one, since the former requires stronger perturbations
in order to satisfy DP. Moreover, the histogram
structure itself may reveal sensitive information,
which further complicates the problem. Motivated by
this, we propose two novel mechanisms, namely
NoiseFirst and StructureFirst, for computing
DP-compliant histograms. Their main difference lies in
the relative order of the noise injection and the
histogram structure computation steps. NoiseFirst has
the additional benefit that it can improve the accuracy
of an already published DP-compliant histogram computed
using a naive method. For each of proposed mechanisms,
we design algorithms for computing the optimal
histogram structure with two different objectives:
minimizing the mean square error and the mean absolute
error, respectively. Going one step further, we extend
both mechanisms to answer arbitrary range queries.
Extensive experiments, using several real datasets,
confirm that our two proposals output highly accurate
query answers and consistently outperform existing
competitors.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fink:2013:AAP,
author = "Robert Fink and Jiewen Huang and Dan Olteanu",
title = "Anytime approximation in probabilistic databases",
journal = j-VLDB-J,
volume = "22",
number = "6",
pages = "823--848",
month = dec,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0310-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Feb 13 09:58:45 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This article describes an approximation algorithm for
computing the probability of propositional formulas
over discrete random variables. It incrementally
refines lower and upper bounds on the probability of
the formulas until the desired absolute or relative
error guarantee is reached. This algorithm is used by
the SPROUT query engine to approximate the
probabilities of results to relational algebra queries
on expressive probabilistic databases.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Drosou:2013:YER,
author = "Marina Drosou and Evaggelia Pitoura",
title = "{YmalDB}: exploring relational databases via
result-driven recommendations",
journal = j-VLDB-J,
volume = "22",
number = "6",
pages = "849--874",
month = dec,
year = "2013",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0311-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Feb 13 09:58:45 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The typical user interaction with a database system is
through queries. However, many times users do not have
a clear understanding of their information needs or the
exact content of the database. In this paper, we
propose assisting users in database exploration by
recommending to them additional items, called Ymal
(``You May Also Like'') results, that, although not
part of the result of their original query, appear to
be highly related to it. Such items are computed based
on the most interesting sets of attribute values,
called faSets, that appear in the result of the
original query. The interestingness of a faSet is
defined based on its frequency in the query result and
in the database. Database frequency estimations rely on
a novel approach of maintaining a set of representative
rare faSets. We have implemented our approach and
report results regarding both its performance and its
usefulness.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Johnson:2014:EUC,
author = "Ryan Johnson and Ippokratis Pandis and Anastasia
Ailamaki",
title = "Eliminating unscalable communication in transaction
processing",
journal = j-VLDB-J,
volume = "23",
number = "1",
pages = "1--23",
month = feb,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0312-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Feb 13 09:58:46 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Multicore hardware demands software parallelism.
Transaction processing workloads typically exhibit high
concurrency, and, thus, provide ample opportunities for
parallel execution. Unfortunately, because of the
characteristics of the application, transaction
processing systems must moderate and coordinate
communication between independent agents; since it is
notoriously difficult to implement high performing
transaction processing systems that incur no
communication whatsoever. As a result, transaction
processing systems cannot always convert abundant, even
embarrassing, request-level parallelism into execution
parallelism due to communication bottlenecks.
Transaction processing system designers must therefore
find ways to achieve scalability while still allowing
communication to occur. To this end, we identify three
forms of communication in the system-- unbounded,
fixed, and cooperative --and argue that only the first
type poses a fundamental threat to scalability. The
other two types tend not impose obstacles to
scalability, though they may reduce single-thread
performance. We argue that proper analysis of
communication patterns in any software system is a
powerful tool for improving the system's scalability.
Then, we present and evaluate under a common framework
techniques that attack significant sources of unbounded
communication during transaction processing and sketch
a solution for those that remain. The solutions we
present affect fundamental services of any transaction
processing engine, such as locking, logging, physical
page accesses, and buffer pool frame accesses. They
either reduce such communication through caching,
downgrade it to a less-threatening type, or eliminate
it completely through system design. We find that the
later technique, revisiting the transaction processing
architecture, is the most effective. The final design
cuts unbounded communication by roughly an order of
magnitude compared with the baseline, while exhibiting
better scalability on multicore machines.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhou:2014:EQP,
author = "Junfeng Zhou and Zhifeng Bao and Wei Wang and Jinjia
Zhao and Xiaofeng Meng",
title = "Efficient query processing for {XML} keyword queries
based on the {IDList} index",
journal = j-VLDB-J,
volume = "23",
number = "1",
pages = "25--50",
month = feb,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0313-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Feb 13 09:58:46 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Keyword search over XML data has attracted a lot of
research efforts in the last decade, where one of the
fundamental research problems is how to efficiently
answer a given keyword query w.r.t. a certain query
semantics. We found that the key factor resulting in
the inefficiency for existing methods is that they all
heavily suffer from the common-ancestor-repetition
problem. In this paper, we propose a novel form of
inverted list, namely the IDList; the IDList for
keyword k consists of ordered nodes that directly or
indirectly contain k . We then show that finding
keyword query results based on the smallest lowest
common ancestor and exclusive lowest common ancestor
semantics can be reduced to ordered set intersection
problem, which has been heavily optimized due to its
application in areas such as information retrieval and
database systems. We propose several algorithms that
exploit set intersection in different directions and
with or without using additional indexes. We further
propose several algorithms that are based on hash
search to simplify the operation of finding common
nodes from all involved IDLists. We have conducted an
extensive set of experiments using many
state-of-the-art algorithms and several large-scale
datasets. The results demonstrate that our proposed
methods outperform existing methods by up to two orders
of magnitude in many cases.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Slavov:2014:GBA,
author = "Vasil Slavov and Praveen Rao",
title = "A gossip-based approach for {Internet}-scale
cardinality estimation of {XPath} queries over
distributed semistructured data",
journal = j-VLDB-J,
volume = "23",
number = "1",
pages = "51--76",
month = feb,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0314-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Feb 13 09:58:46 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we address the problem of cardinality
estimation of XPath queries over XML data stored in a
distributed, Internet-scale environment such as a
large-scale, data sharing system designed to foster
innovations in biomedical and health informatics. The
cardinality estimate of XPath expressions is useful in
XQuery optimization, designing IR-style relevance
ranking schemes, and statistical hypothesis testing. We
present a novel gossip algorithm called XGossip, which
given an XPath query estimates the number of XML
documents in the network that contain a match for the
query. XGossip is designed to be scalable,
decentralized, and robust to failures--properties that
are desirable in a large-scale distributed system.
XGossip employs a novel divide-and-conquer strategy for
load balancing and reducing the bandwidth consumption.
We conduct theoretical analysis of XGossip in terms of
accuracy of cardinality estimation, message complexity,
and bandwidth consumption. We present a comprehensive
performance evaluation of XGossip on Amazon EC2 using a
heterogeneous collection of XML documents.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Whang:2014:IER,
author = "Steven Euijong Whang and Hector Garcia-Molina",
title = "Incremental entity resolution on rules and data",
journal = j-VLDB-J,
volume = "23",
number = "1",
pages = "77--102",
month = feb,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0315-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Feb 13 09:58:46 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Entity resolution (ER) identifies database records
that refer to the same real-world entity. In practice,
ER is not a one-time process, but is constantly
improved as the data, schema and application are better
understood. We first address the problem of keeping the
ER result up-to-date when the ER logic or data
``evolve'' frequently. A na{\"\i}ve approach that
re-runs ER from scratch may not be tolerable for
resolving large datasets. This paper investigates when
and how we can instead exploit previous
``materialized'' ER results to save redundant work with
evolved logic and data. We introduce algorithm
properties that facilitate evolution, and we propose
efficient rule and data evolution techniques for three
ER models: match-based clustering (records are
clustered based on Boolean matching information),
distance-based clustering (records are clustered based
on relative distances), and pairs ER (the pairs of
matching records are identified). Using real datasets,
we illustrate the cost of materializations and the
potential gains of evolution over the na{\"\i}ve
approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Beskales:2014:SRC,
author = "George Beskales and Ihab F. Ilyas and Lukasz Golab and
Artur Galiullin",
title = "Sampling from repairs of conditional functional
dependency violations",
journal = j-VLDB-J,
volume = "23",
number = "1",
pages = "103--128",
month = feb,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0316-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Feb 13 09:58:46 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Violations of functional dependencies (FDs) and
conditional functional dependencies (CFDs) are common
in practice, often indicating deviations from the
intended data semantics. These violations arise in many
contexts such as data integration and Web data
extraction. Resolving these violations is challenging
for a variety of reasons, one of them being the
exponential number of possible repairs. Most of the
previous work has tackled this problem by producing a
single repair that is nearly optimal with respect to
some metric. In this paper, we propose a novel data
cleaning approach that is not limited to finding a
single repair, namely sampling from the space of
possible repairs. We give several motivating scenarios
where sampling from the space of CFD repairs is
desirable, we propose a new class of useful repairs,
and we present an algorithm that randomly samples from
this space in an efficient way. We also show how to
restrict the space of repairs based on constraints that
reflect the accuracy of different parts of the
database. We experimentally evaluate our algorithms
against previous approaches to show the utility and
efficiency of our approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lee:2014:TEM,
author = "Jongwuk Lee and Seung-Won Hwang",
title = "Toward efficient multidimensional subspace skyline
computation",
journal = j-VLDB-J,
volume = "23",
number = "1",
pages = "129--145",
month = feb,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0317-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Feb 13 09:58:46 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Skyline queries have attracted considerable attention
to assist multicriteria analysis of large-scale
datasets. In this paper, we focus on multidimensional
subspace skyline computation that has been actively
studied for two approaches. First, to narrow down a
full-space skyline, users may consider multiple
subspace skylines reflecting their interest. For this
purpose, we tackle the concept of a skycube, which
consists of all possible non-empty subspace skylines in
a given full space. Second, to understand diverse
semantics of subspace skylines, we address skyline
groups in which a skyline point (or a set of skyline
points) is annotated with decisive subspaces. Our
primary contributions are to identify common building
blocks of the two approaches and to develop orthogonal
optimization principles that benefit both approaches.
Our experimental results show the efficiency of
proposed algorithms by comparing them with
state-of-the-art algorithms in both synthetic and
real-life datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zellag:2014:CAM,
author = "Kamal Zellag and Bettina Kemme",
title = "Consistency anomalies in multi-tier architectures:
automatic detection and prevention",
journal = j-VLDB-J,
volume = "23",
number = "1",
pages = "147--172",
month = feb,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0318-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Feb 13 09:58:46 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Modern transaction systems, consisting of an
application server tier and a database tier, offer
several levels of isolation providing a trade-off
between performance and consistency. While it is fairly
well known how to identify qualitatively the anomalies
that are possible under a certain isolation level, it
is much more difficult to detect and quantify such
anomalies during run-time of a given application. In
this paper, we present a new approach to detect and
quantify consistency anomalies for arbitrary multi-tier
application running under any isolation levels ensuring
at least read committed. In fact, the application can
run even under a mixture of isolation levels. Our
detection approach can be online or off-line and for
each detected anomaly, we identify exactly the
transactions and data items involved. Furthermore, we
classify the detected anomalies into patterns showing
the business methods involved as well as analyzing the
types of cycles that occur. Our approach can help
designers to either choose an isolation level where the
anomalies do not occur or to change the transaction
design to avoid the anomalies. Furthermore, we provide
an option in which the occurrence of anomalies can be
automatically reduced during run-time. To test the
effectiveness and efficiency of our approach, we have
conducted a set of experiments using a wide range of
benchmarks.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ozsoyoglu:2014:SIB,
author = "Z. Meral {\"O}zsoyo{\u{g}}lu and U{\u{g}}ur
{\c{C}}etintemel and Nilesh Dalvi and Hank Korth and
Anthony Tung",
title = "Special issue on best papers of {VLDB 2012}",
journal = j-VLDB-J,
volume = "23",
number = "2",
pages = "173--174",
month = apr,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0356-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 26 17:19:12 MDT 2016",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-014-0356-z;
http://link.springer.com/content/pdf/10.1007/s00778-014-0356-z.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://link.springer.com/journal/778",
}
@Article{Angel:2014:DSM,
author = "Albert Angel and Nick Koudas and Nikos Sarkas and
Divesh Srivastava and Michael Svendsen and Srikanta
Tirthapura",
title = "Dense subgraph maintenance under streaming edge weight
updates for real-time story identification",
journal = j-VLDB-J,
volume = "23",
number = "2",
pages = "175--199",
month = apr,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0340-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 26 17:19:12 MDT 2016",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-013-0340-z",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://link.springer.com/journal/778",
}
@Article{Das:2014:EFE,
author = "Mahashweta Das and Saravanan Thirumuruganathan and
Sihem Amer-Yahia and Gautam Das and Cong Yu",
title = "An expressive framework and efficient algorithms for
the analysis of collaborative tagging",
journal = j-VLDB-J,
volume = "23",
number = "2",
pages = "201--226",
month = apr,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0341-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 26 17:19:12 MDT 2016",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-013-0341-y",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://link.springer.com/journal/778",
}
@Article{Cheng:2014:EPH,
author = "James Cheng and Zechao Shang and Hong Cheng and Haixun
Wang and Jeffrey Xu Yu",
title = "Efficient processing of $k$-hop reachability queries",
journal = j-VLDB-J,
volume = "23",
number = "2",
pages = "227--252",
month = apr,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0346-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 26 17:19:12 MDT 2016",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-013-0346-6",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://link.springer.com/journal/778",
}
@Article{Koch:2014:DHO,
author = "Christoph Koch and Yanif Ahmad and Oliver Kennedy and
Milos Nikolic and Andres N{\"o}tzli and Daniel Lupei
and Amir Shaikhha",
title = "{DBToaster}: higher-order delta processing for
dynamic, frequently fresh views",
journal = j-VLDB-J,
volume = "23",
number = "2",
pages = "253--278",
month = apr,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0348-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 26 17:19:12 MDT 2016",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-013-0348-4",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://link.springer.com/journal/778",
}
@Article{Bailis:2014:QEC,
author = "Peter Bailis and Shivaram Venkataraman and Michael J.
Franklin and Joseph M. Hellerstein and Ion Stoica",
title = "Quantifying eventual consistency with {PBS}",
journal = j-VLDB-J,
volume = "23",
number = "2",
pages = "279--302",
month = apr,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0330-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 26 17:19:12 MDT 2016",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-013-0330-1",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://link.springer.com/journal/778",
}
@Article{Graefe:2014:TSA,
author = "Goetz Graefe and Felix Halim and Stratos Idreos and
Harumi Kuno and Stefan Manegold and Bernhard Seeger",
title = "Transactional support for adaptive indexing",
journal = j-VLDB-J,
volume = "23",
number = "2",
pages = "303--328",
month = apr,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0345-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 26 17:19:12 MDT 2016",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-013-0345-7",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://link.springer.com/journal/778",
}
@Article{Zhang:2014:TCE,
author = "Ning Zhang and Junichi Tatemura and Jignesh M. Patel
and Hakan Hacigumus",
title = "Toward cost-effective storage provisioning for
{DBMSs}",
journal = j-VLDB-J,
volume = "23",
number = "2",
pages = "329--354",
month = apr,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0334-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 26 17:19:12 MDT 2016",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-013-0334-x",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://link.springer.com/journal/778",
}
@Article{Doulkeridis:2014:SLS,
author = "Christos Doulkeridis and Kjetil N{\o}rv{\aa}g",
title = "A survey of large-scale analytical query processing in
{MapReduce}",
journal = j-VLDB-J,
volume = "23",
number = "3",
pages = "355--380",
month = jun,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0319-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 29 06:13:52 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Enterprises today acquire vast volumes of data from
different sources and leverage this information by
means of data analysis to support effective
decision-making and provide new functionality and
services. The key requirement of data analytics is
scalability, simply due to the immense volume of data
that need to be extracted, processed, and analyzed in a
timely fashion. Arguably the most popular framework for
contemporary large-scale data analytics is MapReduce,
mainly due to its salient features that include
scalability, fault-tolerance, ease of programming, and
flexibility. However, despite its merits, MapReduce has
evident performance limitations in miscellaneous
analytical tasks, and this has given rise to a
significant body of research that aim at improving its
efficiency, while maintaining its desirable properties.
This survey aims to review the state of the art in
improving the performance of parallel query processing
using MapReduce. A set of the most significant
weaknesses and limitations of MapReduce is discussed at
a high level, along with solving techniques. A taxonomy
is presented for categorizing existing research on
MapReduce improvements according to the specific
problem they target. Based on the proposed taxonomy, a
classification of existing research is provided
focusing on the optimization objective. Concluding, we
outline interesting directions for future parallel data
processing systems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhou:2014:EDT,
author = "Xiangmin Zhou and Lei Chen",
title = "Event detection over {Twitter} social media streams",
journal = j-VLDB-J,
volume = "23",
number = "3",
pages = "381--400",
month = jun,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0320-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 29 06:13:52 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In recent years, microblogs have become an important
source for reporting real-world events. A real-world
occurrence reported in microblogs is also called a
social event. Social events may hold critical materials
that describe the situations during a crisis. In real
applications, such as crisis management and decision
making, monitoring the critical events over social
streams will enable watch officers to analyze a whole
situation that is a composite event, and make the right
decision based on the detailed contexts such as what is
happening, where an event is happening, and who are
involved. Although there has been significant research
effort on detecting a target event in social networks
based on a single source, in crisis, we often want to
analyze the composite events contributed by different
social users. So far, the problem of integrating
ambiguous views from different users is not well
investigated. To address this issue, we propose a novel
framework to detect composite social events over
streams, which fully exploits the information of social
data over multiple dimensions. Specifically, we first
propose a graphical model called location-time
constrained topic (LTT) to capture the content, time,
and location of social messages. Using LTT, a social
message is represented as a probability distribution
over a set of topics by inference, and the similarity
between two messages is measured by the distance
between their distributions. Then, the events are
identified by conducting efficient similarity joins
over social media streams. To accelerate the similarity
join, we also propose a variable dimensional extendible
hash over social streams. We have conducted extensive
experiments to prove the high effectiveness and
efficiency of the proposed approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hung:2014:QTB,
author = "Ho Hoang Hung and Sourav S. Bhowmick and Ba Quan
Truong and Byron Choi and Shuigeng Zhou",
title = "{QUBLE}: towards blending interactive visual subgraph
search queries on large networks",
journal = j-VLDB-J,
volume = "23",
number = "3",
pages = "401--426",
month = jun,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0322-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 29 06:13:52 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In a previous paper, we laid out the vision of a novel
graph query processing paradigm where instead of
processing a visual query graph after its construction,
it interleaves visual query formulation and processing
by exploiting the latency offered by the gui to filter
irrelevant matches and prefetch partial query results [
8]. Our recent attempts at implementing this vision [8,
9] show significant improvement in system response time
(srt) for subgraph queries. However, these efforts are
designed specifically for graph databases containing a
large collection of small or medium-sized graphs. In
this paper, we propose a novel algorithm called QUBLE
(QUery Blender for Large nEtworks) to realize this
visual subgraph querying paradigm on very large
networks (e.g., protein interaction networks, social
networks). First, it decomposes a large network into a
set of graphlets and supergraphlets using a minimum
cut-based graph partitioning technique. Next, it mines
approximate frequent and small infrequent fragments
(sifs) from them and identifies their occurrences in
these graphlets and supergraphlets. Then, the indexing
framework of [9] is enhanced so that the mined
fragments can be exploited to index graphlets for
efficient blending of visual subgraph query formulation
and query processing. Extensive experiments on large
networks demonstrate effectiveness of QUBLE.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Teodoro:2014:ASS,
author = "George Teodoro and Eduardo Valle and Nathan Mariano
and Ricardo Torres and Wagner {Meira, Jr.} and Joel H.
Saltz",
title = "Approximate similarity search for online multimedia
services on distributed {CPU--GPU} platforms",
journal = j-VLDB-J,
volume = "23",
number = "3",
pages = "427--448",
month = jun,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0329-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 29 06:13:52 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Similarity search in high-dimensional spaces is a
pivotal operation for several database applications,
including online content-based multimedia services.
With the increasing popularity of multimedia
applications, these services are facing new challenges
regarding (1) the very large and growing volumes of
data to be indexed/searched and (2) the necessity of
reducing the response times as observed by end-users.
In addition, the nature of the interactions between
users and online services creates fluctuating query
request rates throughout execution, which requires a
similarity search engine to adapt to better use the
computation platform and minimize response times. In
this work, we address these challenges with
Hypercurves, a flexible framework for answering
approximate k-nearest neighbor (kNN) queries for very
large multimedia databases. Hypercurves executes in
hybrid CPU---GPU environments and is able to attain
massive query-processing rates through the cooperative
use of these devices. Hypercurves also changes its
CPU---GPU task partitioning dynamically according to
the observed load, aiming for optimal response times.
In our empirical evaluation, dynamic task partitioning
reduced query response times by approximately 50\%
compared to the best static task partition. Due to a
probabilistic proof of equivalence to the sequential
kNN algorithm, the CPU---GPU execution of Hypercurves
in distributed (multi-node) environments can be
aggressively optimized, attaining superlinear
scalability while still guaranteeing, with high
probability, results at least as good as those from the
sequential algorithm.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Shang:2014:PTM,
author = "Shuo Shang and Ruogu Ding and Kai Zheng and Christian
S. Jensen and Panos Kalnis and Xiaofang Zhou",
title = "Personalized trajectory matching in spatial networks",
journal = j-VLDB-J,
volume = "23",
number = "3",
pages = "449--468",
month = jun,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0331-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 29 06:13:52 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "With the increasing availability of moving-object
tracking data, trajectory search and matching is
increasingly important. We propose and investigate a
novel problem called personalized trajectory matching
(PTM). In contrast to conventional trajectory
similarity search by spatial distance only, PTM takes
into account the significance of each sample point in a
query trajectory. A PTM query takes a trajectory with
user-specified weights for each sample point in the
trajectory as its argument. It returns the trajectory
in an argument data set with the highest similarity to
the query trajectory. We believe that this type of
query may bring significant benefits to users in many
popular applications such as route planning,
carpooling, friend recommendation, traffic analysis,
urban computing, and location-based services in
general. PTM query processing faces two challenges: how
to prune the search space during the query processing
and how to schedule multiple so-called expansion
centers effectively. To address these challenges, a
novel two-phase search algorithm is proposed that
carefully selects a set of expansion centers from the
query trajectory and exploits upper and lower bounds to
prune the search space in the spatial and temporal
domains. An efficiency study reveals that the algorithm
explores the minimum search space in both domains.
Second, a heuristic search strategy based on priority
ranking is developed to schedule the multiple expansion
centers, which can further prune the search space and
enhance the query efficiency. The performance of the
PTM query is studied in extensive experiments based on
real and synthetic trajectory data sets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Richter:2014:TZO,
author = "Stefan Richter and Jorge-Arnulfo Quian{\'e}-Ruiz and
Stefan Schuh and Jens Dittrich",
title = "Towards zero-overhead static and adaptive indexing in
{Hadoop}",
journal = j-VLDB-J,
volume = "23",
number = "3",
pages = "469--494",
month = jun,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0332-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 29 06:13:52 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Hadoop MapReduce has evolved to an important industry
standard for massive parallel data processing and has
become widely adopted for a variety of use-cases.
Recent works have shown that indexes can improve the
performance of selective MapReduce jobs dramatically.
However, one major weakness of existing approaches is
high index creation costs. We present HAIL (Hadoop
Aggressive Indexing Library), a novel indexing approach
for HDFS and Hadoop MapReduce. HAIL creates different
clustered indexes over terabytes of data with minimal,
often invisible costs, and it dramatically improves
runtimes of several classes of MapReduce jobs. HAIL
features two different indexing pipelines, static
indexing and adaptive indexing. HAIL static indexing
efficiently indexes datasets while uploading them to
HDFS. Thereby, HAIL leverages the default replication
of Hadoop and enhances it with logical replication.
This allows HAIL to create multiple clustered indexes
for a dataset, e.g., one for each physical replica.
Still, in terms of upload time, HAIL matches or even
improves over the performance of standard HDFS.
Additionally, HAIL adaptive indexing allows for
automatic, incremental indexing at job runtime with
minimal runtime overhead. For example, HAIL adaptive
indexing can completely index a dataset as byproduct of
only four MapReduce jobs while incurring an overhead as
low as 11\% for the very first of those job only. In
our experiments, we show that HAIL improves job
runtimes by up to $ 68 \times $ over Hadoop. This
article is an extended version of the VLDB 2012 paper
(Dittrich et al. in PVLDB 5(11):1591---1602, 2012).",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Meier:2014:BR,
author = "Michael Meier",
title = "The backchase revisited",
journal = j-VLDB-J,
volume = "23",
number = "3",
pages = "495--516",
month = jun,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0333-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 29 06:13:52 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Semantic query optimization is the process of finding
equivalent rewritings of an input query given
constraints that hold in a database instance. In this
paper, we report about a Chase \& Backchase (C\&B)
algorithm strategy that generalizes and improves on
well-known methods in the field. The implementation of
our approach, the Pegasussystem, outperforms existing
C\&B systems an average by two orders of magnitude.
This gain in performance is due to a combination of
novel methods that lower the complexity in practical
situations significantly.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gedik:2014:PFS,
author = "Bugra Gedik",
title = "Partitioning functions for stateful data parallelism
in stream processing",
journal = j-VLDB-J,
volume = "23",
number = "4",
pages = "517--539",
month = aug,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0335-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 16 17:57:07 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we study partitioning functions for
stream processing systems that employ stateful data
parallelism to improve application throughput. In
particular, we develop partitioning functions that are
effective under workloads where the domain of the
partitioning key is large and its value distribution is
skewed. We define various desirable properties for
partitioning functions, ranging from balance properties
such as memory, processing, and communication balance,
structural properties such as compactness and fast
lookup, and adaptation properties such as fast
computation and minimal migration. We introduce a
partitioning function structure that is compact and
develop several associated heuristic construction
techniques that exhibit good balance and low migration
cost under skewed workloads. We provide experimental
results that compare our partitioning functions to more
traditional approaches such as uniform and consistent
hashing, under different workload and application
characteristics, and show superior performance.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Koh:2014:FKM,
author = "Jia-Ling Koh and Chen-Yi Lin and Arbee L. Chen",
title = "Finding $ k k $ most favorite products based on
reverse top-$ t t $ queries",
journal = j-VLDB-J,
volume = "23",
number = "4",
pages = "541--564",
month = aug,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0336-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 16 17:57:07 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A reverse top-$t$ query for a product returns a set of
customers, named potential customers, who regard the
product as one of their top-$t$ favorites. Given a set
of customers with different preferences on the features
of the products, we want to select at most k k products
from a pool of candidate products such that their total
number of potential customers is maximized. Two
versions of the problem are defined according to
whether the competitive existing products are given.
For solving this NP-hard problem, we first propose an
incremental greedy approach to find an approximate
solution of the problem with quality guaranteed. For
further speeding up this basic greedy approach, we
exploit several properties of the top-$ t t$ queries
and skyline queries to reduce the solution space of the
problem. In addition, an upper bound of the potential
customers is estimated to reduce the cost of computing
the reverse top-$ t t$ queries for the candidate
products. Finally, when the candidate products are
formed from multiple component tables, we propose a
strategy to reduce the number of the accessed tuples in
the component tables such that only the tuples that are
possibly components of the top-$ t t$ favorites of the
customers need to be accessed. By applying these
pruning strategies, we propose another faster greedy
approach. The experiment results demonstrate that the
proposed pruning strategies work very well and make the
faster greedy algorithms for both versions of the
problem achieve excellent performance on both
efficiency and memory utilization.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zou:2014:GGB,
author = "Lei Zou and M. Tamer {\"O}zsu and Lei Chen and Xuchuan
Shen and Ruizhe Huang and Dongyan Zhao",
title = "{gStore}: a graph-based {SPARQL} query engine",
journal = j-VLDB-J,
volume = "23",
number = "4",
pages = "565--590",
month = aug,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0337-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 16 17:57:07 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We address efficient processing of SPARQL queries over
RDF datasets. The proposed techniques, incorporated
into the gStore system, handle, in a uniform and
scalable manner, SPARQL queries with wildcards and
aggregate operators over dynamic RDF datasets. Our
approach is graph based. We store RDF data as a large
graph and also represent a SPARQL query as a query
graph. Thus, the query answering problem is converted
into a subgraph matching problem. To achieve efficient
and scalable query processing, we develop an index,
together with effective pruning rules and efficient
search algorithms. We propose techniques that use this
infrastructure to answer aggregation queries. We also
propose an effective maintenance algorithm to handle
online updates over RDF repositories. Extensive
experiments confirm the efficiency and effectiveness of
our solutions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Tao:2014:ILW,
author = "Yufei Tao and Yi Yang and Xiaocheng Hu and Cheng Sheng
and Shuigeng Zhou",
title = "Instance-level worst-case query bounds on {R}-trees",
journal = j-VLDB-J,
volume = "23",
number = "4",
pages = "591--607",
month = aug,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0339-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 16 17:57:07 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Even with its significant impacts on the database
area, the R-tree is often criticized by its lack of
good worst-case guarantees. For example, in range
search (where we want to report all the data points in
a query rectangle), it is known that on adversely
designed datasets and queries, an R-tree can be as slow
as a sequential scan that simply reads all the data
points. Nevertheless, R-trees work so well on real data
that they have been widely implemented in commercial
systems. This stark contrast has caused long-term
controversy between practitioners and theoreticians as
to whether this structure deserves its fame. This paper
provides theoretical evidence that, somewhat
surprisingly, R-trees are efficient in the worst case
for range search on many real datasets. Given any
integer K K, we explain how to obtain an upper bound on
the cost of answering all (i.e., infinitely many) range
queries retrieving at most K K objects. On practical
data, the upper bound is only a fraction of the
overhead of sequential scan (unless, apparently, K K is
at the same order as the dataset size). Our upper
bounds are tight up to a constant factor, namely they
cannot be lowered by more than $ O(1) O(1) $ times
while still capturing the most expensive queries. Our
upper bounds can be calculated in constant time by
remembering only three integers. These integers, in
turn, are generated from only the leaf MBRs of an
R-tree, but not the leaf nodes themselves. In practice,
the internal nodes are often buffered in memory, so
that the integers aforementioned can be efficiently
maintained along with the data updates and made
available to a query optimizer at any time.
Furthermore, our analytical framework introduces
instance-level query bound as a new technique for
evaluating the efficiency of heuristic structures in a
theory-flavored manner (previously, experimentation was
the dominant assessment method).",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Cicek:2014:ELD,
author = "A. Ercument Cicek and Mehmet Ercan Nergiz and Yucel
Saygin",
title = "Ensuring location diversity in privacy-preserving
spatio-temporal data publishing",
journal = j-VLDB-J,
volume = "23",
number = "4",
pages = "609--625",
month = aug,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0342-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 16 17:57:07 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The rise of mobile technologies in the last decade has
led to vast amounts of location information generated
by individuals. From the knowledge discovery point of
view, these data are quite valuable, but the inherent
personal information in the data raises privacy
concerns. There exists many algorithms in the
literature to satisfy the privacy requirements of
individuals, by generalizing, perturbing, and
suppressing their data. Current techniques that try to
ensure a level of indistinguishability between
trajectories in a dataset are direct applications of k
k -anonymity, thus suffer from the shortcomings of k k
-anonymity such as the lack of diversity in sensitive
regions. Moreover, these techniques fail to incorporate
some common background knowledge, an adversary might
have such as the underlying map, the traffic density,
and the anonymization algorithm itself. We propose a
new privacy metric p p -confidentiality that ensures
location diversity by bounding the probability of a
user visiting a sensitive location with the p p input
parameter. We perform our probabilistic analysis based
on the background knowledge of the adversary. Instead
of grouping the trajectories, we anonymize the
underlying map, that is, we group nodes (points of
interest) to create obfuscation areas around sensitive
locations. The groups are formed in such a way that the
parts of trajectories entering the groups, coupled with
the adversary background, do not increase the
adversary's belief in violating the p p
-confidentiality. We then use the map anonymization as
a model to anonymize the trajectories. We prove that
our algorithm is resistant to reverse-engineering
attacks when the statistics required for map
anonymization is publicly available. We empirically
evaluate the performance of our algorithm and show that
location diversity can be satisfied effectively.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Unterbrunner:2014:HAE,
author = "Philipp Unterbrunner and Gustavo Alonso and Donald
Kossmann",
title = "High availability, elasticity, and strong consistency
for massively parallel scans over relational data",
journal = j-VLDB-J,
volume = "23",
number = "4",
pages = "627--652",
month = aug,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0343-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 16 17:57:07 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "An elastic and highly available data store is a key
component of many cloud applications. Existing data
stores with strong consistency guarantees are designed
and optimized for small updates, key-value access, and
(if supported) small range queries over a predefined
key column. This raises performance and availability
problems for applications which inherently require
large updates, non-key access, and large range queries.
This paper presents a solution to these problems:
Crescando/RB; a distributed, scan-based, main memory,
relational data store (single table) with robust
performance and high availability. The system addresses
a real, large-scale industry use case: the Amadeus
travel management system. This paper focuses on the
distribution layer of Crescando/RB, the problem and
theory behind it, the rationale underlying key design
decisions, and the novel multicast protocol and
replication framework it is composed of. Highlighting
the key features of the distribution layer, we present
experimental results showing that even under permanent
node failures and large-scale data repartitioning,
Crescando/RB remains fully available and capable of
sustaining a heavy query and update load.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2014:CND,
author = "Rui Chen and Benjamin C. Fung and Philip S. Yu and
Bipin C. Desai",
title = "Correlated network data publication via differential
privacy",
journal = j-VLDB-J,
volume = "23",
number = "4",
pages = "653--676",
month = aug,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0344-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Jul 16 17:57:07 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "With the increasing prevalence of information
networks, research on privacy-preserving network data
publishing has received substantial attention recently.
There are two streams of relevant research, targeting
different privacy requirements. A large body of
existing works focus on preventing node
re-identification against adversaries with structural
background knowledge, while some other studies aim to
thwart edge disclosure. In general, the line of
research on preventing edge disclosure is less
fruitful, largely due to lack of a formal privacy
model. The recent emergence of differential privacy has
shown great promise for rigorous prevention of edge
disclosure. Yet recent research indicates that
differential privacy is vulnerable to data correlation,
which hinders its application to network data that may
be inherently correlated. In this paper, we show that
differential privacy could be tuned to provide provable
privacy guarantees even in the correlated setting by
introducing an extra parameter, which measures the
extent of correlation. We subsequently provide a
holistic solution for non-interactive network data
publication. First, we generate a private vertex
labeling for a given network dataset to make the
corresponding adjacency matrix form dense clusters.
Next, we adaptively identify dense regions of the
adjacency matrix by a data-dependent partitioning
process. Finally, we reconstruct a noisy adjacency
matrix by a novel use of the exponential mechanism. To
our best knowledge, this is the first work providing a
practical solution for publishing real-life network
data via differential privacy. Extensive experiments
demonstrate that our approach performs well on
different types of real-life network datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xiang:2014:AED,
author = "Yang Xiang",
title = "Answering exact distance queries on real-world graphs
with bounded performance guarantees",
journal = j-VLDB-J,
volume = "23",
number = "5",
pages = "677--695",
month = oct,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0338-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 24 08:05:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The ability to efficiently obtain exact distance
information from both directed and undirected graphs is
desired by many real-world applications. In this work,
we unified the query indexing efforts on directed and
undirected graphs into one by proposing the TreeMap
approach. Our approach has very tight bounds on query
time, index size, and construction time for answering
queries on both directed and undirected graphs. The
query time complexity is close to constant for graphs
with a small width of tree decomposition, and the index
construction can be completed without materializing the
distance matrix or other high-cost operations. In the
empirical study, we demonstrated that the TreeMap
approach in general performs much better than
competitive methods in indexing real graphs for
answering exact distance queries.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yao:2014:DMO,
author = "Bin Yao and Xiaokui Xiao and Feifei Li and Yifan Wu",
title = "Dynamic monitoring of optimal locations in road
network databases",
journal = j-VLDB-J,
volume = "23",
number = "5",
pages = "697--720",
month = oct,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0347-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 24 08:05:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Optimal location (OL) queries are a type of spatial
queries that are particularly useful for the strategic
planning of resources. Given a set of existing
facilities and a set of clients, an OL query asks for a
location to build a new facility that optimizes a
certain cost metric (defined based on the distances
between the clients and the facilities). Several
techniques have been proposed to address OL queries,
assuming that all clients and facilities reside in an $
L_p $ space. In practice, however, movements between
spatial locations are usually confined by the
underlying road network, and hence, the actual distance
between two locations can differ significantly from
their $ L_p $ distance. Motivated by the deficiency of
the existing techniques, this paper presents a
comprehensive study on OL queries in road networks. We
propose a unified framework that addresses three
variants of OL queries that find important applications
in practice, and we instantiate the framework with
several novel query processing algorithms. We further
extend our framework to efficiently monitor the OLs
when locations for facilities and/or clients have been
updated. Our dynamic update methods lead to efficient
answering of continuous optimal location queries. We
demonstrate the efficiency of our solutions through
extensive experiments with large real data.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Tran:2014:QRE,
author = "Quoc Trung Tran and Chee-Yong Chan and Srinivasan
Parthasarathy",
title = "Query reverse engineering",
journal = j-VLDB-J,
volume = "23",
number = "5",
pages = "721--746",
month = oct,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0349-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 24 08:05:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we introduce a new problem termed query
reverse engineering (QRE). Given a database $D$ and a
result table $T$ --- the output of some known or
unknown query $Q$ on $D$ --- the goal of QRE is to
reverse-engineer a query $ Q'$ such that the output of
query $ Q'$ on database $D$ (denoted by $ Q'(D)$) is
equal to $T$ (i.e., $ Q(D)$). The QRE problem has
useful applications in database usability, data
analysis, and data security. In this work, we propose a
data-driven approach, TALOS for {\bf T}ree-based
classifier with {\bf A}t {\bf L}east {\bf O}ne {\bf
S}emantics, that is based on a novel dynamic data
classification formulation and extend the approach to
efficiently support the three key dimensions of the QRE
problem: whether the input query is known\slash
unknown, supporting different query fragments, and
supporting multiple database versions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Martinenghi:2014:TBR,
author = "Davide Martinenghi and Riccardo Torlone",
title = "Taxonomy-based relaxation of query answering in
relational databases",
journal = j-VLDB-J,
volume = "23",
number = "5",
pages = "747--769",
month = oct,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-013-0350-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 24 08:05:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Traditional information search in which queries are
posed against a known and rigid schema over a
structured database is shifting toward a Web scenario
in which exposed schemas are vague or absent and data
come from heterogeneous sources. In this framework,
query answering cannot be precise and needs to be
relaxed, with the goal of matching user requests with
accessible data. In this paper, we propose a logical
model and a class of abstract query languages as a
foundation for querying relational data sets with vague
schemas. Our approach relies on the availability of
taxonomies, that is, simple classifications of terms
arranged in a hierarchical structure. The model is a
natural extension of the relational model in which data
domains are organized in hierarchies, according to
different levels of generalization between terms. We
first propose a conservative extension of the
relational algebra for this model in which special
operators allow the specification of relaxed queries
over vaguely structured information. We also study
equivalence and rewriting properties of the algebra
that can be used for query optimization. We then
illustrate a logic-based query language that can
provide a basis for expressing relaxed queries in a
declarative way. We finally investigate the expressive
power of the proposed query languages and the
independence of the taxonomy in this context.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Soria-Comas:2014:EDU,
author = "Jordi Soria-Comas and Josep Domingo-Ferrer and David
S{\'a}nchez and Sergio Mart{\'\i}nez",
title = "Enhancing data utility in differential privacy via
microaggregation-based $k$-anonymity",
journal = j-VLDB-J,
volume = "23",
number = "5",
pages = "771--794",
month = oct,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0351-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 24 08:05:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "It is not uncommon in the data anonymization
literature to oppose the ``old'' $k$-anonymity model to
the ``new'' differential privacy model, which offers
more robust privacy guarantees. Yet, it is often
disregarded that the utility of the anonymized results
provided by differential privacy is quite limited, due
to the amount of noise that needs to be added to the
output, or because utility can only be guaranteed for a
restricted type of queries. This is in contrast with
$k$-anonymity mechanisms, which make no assumptions on
the uses of anonymized data while focusing on
preserving data utility from a general perspective. In
this paper, we show that a synergy between differential
privacy and $k$-anonymity can be found: $k$-anonymity
can help improving the utility of differentially
private responses to arbitrary queries. We devote
special attention to the utility improvement of
differentially private published data sets.
Specifically, we show that the amount of noise required
to fulfill $ \varepsilon $-differential privacy can be
reduced if noise is added to a $k$-anonymous version of
the data set, where $k$-anonymity is reached through a
specially designed microaggregation of all attributes.
As a result of noise reduction, the general analytical
utility of the anonymized output is increased. The
theoretical benefits of our proposal are illustrated in
a practical setting with an empirical evaluation on
three data sets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Magnani:2014:TBP,
author = "Matteo Magnani and Ira Assent and Michael L.
Mortensen",
title = "Taking the {Big Picture}: representative skylines
based on significance and diversity",
journal = j-VLDB-J,
volume = "23",
number = "5",
pages = "795--815",
month = oct,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0352-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 24 08:05:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The skyline is a popular operator to extract records
from a database when a record scoring function is not
available. However, the result of a skyline query can
be very large. The problem addressed in this paper is
the automatic selection of a small number ($k$) of
representative skyline records. Existing approaches
have only focused on partial aspects of this problem.
Some try to identify sets of diverse records giving an
overall approximation of the skyline. These techniques,
however, are sensitive to the scaling of attributes or
to the insertion of non-skyline records into the
database. Others exploit some knowledge of the record
scoring function to identify the most significant
record, but not sets of records representative of the
whole skyline. In this paper, we introduce a novel
approach taking both the significance of all the
records and their diversity into account, adapting to
available knowledge of the scoring function, but also
working under complete ignorance. We show the
intractability of the problem and present approximate
algorithms. We experimentally show that our approach is
efficient, scalable and that it improves existing works
in terms of the significance and diversity of the
results.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sidlauskas:2014:PEM,
author = "Darius {\v{S}}idlauskas and Simonas {\v{S}}altenis and
Christian S. Jensen",
title = "Processing of extreme moving-object update and query
workloads in main memory",
journal = j-VLDB-J,
volume = "23",
number = "5",
pages = "817--841",
month = oct,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0353-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Sep 24 08:05:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The efficient processing of workloads that interleave
moving-object updates and queries is challenging. In
addition to the conflicting needs for update-efficient
versus query-efficient data structures, the increasing
parallel capabilities of multi-core processors yield
challenges. To prevent concurrency anomalies and to
ensure correct system behavior, conflicting update and
query operations must be serialized. In this setting,
it is a key concern to avoid that operations are
blocked, which leaves processing cores idle. To enable
efficient processing, we first examine concurrency
degrees from traditional transaction processing in the
context of our target domain and propose new semantics
that enable a high degree of parallelism and ensure
up-to-date query results. We define the new semantics
for range and $k$-nearest neighbor queries. Then, we
present a main-memory indexing technique called
parallel grid that implements the proposed semantics as
well as two other variants supporting different
semantics. This enables us to quantify the effects that
different degrees of consistency have on performance.
We also present an alternative time-partitioning
approach. Empirical studies with the above and three
existing proposals conducted on modern processors show
that our proposals scale near-linearly with the number
of hardware threads and thus are able to benefit from
increasing on-chip parallelism.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Aboulnaga:2014:SSD,
author = "Ashraf Aboulnaga and Beng Chin Ooi and Patrick
Valduriez",
title = "Special section on data-intensive cloud
infrastructure",
journal = j-VLDB-J,
volume = "23",
number = "6",
pages = "843--843",
month = dec,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0371-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Nov 24 15:31:08 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kumar:2014:SWA,
author = "K. Ashwin Kumar and Abdul Quamar and Amol Deshpande
and Samir Khuller",
title = "{SWORD}: workload-aware data placement and replica
selection for cloud data management systems",
journal = j-VLDB-J,
volume = "23",
number = "6",
pages = "845--870",
month = dec,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0362-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Nov 24 15:31:08 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Cloud computing is increasingly being seen as a way to
reduce infrastructure costs and add elasticity, and is
being used by a wide range of organizations. Cloud data
management systems today need to serve a range of
different workloads, from analytical read-heavy
workloads to transactional (OLTP) workloads. For both
the service providers and the users, it is critical to
minimize the consumption of resources like CPU, memory,
communication bandwidth, and energy, without
compromising on service-level agreements if any. In
this article, we develop a workload-aware data
placement and replication approach, called SWORD, for
minimizing resource consumption in such an environment.
Specifically, we monitor and model the expected
workload as a hypergraph and develop partitioning
techniques that minimize the average query span, i.e.,
the average number of machines involved in the
execution of a query or a transaction. We empirically
justify the use of query span as the metric to
optimize, for both analytical and transactional
workloads, and develop a series of replication and data
placement algorithms by drawing connections to several
well-studied graph theoretic concepts. We introduce a
suite of novel techniques to achieve high scalability
by reducing the overhead of partitioning and query
routing. To deal with workload changes, we propose an
incremental repartitioning technique that modifies data
placement in small steps without resorting to complete
repartitioning. We propose the use of fine-grained
quorums defined at the level of groups of data items to
control the cost of distributed updates, improve
throughput, and adapt to different workloads. We
empirically illustrate the benefits of our approach
through a comprehensive experimental evaluation for two
classes of workloads. For analytical read-only
workloads, we show that our techniques result in
significant reduction in total resource consumption.
For OLTP workloads, we show that our approach improves
transaction latencies and overall throughput by
minimizing the number of distributed transactions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sahli:2014:ASP,
author = "Majed Sahli and Essam Mansour and Panos Kalnis",
title = "{ACME}: a scalable parallel system for extracting
frequent patterns from a very long sequence",
journal = j-VLDB-J,
volume = "23",
number = "6",
pages = "871--893",
month = dec,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0370-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Nov 24 15:31:08 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Modern applications, including bioinformatics, time
series, and web log analysis, require the extraction of
frequent patterns, called motifs, from one very long
(i.e., several gigabytes) sequence. Existing approaches
are either heuristics that are error-prone, or exact
(also called combinatorial) methods that are extremely
slow, therefore, applicable only to very small
sequences (i.e., in the order of megabytes). This paper
presents ACME, a combinatorial approach that scales to
gigabyte-long sequences and is the first to support
supermaximal motifs. ACME is a versatile parallel
system that can be deployed on desktop multi-core
systems, or on thousands of CPUs in the cloud. However,
merely using more compute nodes does not guarantee
efficiency, because of the related overheads. To this
end, ACME introduces an automatic tuning mechanism that
suggests the appropriate number of CPUs to utilize, in
order to meet the user constraints in terms of run
time, while minimizing the financial cost of cloud
resources. Our experiments show that, compared to the
state of the art, ACME supports three orders of
magnitude longer sequences (e.g., DNA for the entire
human genome); handles large alphabets (e.g., English
alphabet for Wikipedia); scales out to 16,384 CPUs on a
supercomputer; and supports elastic deployment in the
cloud.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lo:2014:MGD,
author = "Eric Lo and Nick Cheng and Wilfred W. Lin and Wing-Kai
Hon and Byron Choi",
title = "{MyBenchmark}: generating databases for query
workloads",
journal = j-VLDB-J,
volume = "23",
number = "6",
pages = "895--913",
month = dec,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0354-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Nov 24 15:31:08 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "To evaluate the performance of database applications
and database management systems (DBMSs), we usually
execute workloads of queries on generated databases of
different sizes and then benchmark various measures
such as respond time and throughput. This paper
introduces MyBenchmark, a parallel data generation tool
that takes a set of queries as input and generates
database instances. Users of MyBenchmark can control
the characteristics of the generated data as well as
the characteristics of the resulting workload.
Applications of MyBenchmark include DBMS testing,
database application testing, and application-driven
benchmarking. In this paper, we present the
architecture and the implementation algorithms of
MyBenchmark. Experimental results show that MyBenchmark
is able to generate workload-aware databases for a
variety of workloads including query workloads
extracted from TPC-C, TPC-E, TPC-H, and TPC-W
benchmarks.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xie:2014:MEB,
author = "Qing Xie and Chaoyi Pang and Xiaofang Zhou and
Xiangliang Zhang and Ke Deng",
title = "Maximum error-bounded {Piecewise Linear
Representation} for online stream approximation",
journal = j-VLDB-J,
volume = "23",
number = "6",
pages = "915--937",
month = dec,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0355-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Nov 24 15:31:08 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Given a time series data stream, the generation of
error-bounded Piecewise Linear Representation
(error-bounded PLR) is to construct a number of
consecutive line segments to approximate the stream,
such that the approximation error does not exceed a
prescribed error bound. In this work, we consider the
error bound in L_\infty L `? norm as approximation
criterion, which constrains the approximation error on
each corresponding data point, and aim on designing
algorithms to generate the minimal number of segments.
In the literature, the optimal approximation algorithms
are effectively designed based on transformed space
other than time-value space, while desirable optimal
solutions based on original time domain (i.e.,
time-value space) are still lacked. In this article, we
proposed two linear-time algorithms to construct
error-bounded PLR for data stream based on time domain,
which are named OptimalPLR and GreedyPLR, respectively.
The OptimalPLR is an optimal algorithm that generates
minimal number of line segments for the stream
approximation, and the GreedyPLR is an alternative
solution for the requirements of high efficiency and
resource-constrained environment. In order to evaluate
the superiority of OptimalPLR, we theoretically
analyzed and compared OptimalPLR with the state-of-art
optimal solution in transformed space, which also
achieves linear complexity. We successfully proved the
theoretical equivalence between time-value space and
such transformed space, and also discovered the
superiority of OptimalPLR on processing efficiency in
practice. The extensive results of empirical evaluation
support and demonstrate the effectiveness and
efficiency of our proposed algorithms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Alexandrov:2014:SPB,
author = "Alexander Alexandrov and Rico Bergmann and Stephan
Ewen and Johann-Christoph Freytag and Fabian Hueske and
Arvid Heise and Odej Kao and Marcus Leich and Ulf Leser
and Volker Markl and Felix Naumann and Mathias Peters
and Astrid Rheinl{\"a}nder and Matthias J. Sax and
Sebastian Schelter and Mareike H{\"o}ger and Kostas
Tzoumas and Daniel Warneke",
title = "The {Stratosphere} platform for big data analytics",
journal = j-VLDB-J,
volume = "23",
number = "6",
pages = "939--964",
month = dec,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0357-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Nov 24 15:31:08 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We present Stratosphere, an open-source software stack
for parallel data analysis. Stratosphere brings
together a unique set of features that allow the
expressive, easy, and efficient programming of
analytical applications at very large scale.
Stratosphere's features include ``in situ'' data
processing, a declarative query language, treatment of
user-defined functions as first-class citizens,
automatic program parallelization and optimization,
support for iterative programs, and a scalable and
efficient execution engine. Stratosphere covers a
variety of ``Big Data'' use cases, such as data
warehousing, information extraction and integration,
data cleansing, graph analysis, and statistical
analysis applications. In this paper, we present the
overall system architecture design decisions, introduce
Stratosphere through example queries, and then dive
into the internal workings of the system's components
that relate to extensibility, programming model,
optimization, and query execution. We experimentally
compare Stratosphere against popular open-source
alternatives, and we conclude with a research outlook
for the next years.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ward:2014:RTC,
author = "Phillip G. Ward and Zhen He and Rui Zhang and
Jianzhong Qi",
title = "Real-time continuous intersection joins over large
sets of moving objects using graphic processing units",
journal = j-VLDB-J,
volume = "23",
number = "6",
pages = "965--985",
month = dec,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0358-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Nov 24 15:31:08 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The Multiple Time Bucket Join (MTB-join) algorithm is
the state of the art for processing the continuous
intersection join (CI-join) query over moving objects.
It considerably outperforms alternatives, but still
falls short of real-time application performance
requirements for large sets of moving objects. In this
paper, we achieve real-time performance for the CI-join
query over large sets of moving objects by exploiting
the computational power of commodity graphics
processing units (GPUs). We first analyze how the main
characteristics of the MTB-join algorithm make it ill
suited to GPUs and identify key challenges in designing
efficient GPU-based algorithms for the query. We then
address these challenges by developing the
multi-layered grid join (MLG-join) algorithm which has
the following key features: (i) memory locality
friendly indexing, (ii) no dynamic memory allocation,
(iii) in-place object updates, (iv) lock-free
concurrent updates, and (v) massive parallelism. These
features unleash the full potential of the memory
bandwidth and parallel processing of GPUs. Furthermore,
we conduct a theoretical analysis which can predict the
pruning power of the MLG-join algorithm given certain
parameter values used in the algorithm. This allows us
to select optimal parameter values. Through extensive
experimental results, we show that our analysis
accurately models the MLG-join algorithm's sensitivity
to parameter values. The proposed MLG-join algorithm
outperforms the MTB-join algorithm, and a GPU-based
nested-loops join algorithm, by up to two orders of
magnitude, and achieves real-time performance for
CI-join queries on large sets of moving objects.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Binnig:2014:DSI,
author = "Carsten Binnig and Stefan Hildenbrand and Franz
F{\"a}rber and Donald Kossmann and Juchang Lee and
Norman May",
title = "Distributed snapshot isolation: global transactions
pay globally, local transactions pay locally",
journal = j-VLDB-J,
volume = "23",
number = "6",
pages = "987--1011",
month = dec,
year = "2014",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0359-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Nov 24 15:31:08 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Modern database systems employ Snapshot Isolation to
implement concurrency control and isolation because it
promises superior query performance compared to
lock-based alternatives. Furthermore, Snapshot
Isolation never blocks readers, which is an important
property for modern information systems, which have
mixed workloads of heavy OLAP queries and short update
transactions. This paper revisits the problem of
implementing Snapshot Isolation in a distributed
database system and makes three important
contributions. First, a complete definition of
Distributed Snapshot Isolation is given, thereby
extending existing definitions from the literature.
Based on this definition, a set of criteria is proposed
to efficiently implement Snapshot Isolation in a
distributed system. Second, the design space of
alternative methods to implement Distributed Snapshot
Isolation is presented based on this set of criteria.
Third, a new approach to implement Distributed Snapshot
Isolation is devised; we refer to this approach as
Incremental. The results of comprehensive performance
experiments with the TPC-C benchmark show that the
Incremental approach significantly outperforms any
other known method from the literature. Furthermore,
the Incremental approach requires no a priori knowledge
of which nodes of a distributed system are involved in
executing a transaction. Also, the Incremental approach
can execute transactions that involve data from a
single node only with the same efficiency as a
centralized database system. This way, the Incremental
approach takes advantage of sharding or other ways to
improve data locality. The cost for synchronizing
transactions in a distributed system is only paid by
transactions that actually involve data from several
nodes. All these properties make the Incremental
approach more practical than related methods proposed
in the literature.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Vlachos:2015:CMF,
author = "Michail Vlachos and Nikolaos M. Freris and Anastasios
Kyrillidis",
title = "Compressive mining: fast and optimal data mining in
the compressed domain",
journal = j-VLDB-J,
volume = "24",
number = "1",
pages = "1--24",
month = feb,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0360-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 6 15:25:03 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Real-world data typically contain repeated and
periodic patterns. This suggests that they can be
effectively represented and compressed using only a few
coefficients of an appropriate basis (e.g., Fourier and
wavelets). However, distance estimation when the data
are represented using different sets of coefficients is
still a largely unexplored area. This work studies the
optimization problems related to obtaining the tightest
lower/upper bound on Euclidean distances when each data
object is potentially compressed using a different set
of orthonormal coefficients. Our technique leads to
tighter distance estimates, which translates into more
accurate search, learning and mining operations
directly in the compressed domain. We formulate the
problem of estimating lower/upper distance bounds as an
optimization problem. We establish the properties of
optimal solutions and leverage the theoretical analysis
to develop a fast algorithm to obtain an exact solution
to the problem. The suggested solution provides the
tightest estimation of the $ L_2$-norm or the
correlation. We show that typical data analysis
operations, such as $k$-nearest-neighbor search or
$k$-Means clustering, can operate more accurately using
the proposed compression and distance reconstruction
technique. We compare it with many other prevalent
compression and reconstruction techniques, including
random projections and PCA-based techniques. We
highlight a surprising result, namely that when the
data are highly sparse in some basis, our technique may
even outperform PCA-based compression. The
contributions of this work are generic as our
methodology is applicable to any sequential or
high-dimensional data as well as to any orthogonal data
transformation used for the underlying data compression
scheme.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sistla:2015:CNN,
author = "A. Prasad Sistla and Ouri Wolfson and Bo Xu",
title = "Continuous nearest-neighbor queries with location
uncertainty",
journal = j-VLDB-J,
volume = "24",
number = "1",
pages = "25--50",
month = feb,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0361-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 6 15:25:03 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we consider the problem of evaluating
the continuous query of finding the k k nearest objects
with respect to a given point object O_{q} O q among a
set of n n moving point-objects. The query returns a
sequence of answer-pairs, namely pairs of the form $
(I, S) $ such that $I$ is a time interval and $S$ is
the set of objects that are closest to $ O_q$ during
$I$. When there is uncertainty associated with the
locations of the moving objects, $S$ is the set of all
the objects that are possibly the $k$ nearest
neighbors. We analyze the lower bound and the upper
bound on the maximum number of answer-pairs, for the
certain case and the uncertain case, respectively.
Then, we consider two different types of algorithms.
The first is off-line algorithms that compute a priori
all the answer-pairs. The second type is on-line
algorithms that at any time return the current
answer-pair. We present algorithms for the certain case
and the uncertain case, respectively, and analyze their
complexity. We experimentally compare different
algorithms using a database of 1 million objects
derived from real-world GPS traces.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gur:2015:SFA,
author = "Izzeddin G{\"u}r and Mehmet G{\"u}vercin and Hakan
Ferhatosmanoglu",
title = "Scaling forecasting algorithms using clustered
modeling",
journal = j-VLDB-J,
volume = "24",
number = "1",
pages = "51--65",
month = feb,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0363-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 6 15:25:03 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Research on forecasting has traditionally focused on
building more accurate statistical models for a given
time series. The models are mostly applied to limited
data due to efficiency and scalability problems.
However, many enterprise applications require scalable
forecasting on large number of data series. For
example, telecommunication companies need to forecast
each of their customers' traffic load to understand
their usage behavior and to tailor targeted campaigns.
Forecasting models are typically applied on aggregate
data to estimate the total traffic volume for revenue
estimation and resource planning. However, they cannot
be easily applied to each user individually as building
accurate models for large number of users would be time
consuming. The problem is exacerbated when the
forecasting process is continuous and the models need
to be updated periodically. This paper addresses the
problem of building and updating forecasting models
continuously for multiple data series. We propose
dynamic clustered modeling for forecasting by utilizing
representative models as an analogy to cluster centers.
We apply the models to each individual series through
iterative nonlinear optimization. We develop two
approaches: The Integrated Clustered Modeling
integrates clustering and modeling simultaneously, and
the Sequential Clustered Modeling applies them
sequentially. Our findings indicate that modeling an
individual's behavior using its segment can be more
scalable and accurate than the individual model itself.
The grouped models avoid overfits and capture common
motifs even on noisy data. Experimental results from a
telco CRM application show the method is efficient and
scalable, and also more accurate than having separate
individual models.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kaoudi:2015:RCS,
author = "Zoi Kaoudi and Ioana Manolescu",
title = "{RDF} in the clouds: a survey",
journal = j-VLDB-J,
volume = "24",
number = "1",
pages = "67--91",
month = feb,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0364-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 6 15:25:03 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The Resource Description Framework (RDF) pioneered by
the W3C is increasingly being adopted to model data in
a variety of scenarios, in particular data to be
published or exchanged on the Web. Managing large
volumes of RDF data is challenging, due to the sheer
size, the heterogeneity, and the further complexity
brought by RDF reasoning. To tackle the size challenge,
distributed storage architectures are required. Cloud
computing is an emerging paradigm massively adopted in
many applications for the scalability, fault-tolerance,
and elasticity feature it provides, enabling the easy
deployment of distributed and parallel architectures.
In this article, we survey RDF data management
architectures and systems designed for a cloud
environment, and more generally, those large-scale RDF
data management systems that can be easily deployed
therein. We first give the necessary background, then
describe the existing systems and proposals in this
area, and classify them according to dimensions related
to their capabilities and implementation techniques.
The survey ends with a discussion of open problems and
perspectives.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Su:2015:CTD,
author = "Han Su and Kai Zheng and Jiamin Huang and Haozhou Wang
and Xiaofang Zhou",
title = "Calibrating trajectory data for spatio-temporal
similarity analysis",
journal = j-VLDB-J,
volume = "24",
number = "1",
pages = "93--116",
month = feb,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0365-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 6 15:25:03 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Due to the prevalence of GPS-enabled devices and
wireless communications technologies, spatial
trajectories that describe the movement history of
moving objects are being generated and accumulated at
an unprecedented pace. Trajectory data in a database
are intrinsically heterogeneous, as they represent
discrete approximations of original continuous paths
derived using different sampling strategies and
different sampling rates. Such heterogeneity can have a
negative impact on the effectiveness of trajectory
similarity measures, which are the basis of many
crucial trajectory processing tasks. In this paper, we
pioneer a systematic approach to trajectory calibration
that is a process to transform a heterogeneous
trajectory dataset to one with (almost) unified
sampling strategies. Specifically, we propose an
anchor-based calibration system that aligns
trajectories to a set of anchor points, which are fixed
locations independent of trajectory data. After
examining four different types of anchor points for the
purpose of building a stable reference system, we
propose a spatial-only geometry-based calibration
approach that considers the spatial relationship
between anchor points and trajectories. Then a more
advanced spatial-only model-based calibration method is
presented, which exploits the power of machine learning
techniques to train inference models from historical
trajectory data to improve calibration effectiveness.
Afterward, since trajectory has temporal information,
we extend these two spatial-only trajectory calibration
algorithms to incorporate the temporal information,
which can infer a proper time stamp to each anchor
point of a calibrated trajectory. At last, we provide a
solution to reduce cost, i.e., the number of
trajectories that is necessary to be re-calibrated, of
the updating of the reference system. Finally, we
conduct extensive experiments using real trajectory
datasets to demonstrate the effectiveness and
efficiency of the proposed calibration system.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2015:CAI,
author = "Hui Li and Sourav S. Bhowmick and Aixin Sun and
Jiangtao Cui",
title = "Conformity-aware influence maximization in online
social networks",
journal = j-VLDB-J,
volume = "24",
number = "1",
pages = "117--141",
month = feb,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0366-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 6 15:25:03 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Influence maximization (im) is the problem of finding
a small subset of nodes (seed nodes) in a social
network that could maximize the spread of influence.
Despite the progress achieved by state-of-the-art
greedy im techniques, they suffer from two key
limitations. Firstly, they are inefficient as they can
take days to find seeds in very large real-world
networks. Secondly, although extensive research in
social psychology suggests that humans will readily
conform to the wishes or beliefs of others,
surprisingly, existing im techniques are
conformity-unaware. That is, they only utilize an
individual's ability to influence another but ignores
conformity (a person's inclination to be influenced) of
the individuals. In this paper, we propose a novel
conformity-aware cascade ($ C^2$) model which leverages
on the interplay between influence and conformity in
obtaining the influence probabilities of nodes from
underlying data for estimating influence spreads. We
also propose a variant of this model called $ C^3$
model that supports context-specific influence and
conformity of nodes. A salient feature of these models
is that they are aligned to the popular social forces
principle in social psychology. Based on these models,
we propose a novel greedy algorithm called cinema that
generates high-quality seed set for the im problem. It
first partitions, the network into a set of
non-overlapping subnetworks and for each of these
subnetworks it computes the influence and conformity
indices of nodes by analyzing the sentiments expressed
by individuals. Each subnetwork is then associated with
a cog-sublist which stores the marginal gains of the
nodes in the subnetwork in descending order. The node
with maximum marginal gain in each cog-sublist is
stored in a data structure called mag-list. These
structures are manipulated by cinema to efficiently
find the seed set. A key feature of such
partitioning-based strategy is that each node's
influence computation and updates can be limited to the
subnetwork it resides instead of the entire network.
This paves way for seamless adoption of cinema on a
distributed platform. Our empirical study with
real-world social networks comprising of millions of
nodes demonstrates that cinema as well as its
context-aware and distributed variants generate
superior quality seed set compared to state-of-the-art
im approaches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Deng:2015:UFA,
author = "Dong Deng and Guoliang Li and Jianhua Feng and Yi Duan
and Zhiguo Gong",
title = "A unified framework for approximate dictionary-based
entity extraction",
journal = j-VLDB-J,
volume = "24",
number = "1",
pages = "143--167",
month = feb,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0367-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 6 15:25:03 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Dictionary-based entity extraction identifies
predefined entities (e.g., person names or locations)
from documents. A recent trend for improving extraction
recall is to support approximate entity extraction,
which finds all substrings from documents that
approximately match entities in a given dictionary.
Existing methods to address this problem support either
token-based similarity (e.g., Jaccard Similarity) or
character-based dissimilarity (e.g., Edit Distance). It
calls for a unified method to support various
similarity/dissimilarity functions, since a unified
method can reduce the programming efforts, the hardware
requirements, and the manpower. In this paper, we
propose a unified framework to support various
similarity/dissimilarity functions, such as jaccard
similarity, cosine similarity, dice similarity, edit
similarity, and edit distance. Since many real-world
applications have high-performance requirement for
approximate entity extraction on data streams (e.g.,
Twitter), we focus on devising efficient algorithms to
achieve high performance. We find that many substrings
in documents have overlaps, and we can utilize the
shared computation across the overlaps to avoid
unnecessary redundant computation. To this end, we
propose efficient filtering algorithms and develop
effective pruning techniques. Experimental results show
our method achieves high performance and outperforms
state-of-the-art studies significantly.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hung:2015:CAC,
author = "Chih-Chieh Hung and Wen-Chih Peng and Wang-Chien Lee",
title = "Clustering and aggregating clues of trajectories for
mining trajectory patterns and routes",
journal = j-VLDB-J,
volume = "24",
number = "2",
pages = "169--192",
month = apr,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-011-0262-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Mar 18 19:14:35 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we propose a new trajectory pattern
mining framework, namely Clustering and Aggregating
Clues of Trajectories (CACT), for discovering
trajectory routes that represent the frequent movement
behaviors of a user. In addition to spatial and
temporal biases, we observe that trajectories contain
silent durations, i.e., the time durations when no data
points are available to describe the movements of
users, which bring many challenging issues to
trajectory pattern mining. We claim that a movement
behavior would leave some clues in its various
sampled/observed trajectories. These clues may be
extracted from spatially and temporally co-located data
points from the observed trajectories. Based on this
observation, we propose clue-aware trajectory
similarity to measure the clues between two
trajectories. Accordingly, we further propose the
clue-aware trajectory clustering algorithm to cluster
similar trajectories into groups to capture the
movement behaviors of the user. Finally, we devise the
clue-aware trajectory aggregation algorithm to
aggregate trajectories in the same group to derive the
corresponding trajectory pattern and route. We validate
our ideas and evaluate the proposed CACT framework by
experiments using both synthetic and real datasets. The
experimental results show that CACT is more effective
in discovering trajectory patterns than the
state-of-the-art techniques for mining trajectory
patterns.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Eichinger:2015:TSC,
author = "Frank Eichinger and Pavel Efros and Stamatis
Karnouskos and Klemens B{\"o}hm",
title = "A time-series compression technique and its
application to the smart grid",
journal = j-VLDB-J,
volume = "24",
number = "2",
pages = "193--218",
month = apr,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0368-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Mar 18 19:14:35 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Time-series data is increasingly collected in many
domains. One example is the smart electricity
infrastructure, which generates huge volumes of such
data from sources such as smart electricity meters.
Although today these data are used for visualization
and billing in mostly 15-min resolution, its original
temporal resolution frequently is more fine-grained,
e.g., seconds. This is useful for various analytical
applications such as short-term forecasting,
disaggregation and visualization. However, transmitting
and storing huge amounts of such fine-grained data are
prohibitively expensive in terms of storage space in
many cases. In this article, we present a compression
technique based on piecewise regression and two methods
which describe the performance of the compression.
Although our technique is a general approach for
time-series compression, smart grids serve as our
running example and as our evaluation scenario.
Depending on the data and the use-case scenario, the
technique compresses data by ratios of up to factor
5,000 while maintaining its usefulness for analytics.
The proposed technique has outperformed related work
and has been applied to three real-world energy
datasets in different scenarios. Finally, we show that
the proposed compression technique can be implemented
in a state-of-the-art database management system.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xue:2015:SDS,
author = "Andy Yuan Xue and Jianzhong Qi and Xing Xie and Rui
Zhang and Jin Huang and Yuan Li",
title = "Solving the data sparsity problem in destination
prediction",
journal = j-VLDB-J,
volume = "24",
number = "2",
pages = "219--243",
month = apr,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0369-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Mar 18 19:14:35 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Destination prediction is an essential task for many
emerging location-based applications such as
recommending sightseeing places and targeted
advertising according to destinations. A common
approach to destination prediction is to derive the
probability of a location being the destination based
on historical trajectories. However, almost all the
existing techniques use various kinds of extra
information such as road network, proprietary travel
planner, statistics requested from government, and
personal driving habits. Such extra information, in
most circumstances, is unavailable or very costly to
obtain. Thereby we approach the task of destination
prediction by using only historical trajectory dataset.
However, this approach encounters the ``data sparsity
problem'', i.e., the available historical trajectories
are far from enough to cover all possible query
trajectories, which considerably limits the number of
query trajectories that can obtain predicted
destinations. We propose a novel method named
Sub-Trajectory Synthesis (SubSyn) to address the data
sparsity problem. SubSyn first decomposes historical
trajectories into sub-trajectories comprising two
adjacent locations, and then connects the
sub-trajectories into ``synthesised'' trajectories.
This process effectively expands the historical
trajectory dataset to contain much more trajectories.
Experiments based on real datasets show that SubSyn can
predict destinations for up to ten times more query
trajectories than a baseline prediction algorithm.
Furthermore, the running time of the SubSyn-training
algorithm is almost negligible for a large set of 1.9
million trajectories, and the SubSyn-prediction
algorithm runs over two orders of magnitude faster than
the baseline prediction algorithm constantly.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2015:ECS,
author = "Zhiwei Zhang and Jeffrey Xu Yu and Lu Qin and Lijun
Chang and Xuemin Lin",
title = "{I/O} efficient: computing {SCCs} in massive graphs",
journal = j-VLDB-J,
volume = "24",
number = "2",
pages = "245--270",
month = apr,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0372-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Mar 18 19:14:35 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A strongly connected component ($ \mathsf {SCC}$) is a
maximal subgraph of a directed graph GG in which every
pair of nodes is reachable from each other in the $
\mathsf {SCC}$. With such a property, a general
directed graph can be represented by a directed acyclic
graph (DAG) by contracting every $ \mathsf {SCC}$ of GG
to a node in DAG. In many real applications that need
graph pattern matching, topological sorting, or
reachability query processing, the best way to deal
with a general directed graph is to deal with its DAG
representation. Therefore, finding all \mathsf
{SCC}SCCs in a directed graph GG is a critical
operation. The existing in-memory algorithms based on
depth first search (DFS) can find all $ \mathsf {SCC}$
s in linear time with respect to the size of a graph.
However, when a graph cannot reside entirely in the
main memory, the existing external or semi-external
algorithms to find all $ \mathsf {SCC}$ s have
limitation to achieve high I/O efficiency. In this
paper, we study new I/O-efficient semi-external
algorithms to find all $ \mathsf {SCC}$ s for a massive
directed graph GG that cannot reside in main memory
entirely. To overcome the deficiency of the existing
DFS-based semi-external algorithm that heavily relies
on a total order, we explore a weak order based on
which we investigate new algorithms. We propose a new
two-phase algorithm, namely, tree construction and tree
search. In the tree construction phase, a spanning tree
of GG can be constructed in bounded number of
sequential scans of GG. In the tree search phase, it
needs to sequentially scan the graph once to find all $
\mathsf {SCC}$ s. In addition, we propose a new
single-phase algorithm, which combines the tree
construction and tree search phases into a single
phase, with three new optimization techniques. They are
early acceptance, early rejection, and batch
processing. By the single-phase algorithm with the new
optimization techniques, we can significantly reduce
the number of I/Os and the CPU cost. We prove the
correctness of the algorithms. We conduct extensive
experimental studies using 4 real datasets including a
massive real dataset and several synthetic datasets to
confirm the I/O efficiency of our approaches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yuan:2015:GSS,
author = "Ye Yuan and Guoren Wang and Lei Chen and Haixun Wang",
title = "Graph similarity search on large uncertain graph
databases",
journal = j-VLDB-J,
volume = "24",
number = "2",
pages = "271--296",
month = apr,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0373-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Mar 18 19:14:35 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Many studies have been conducted on seeking an
efficient solution for graph similarity search over
certain (deterministic) graphs due to its wide
application in many fields, including bioinformatics,
social network analysis, and Resource Description
Framework data management. All prior work assumes that
the underlying data is deterministic. However, in
reality, graphs are often noisy and uncertain due to
various factors, such as errors in data extraction,
inconsistencies in data integration, and for
privacy-preserving purposes. Therefore, in this paper,
we study similarity graph containment search on large
uncertain graph databases. Similarity graph containment
search consists of subgraph similarity search and
supergraph similarity search. Different from previous
works assuming that edges in an uncertain graph are
independent of each other, we study uncertain graphs
where edges' occurrences are correlated. We formally
prove that subgraph or supergraph similarity search
over uncertain graphs is \#P-hard; thus, we employ a
filter-and-verify framework to speed up these two
queries. For the subgraph similarity query, in the
filtering phase, we develop tight lower and upper
bounds of subgraph similarity probability based on a
probabilistic matrix index (PMI). PMI is composed of
discriminative subgraph features associated with tight
lower and upper bounds of subgraph isomorphism
probability. Based on PMI, we can filter out a large
number of uncertain graphs and maximize the pruning
capability. During the verification phase, we develop
an efficient sampling algorithm to validate the
remaining candidates. For the supergraph similarity
query, in the filtering phase, we propose two pruning
algorithms, one lightweight and the other strong, based
on maximal common subgraphs of query graph and data
graph. We run the two pruning algorithms against a
probabilistic index that consists of powerful graph
features. In the verification, we design an approximate
algorithm based on the Horvitz---Thompson estimator to
fast validate the remaining candidates. The
efficiencies of our proposed solutions to the subgraph
and supergraph similarity search have been verified
through extensive experiments on real uncertain graph
datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yang:2015:TPC,
author = "Bin Yang and Chenjuan Guo and Yu Ma and Christian S.
Jensen",
title = "Toward personalized, context-aware routing",
journal = j-VLDB-J,
volume = "24",
number = "2",
pages = "297--318",
month = apr,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0378-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Mar 18 19:14:35 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A driver's choice of a route to a destination may
depend on the route's length and travel time, but a
multitude of other, possibly hard-to-formalize aspects,
may also factor into the driver's decision. There is
evidence that a driver's choice of route is context
dependent, e.g., varies across time, and that route
choice also varies from driver to driver. In contrast,
conventional routing services support little in the way
of context dependence, and they deliver the same routes
to all drivers. We study how to identify context-aware
driving preferences for individual drivers from
historical trajectories, and thus how to provide
foundations for personalized navigation, but also
professional driver education and traffic planning. We
provide techniques that are able to capture
time-dependent and uncertain properties of dynamic
travel costs, such as travel time and fuel consumption,
from trajectories, and we provide techniques capable of
capturing the driving behaviors of different drivers in
terms of multiple dynamic travel costs. Further, we
propose techniques that are able to identify a driver's
contexts and then to identify driving preferences for
each context using historical trajectories from the
driver. Empirical studies with a large trajectory data
set offer insight into the design properties of the
proposed techniques and suggest that they are
effective.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Huang:2015:TKS,
author = "Xin Huang and Hong Cheng and Rong-Hua Li and Lu Qin
and Jeffrey Xu Yu",
title = "Top-{$K$} structural diversity search in large
networks",
journal = j-VLDB-J,
volume = "24",
number = "3",
pages = "319--343",
month = jun,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0379-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 15 17:21:03 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Social contagion depicts a process of information
(e.g., fads, opinions, news) diffusion in the online
social networks. A recent study reports that in a
social contagion process, the probability of contagion
is tightly controlled by the number of connected
components in an individual's neighborhood. Such a
number is termed structural diversity of an individual,
and it is shown to be a key predictor in the social
contagion process. Based on this, a fundamental issue
in a social network is to find top-kk users with the
highest structural diversities. In this paper, we, for
the first time, study the top-kk structural diversity
search problem in a large network. Specifically, we
study two types of structural diversity measures,
namely, component-based structural diversity measure
and core-based structural diversity measure. For
component-based structural diversity, we develop an
effective upper bound of structural diversity for
pruning the search space. The upper bound can be
incrementally refined in the search process. Based on
such upper bound, we propose an efficient framework for
top-kk structural diversity search. To further speed up
the structural diversity evaluation in the search
process, several carefully devised search strategies
are proposed. We also design efficient techniques to
handle frequent updates in dynamic networks and
maintain the top-kk results. We further show how the
techniques proposed in component-based structural
diversity measure can be extended to handle the
core-based structural diversity measure. Extensive
experimental studies are conducted in real-world large
networks and synthetic graphs, and the results
demonstrate the efficiency and effectiveness of the
proposed methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Papapetrou:2015:SDS,
author = "Odysseas Papapetrou and Minos Garofalakis and Antonios
Deligiannakis",
title = "Sketching distributed sliding-window data streams",
journal = j-VLDB-J,
volume = "24",
number = "3",
pages = "345--368",
month = jun,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0380-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 15 17:21:03 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "While traditional data management systems focus on
evaluating single, ad hoc queries over static data sets
in a centralized setting, several emerging applications
require (possibly, continuous) answers to queries on
dynamic data that is widely distributed and constantly
updated. Furthermore, such query answers often need to
discount data that is ``stale'' and operate solely on a
sliding window of recent data arrivals (e.g., data
updates occurring over the last 24 h). Such distributed
data streaming applications mandate novel algorithmic
solutions that are both time and space efficient (to
manage high-speed data streams) and also communication
efficient (to deal with physical data distribution). In
this paper, we consider the problem of complex query
answering over distributed, high-dimensional data
streams in the sliding-window model. We introduce a
novel sketching technique (termed ECM-sketch) that
allows effective summarization of streaming data over
both time-based and count-based sliding windows with
probabilistic accuracy guarantees. Our sketch structure
enables point, as well as inner product, queries and
can be employed to address a broad range of problems,
such as maintaining frequency statistics, finding heavy
hitters, and computing quantiles in the sliding-window
model. Focusing on distributed environments, we
demonstrate how ECM-sketches of individual, local
streams can be composed to generate a (low-error)
ECM-sketch summary of the order-preserving merging of
all streams; furthermore, we show how ECM-sketches can
be exploited for continuous monitoring of
sliding-window queries over distributed streams. Our
extensive experimental study with two real-life data
sets validates our theoretical claims and verifies the
effectiveness of our techniques. To the best of our
knowledge, ours is the first work to address efficient,
guaranteed-error complex query answering over
distributed data streams in the sliding-window model.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yuan:2015:EDS,
author = "Ye Yuan and Guoren Wang and Jeffery Yu Xu and Lei
Chen",
title = "Efficient distributed subgraph similarity matching",
journal = j-VLDB-J,
volume = "24",
number = "3",
pages = "369--394",
month = jun,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0381-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 15 17:21:03 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Given a query graph qq and a data graph GG, subgraph
similarity matching is to retrieve all matches of qq in
GG with the number of missing edges bounded by a given
threshold $ \epsilon \in ? $. Many works have been
conducted to study the problem of subgraph similarity
matching due to its ability to handle applications
involved with noisy or erroneous graph data. In
practice, a data graph can be extremely large, e.g., a
web-scale graph containing hundreds of millions of
vertices and billions of edges. The state-of-the-art
approaches employ centralized algorithms to process the
subgraph similarity queries, and thus, they are
infeasible for such a large graph due to the limited
computational power and storage space of a centralized
server. To address this problem, in this paper, we
investigate subgraph similarity matching for a
web-scale graph deployed in a distributed environment.
We propose distributed algorithms and optimization
techniques that exploit the properties of subgraph
similarity matching, so that we can well utilize the
parallel computing power and lower the communication
cost among the distributed data centers for query
processing. Specifically, we first relax and decompose
qq into a minimum number of sub-queries. Next, we send
each sub-query to conduct the exact matching in
parallel. Finally, we schedule and join the exact
matches to obtain final query answers. Moreover, our
workload-balance strategy further speeds up the query
processing. Our experimental results demonstrate the
feasibility of our proposed approach in performing
subgraph similarity matching over web-scale graph
data.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mirylenka:2015:CHH,
author = "Katsiaryna Mirylenka and Graham Cormode and Themis
Palpanas and Divesh Srivastava",
title = "Conditional heavy hitters: detecting interesting
correlations in data streams",
journal = j-VLDB-J,
volume = "24",
number = "3",
pages = "395--414",
month = jun,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0382-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 15 17:21:03 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The notion of heavy hitters--items that make up a
large fraction of the population--has been successfully
used in a variety of applications across sensor and
RFID monitoring, network data analysis, event mining,
and more. Yet this notion often fails to capture the
semantics we desire when we observe data in the form of
correlated pairs. Here, we are interested in items that
are conditionally frequent: when a particular item is
frequent within the context of its parent item. In this
work, we introduce and formalize the notion of
conditional heavy hitters to identify such items, with
applications in network monitoring and Markov chain
modeling. We explore the relationship between
conditional heavy hitters and other related notions in
the literature, and show analytically and
experimentally the usefulness of our approach. We
introduce several algorithm variations that allow us to
efficiently find conditional heavy hitters for input
data with very different characteristics, and provide
analytical results for their performance. Finally, we
perform experimental evaluations with several synthetic
and real datasets to demonstrate the efficacy of our
methods and to study the behavior of the proposed
algorithms for different types of data.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gao:2015:ECP,
author = "Yunjun Gao and Lu Chen and Xinhan Li and Bin Yao and
Gang Chen",
title = "Efficient $ k k$-closest pair queries in general
metric spaces",
journal = j-VLDB-J,
volume = "24",
number = "3",
pages = "415--439",
month = jun,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0383-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 15 17:21:03 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Given two object sets PP and QQ, a k-closest
pair(k\hbox {CP})(kCP)query finds kk closest object
pairs from P\times QP$ \times $Q. This operation is
common in many real-life applications such as GIS, data
mining, and recommender systems. Although it has
received much attention in the Euclidean space, there
is little prior work on the metric space. In this
paper, we study the problem of kCP query processing in
general metric spaces, namely Metric kCP(\hbox
{M}k\hbox {CP})(MkCP)search, and propose several
efficient algorithms using dynamic disk-based metric
indexes (e.g., M-tree), which can be applied to
arbitrary type of data as long as a certain metric
distance is defined and satisfies the triangle
inequality. Our approaches follow depth-first and/or
best-first traversal paradigm(s), employ effective
pruning rules based on metric space properties and the
counting information preserved in the metric index,
take advantage of aggressive pruning and compensation
to further boost query efficiency, and derive a
node-based cost model for \hbox {M}k\hbox {CP}MkCP
retrieval. In addition, we extend our techniques to
tackle two interesting variants of \hbox {M}k\hbox
{CP}MkCP queries. Extensive experiments with both real
and synthetic data sets demonstrate the performance of
our proposed algorithms, the effectiveness of our
developed pruning rules, and the accuracy of our
presented cost model.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Aksoy:2015:RPE,
author = "Cem Aksoy and Aggeliki Dimitriou and Dimitri
Theodoratos",
title = "Reasoning with patterns to effectively answer {XML}
keyword queries",
journal = j-VLDB-J,
volume = "24",
number = "3",
pages = "441--465",
month = jun,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0384-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 15 17:21:03 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Keyword search is a popular technique for searching
tree-structured data on the Web because it frees the
user from knowing a complex query language and the
structure of the data sources. However, the imprecision
of the keyword queries usually results in a very large
number of results of which only a few are relevant to
the query. Multiple previous approaches have tried to
address this problem. They exploit the structural
properties of the tree data in order to filter out
irrelevant results. This is not an easy task though,
and in the general case, these approaches show low
precision and/or recall and low quality of result
ranking. In this paper, we argue that exploiting the
structural relationships of the query matches locally
in the data tree is not sufficient and a global
analysis of the keyword matches in the data tree is
necessary in order to assign meaningful semantics to
keyword queries. We present an original approach for
answering keyword queries which extracts structural
patterns of the query matches and reasons with them in
order to return meaningful results ranked with respect
to their relevance to the query. Comparisons between
patterns are realized based on different types of
homomorphisms between patterns. As the number of
patterns is typically much smaller than that of the of
query matches, this global reasoning is feasible. We
design an efficient stack-based algorithm for
evaluating keyword queries on tree-structured data, and
we also devise a heuristic extension which further
improves its performance. We run comprehensive
experiments on different datasets to evaluate the
efficiency of the algorithms and the effectiveness of
our ranking and filtering semantics. The experimental
results show that our approach produces results of
higher quality compared to previous ones and our
algorithms are fast and scale well with respect to the
input and output size.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Roy:2015:TAO,
author = "Senjuti Basu Roy and Ioanna Lykourentzou and Saravanan
Thirumuruganathan and Sihem Amer-Yahia and Gautam Das",
title = "Task assignment optimization in knowledge-intensive
crowdsourcing",
journal = j-VLDB-J,
volume = "24",
number = "4",
pages = "467--491",
month = aug,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0385-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Aug 8 13:52:45 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We present SmartCrowd, a framework for optimizing task
assignment in knowledge-intensive crowdsourcing (KI-C).
SmartCrowd distinguishes itself by formulating, for the
first time, the problem of worker-to-task assignment in
KI-C as an optimization problem, by proposing efficient
adaptive algorithms to solve it and by accounting for
human factors, such as worker expertise, wage
requirements, and availability inside the optimization
process. We present rigorous theoretical analyses of
the task assignment optimization problem and propose
optimal and approximation algorithms with guarantees,
which rely on index pre-computation and adaptive
maintenance. We perform extensive performance and
quality experiments using real and synthetic data to
demonstrate that the SmartCrowd approach is necessary
to achieve efficient task assignments of high-quality
under guaranteed cost budget.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bao:2015:GFR,
author = "Zhifeng Bao and Yong Zeng and Tok Wang Ling and
Dongxiang Zhang and Guoliang Li and H. V. Jagadish",
title = "A general framework to resolve the {MisMatch} problem
in {XML} keyword search",
journal = j-VLDB-J,
volume = "24",
number = "4",
pages = "493--518",
month = aug,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0386-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Aug 8 13:52:45 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "When users issue a query to a database, they have
expectations about the results. If what they search for
is unavailable in the database, the system will return
an empty result or, worse, erroneous mismatch results.
We call this problem the MisMatch problem. In this
paper, we solve the MisMatch problem in the context of
XML keyword search. Our solution is based on two novel
concepts that we introduce: target node type and
Distinguishability. Target Node Type represents the
type of node a query result intends to match, and
Distinguishability is used to measure the importance of
the query keywords. Using these concepts, we develop a
low-cost post-processing algorithm on the results of
query evaluation to detect the MisMatch problem and
generate helpful suggestions to users. Our approach has
three noteworthy features: (1) for queries with the
MisMatch problem, it generates the explanation,
suggested queries and their sample results as the
output to users, helping users judge whether the
MisMatch problem is solved without reading all query
results; (2) it is portable as it can work with any
lowest common ancestor-based matching semantics (for
XML data without ID references) or minimal Steiner
tree-based matching semantics (for XML data with ID
references) which return tree structures as results. It
is orthogonal to the choice of result retrieval method
adopted; (3) it is lightweight in the way that it
occupies a very small proportion of the whole query
evaluation time. Extensive experiments on three real
datasets verify the effectiveness, efficiency and
scalability of our approach. A search engine called
XClear has been built and is available at
http://xclear.comp.nus.edu.sg.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kotsifakos:2015:EBS,
author = "Alexios Kotsifakos and Isak Karlsson and Panagiotis
Papapetrou and Vassilis Athitsos and Dimitrios
Gunopulos",
title = "Embedding-based subsequence matching with gaps ---
range --- tolerances: a {Query-By-Humming}
application",
journal = j-VLDB-J,
volume = "24",
number = "4",
pages = "519--536",
month = aug,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0387-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Aug 8 13:52:45 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We present a subsequence matching framework that
allows for gaps in both query and target sequences,
employs variable matching tolerance efficiently tuned
for each query and target sequence, and constrains the
maximum matching range. Using this framework, a dynamic
programming method is proposed, called SMBGT, that,
given a short query sequence Q and a large database,
identifies in quadratic time the subsequence of the
database that best matches Q. SMBGT is highly
applicable to music retrieval. However, in
Query-By-Humming applications, runtime is critical.
Hence, we propose a novel embedding-based approach,
called ISMBGT, for speeding up search under SMBGT.
Using a set of reference sequences, ISMBGT maps both Q
and each position of each database sequence into
vectors. The database vectors closest to the query
vector are identified, and SMBGT is then applied
between Q and the subsequences that correspond to those
database vectors. The key novelties of ISMBGT are that
it does not require training, it is query sensitive,
and it exploits the flexibility of SMBGT. We present an
extensive experimental evaluation using synthetic and
hummed queries on a large music database. Our findings
show that ISMBGT can achieve speedups of up to an order
of magnitude against brute-force search and over an
order of magnitude against cDTW, while maintaining a
retrieval accuracy very close to that of brute-force
search.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Skovsgaard:2015:FTR,
author = "Anders Skovsgaard and Christian S. Jensen",
title = "Finding top-$k$ relevant groups of spatial web
objects",
journal = j-VLDB-J,
volume = "24",
number = "4",
pages = "537--555",
month = aug,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0388-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Aug 8 13:52:45 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The web is increasingly being accessed from
geo-positioned devices such as smartphones, and rapidly
increasing volumes of web content are geo-tagged. In
addition, studies show that a substantial fraction of
all web queries has local intent. This development
motivates the study of advanced spatial keyword-based
querying of web content. Previous research has
primarily focused on the retrieval of the top-k
individual spatial web objects that best satisfy a
query specifying a location and a set of keywords. This
paper proposes a new type of query functionality that
returns top-k groups of objects while taking into
account aspects such as group density, distance to the
query, and relevance to the query keywords. To enable
efficient processing, novel indexing and query
processing techniques for single and multiple keyword
queries are proposed. Empirical performance studies
with an implementation of the techniques and real data
suggest that the proposals are viable in practical
settings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Abedjan:2015:PRD,
author = "Ziawasch Abedjan and Lukasz Golab and Felix Naumann",
title = "Profiling relational data: a survey",
journal = j-VLDB-J,
volume = "24",
number = "4",
pages = "557--581",
month = aug,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0389-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Aug 8 13:52:45 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Profiling data to determine metadata about a given
dataset is an important and frequent activity of any IT
professional and researcher and is necessary for
various use-cases. It encompasses a vast array of
methods to examine datasets and produce metadata. Among
the simpler results are statistics, such as the number
of null values and distinct values in a column, its
data type, or the most frequent patterns of its data
values. Metadata that are more difficult to compute
involve multiple columns, namely correlations, unique
column combinations, functional dependencies, and
inclusion dependencies. Further techniques detect
conditional properties of the dataset at hand. This
survey provides a classification of data profiling
tasks and comprehensively reviews the state of the art
for each class. In addition, we review data profiling
tools and systems from research and industry. We
conclude with an outlook on the future of data
profiling beyond traditional profiling tasks and beyond
relational databases.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Deutch:2015:PBA,
author = "Daniel Deutch and Yuval Moskovitch and Val Tannen",
title = "Provenance-based analysis of data-centric processes",
journal = j-VLDB-J,
volume = "24",
number = "4",
pages = "583--607",
month = aug,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0390-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Aug 8 13:52:45 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We consider in this paper static analysis of the
possible executions of data-dependent applications,
namely applications whose control flow is guided by a
finite-state machine, as well as by the state of an
underlying database. We note that previous work in this
context has not addressed two important features of
such analysis, namely analysis under hypothetical
scenarios, such as changes to the application's state
machine and/or to the underlying database, and the
consideration of meta-data, such as cost or access
privileges. Observing that semiring-based provenance
has been proven highly effective in supporting these
two features for database queries, we develop in this
paper a semiring-based provenance framework for the
analysis of data-dependent processes, accounting for
hypothetical reasoning and meta-data. The development
addresses two interacting new challenges: (1) combining
provenance annotations for both information that
resides in the database and information about external
inputs (e.g., user choices) and (2) finitely capturing
infinitely many process executions. We have implemented
our framework as part of the PROPOLIS system.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bohlen:2015:SIB,
author = "Michael H. B{\"o}hlen and Christoph Koch",
title = "Special issue on best papers of {VLDB 2013}",
journal = j-VLDB-J,
volume = "24",
number = "5",
pages = "609--610",
month = oct,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0401-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Sep 18 06:51:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yan:2015:ALK,
author = "Zhepeng Yan and Nan Zheng and Zachary G. Ives and
Partha Pratim Talukdar and Cong Yu",
title = "Active learning in keyword search-based data
integration",
journal = j-VLDB-J,
volume = "24",
number = "5",
pages = "611--631",
month = oct,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0374-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Sep 18 06:51:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The problem of scaling up data integration, such that
new sources can be quickly utilized as they are
discovered, remains elusive: Global schemas for
integrated data are difficult to develop and expand,
and schema and record matching techniques are limited
by the fact that data and metadata are often
under-specified and must be disambiguated by data
experts. One promising approach is to avoid using a
global schema, and instead to develop keyword
search-based data integration--where the system lazily
discovers associations enabling it to join together
matches to keywords, and return ranked results. The
user is expected to understand the data domain and
provide feedback about answers' quality. The system
generalizes such feedback to learn how to correctly
integrate data. A major open challenge is that under
this model, the user only sees and offers feedback on a
few ``top-kk'' results: This result set must be
carefully selected to include answers of high relevance
and answers that are highly informative when feedback
is given on them. Existing systems merely focus on
predicting relevance, by composing the scores of
various schema and record matching algorithms. In this
paper, we show how to predict the uncertainty
associated with a query result's score, as well as how
informative feedback is on a given result. We build
upon these foundations to develop an active learning
approach to keyword search-based data integration, and
we validate the effectiveness of our solution over real
data from several very different domains.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zou:2015:CDA,
author = "Tao Zou and Ronan Bras and Marcos Vaz Salles and Alan
Demers and Johannes Gehrke",
title = "{ClouDiA}: a deployment advisor for public clouds",
journal = j-VLDB-J,
volume = "24",
number = "5",
pages = "633--653",
month = oct,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0375-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Sep 18 06:51:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "An increasing number of distributed data-driven
applications are moving into shared public clouds. By
sharing resources and operating at scale, public clouds
promise higher utilization and lower costs than private
clusters. To achieve high utilization, however, cloud
providers inevitably allocate virtual machine instances
non-contiguously; i.e., instances of a given
application may end-up in physically distant machines
in the cloud. This allocation strategy can lead to
large differences in average latency between instances.
For a large class of applications, this difference can
result in significant performance degradation, unless
care is taken in how application components are mapped
to instances. In this paper, we propose ClouDiA, a
general deployment advisor that selects application
node deployments minimizing either (i) the largest
latency between application nodes, or (ii) the longest
critical path among all application nodes. ClouDiA
employs a number of algorithmic techniques, including
mixed-integer programming and constraint programming
techniques, to efficiently search the space of possible
mappings of application nodes to instances. Through
experiments with synthetic and real applications in
Amazon EC2, we show that mean latency is a robust
metric to model communication cost in these
applications and that our search techniques yield a
15---55 \% reduction in time-to-solution or service
response time, without any need for modifying
application code.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhu:2015:SAP,
author = "Fanwei Zhu and Yuan Fang and Kevin Chen-Chuan Chang
and Jing Ying",
title = "Scheduled approximation for {Personalized PageRank}
with {Utility-based Hub Selection}",
journal = j-VLDB-J,
volume = "24",
number = "5",
pages = "655--679",
month = oct,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0376-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Sep 18 06:51:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "As Personalized PageRank has been widely leveraged for
ranking on a graph, the efficient computation of
Personalized PageRank Vector (PPV) becomes a prominent
issue. In this paper, we propose FastPPV, an
approximate PPV computation algorithm that is
incremental and accuracy-aware. Our approach hinges on
a novel paradigm of scheduled approximation: the
computation is partitioned and scheduled for processing
in an ``organized'' way, such that we can gradually
improve our PPV estimation in an incremental manner and
quantify the accuracy of our approximation at query
time. Guided by this principle, we develop an efficient
hub-based realization, where we adopt the metric of hub
length to partition and schedule random walk tours so
that the approximation error reduces exponentially over
iterations. In addition, as tours are segmented by
hubs, the shared substructures between different tours
(around the same hub) can be reused to speed up query
processing both within and across iterations. Given the
key roles played by the hubs, we further investigate
the problem of hub selection. In particular, we develop
a conceptual model to select hubs based on the two
desirable properties of hubs--sharing and
discriminating, and present several different
strategies to realize the conceptual model. Finally, we
evaluate FastPPV over two real-world graphs, and show
that it not only significantly outperforms two
state-of-the-art baselines in both online and offline
phrases, but also scales well on larger graphs. In
particular, we are able to achieve near-constant time
online query processing irrespective of graph size.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ren:2015:VLM,
author = "Kun Ren and Alexander Thomson and Daniel J. Abadi",
title = "{VLL}: a lock manager redesign for main memory
database systems",
journal = j-VLDB-J,
volume = "24",
number = "5",
pages = "681--705",
month = oct,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0377-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Sep 18 06:51:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Lock managers are increasingly becoming a bottleneck
in database systems that use pessimistic concurrency
control. In this paper, we introduce very lightweight
locking (VLL), an alternative approach to pessimistic
concurrency control for main memory database systems,
which avoids almost all overhead associated with
traditional lock manager operations. We also propose a
protocol called selective contention analysis (SCA),
which enables systems implementing VLL to achieve high
transactional throughput under high-contention
workloads. We implement these protocols both in a
traditional single-machine multi-core database server
setting and in a distributed database where data are
partitioned across many commodity machines in a
shared-nothing cluster. Furthermore, we show how VLL
and SCA can be extended to enable range locking. Our
experiments show that VLL dramatically reduces locking
overhead and thereby increases transactional throughput
in both settings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Galarraga:2015:FRM,
author = "Luis Gal{\'a}rraga and Christina Teflioudi and Katja
Hose and Fabian M. Suchanek",
title = "Fast rule mining in ontological knowledge bases with
{AMIE++}",
journal = j-VLDB-J,
volume = "24",
number = "6",
pages = "707--730",
month = dec,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0394-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 25 15:38:42 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Recent advances in information extraction have led to
huge knowledge bases (KBs), which capture knowledge in
a machine-readable format. Inductive logic programming
(ILP) can be used to mine logical rules from these KBs,
such as ``If two persons are married, then they
(usually) live in the same city.'' While ILP is a
mature field, mining logical rules from KBs is
difficult, because KBs make an open-world assumption.
This means that absent information cannot be taken as
counterexamples. Our approach AMIE (Gal&\#225;rraga et
al. in WWW, 2013) has shown how rules can be mined
effectively from KBs even in the absence of
counterexamples. In this paper, we show how this
approach can be optimized to mine even larger KBs with
more than 12M statements. Extensive experiments show
how our new approach, AMIE++, extends to areas of
mining that were previously beyond reach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chandra:2015:DGT,
author = "Bikash Chandra and Bhupesh Chawda and Biplab Kar and
K. V. Reddy and Shetal Shah and S. Sudarshan",
title = "Data generation for testing and grading {SQL}
queries",
journal = j-VLDB-J,
volume = "24",
number = "6",
pages = "731--755",
month = dec,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0395-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 25 15:38:42 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Correctness of SQL queries is usually tested by
executing the queries on one or more datasets.
Erroneous queries are often the results of small
changes or mutations of the correct query. A mutation
Q'`? of a query Q is killed by a dataset D if Q(D) \ne
`? Q'`?(D). Earlier work on the XData system showed how
to generate datasets that kill all mutations in a class
of mutations that included join type and comparison
operation mutations. In this paper, we extend the XData
data generation techniques to handle a wider variety of
SQL queries and a much larger class of mutations. We
have also built a system for grading SQL queries using
the datasets generated by XData. We present a study of
the effectiveness of the datasets generated by the
extended XData approach, using a variety of queries
including queries submitted by students as part of a
database course. We show that the XData datasets
outperform predefined datasets as well as manual
grading done earlier by teaching assistants, while also
avoiding the drudgery of manual correction. Thus, we
believe that our techniques will be of great value to
database course instructors and TAs, particularly to
those of MOOCs. It will also be valuable to database
application developers and testers for testing SQL
queries.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2015:MMO,
author = "Chao Li and Gerome Miklau and Michael Hay and Andrew
Mcgregor and Vibhor Rastogi",
title = "The matrix mechanism: optimizing linear counting
queries under differential privacy",
journal = j-VLDB-J,
volume = "24",
number = "6",
pages = "757--781",
month = dec,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0398-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 25 15:38:42 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Differential privacy is a robust privacy standard that
has been successfully applied to a range of data
analysis tasks. We describe the matrix mechanism, an
algorithm for answering a workload of linear counting
queries that adapts the noise distribution to
properties of the provided queries. Given a workload,
the mechanism uses a different set of queries, called a
query strategy, which are answered using a standard
Laplace or Gaussian mechanism. Noisy answers to the
workload queries are then derived from the noisy
answers to the strategy queries. This two-stage process
can result in a more complex, correlated noise
distribution that preserves differential privacy but
increases accuracy. We provide a formal analysis of the
error of query answers produced by the mechanism and
investigate the problem of computing the optimal query
strategy in support of a given workload. We show that
this problem can be formulated as a rank-constrained
semidefinite program. We analyze two seemingly distinct
techniques proposed in the literature, whose similar
behavior is explained by viewing them as instances of
the matrix mechanism. We also describe an extension of
the mechanism in which nonnegativity constraints are
included in the derivation process and provide
experimental evidence of its efficacy.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Armenatzoglou:2015:GSR,
author = "Nikos Armenatzoglou and Ritesh Ahuja and Dimitris
Papadias",
title = "{Geo-Social Ranking}: functions and query processing",
journal = j-VLDB-J,
volume = "24",
number = "6",
pages = "783--799",
month = dec,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0400-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 25 15:38:42 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Given a query location q, Geo-Social Ranking (GSR)
ranks the users of a Geo-Social Network based on their
distance to q, the number of their friends in the
vicinity of q, and possibly the connectivity of those
friends. We propose a general GSR framework and four
GSR functions that assign scores in different ways: (i)
LC, which is a weighted linear combination of social
(i.e., friendships) and spatial (i.e., distance to q)
aspects, (ii) RC, which is a ratio combination of the
two aspects, (iii) HGS, which considers the number of
friends in coincident circles centered at q, and (iv)
GST, which takes into account triangles of friends in
the vicinity of q. We investigate the behavior of the
functions, qualitatively assess their results, and
study the effects of their parameters. Moreover, for
each ranking function, we design a query processing
technique that utilizes its specific characteristics to
efficiently retrieve the top-k users. Finally, we
experimentally evaluate the performance of the top-k
algorithms with real and synthetic datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Santini:2015:QSU,
author = "Simone Santini",
title = "Querying streams using regular expressions: some
semantics, decidability, and efficiency issues",
journal = j-VLDB-J,
volume = "24",
number = "6",
pages = "801--821",
month = dec,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0402-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 25 15:38:42 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper analyzes the decidability and complexity
problems that arise when matching regular expressions
on infinite streams of sets of symbols. We show that in
important application domains, several apparently
obvious semantics lead to detecting spurious events
(events that are mere artifacts of the semantics) or to
missing events of potential interest. We single out a
class of semantics, of interest in many applications,
which we dub use-and-throw: In a use-and-throw
semantics, an elementary event can participate in the
creation of at most one detected complex event. Many
areas of research have identified this as a desirable
requirement (we give the examples of databases and
video surveillance), but hitherto there has been no
systematic study of the characteristics of these
semantics, in particular their decidability and
algorithmic complexity. This paper is meant to provide
at least some initial answers on this subject. We
analyze several semantics, provide polynomial
algorithms for them, and prove their correctness and
their properties.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2015:ATE,
author = "Xiang Wang and Ying Zhang and Wenjie Zhang and Xuemin
Lin and Wei Wang",
title = "{AP-Tree}: efficiently support location-aware
{Publish\slash Subscribe}",
journal = j-VLDB-J,
volume = "24",
number = "6",
pages = "823--848",
month = dec,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0403-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 25 15:38:42 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We investigate the problem of efficiently supporting
location-aware Publish/Subscribe (Pub/Sub for short),
which is essential in many applications such as
location-based recommendation and advertising, thanks
to the proliferation of geo-equipped devices and the
ensuing location-based social media applications. In a
location-aware Pub/Sub system (e.g., an e-coupon
system), subscribers can register their interest as
spatial-keyword subscriptions (e.g., interest in nearby
iphone discount); each incoming geo-textual message
(e.g., geo-tagged e-coupon) will be delivered to all
the relevant subscribers immediately. While there are
several prior approaches aiming at providing efficient
processing techniques for this problem, their
approaches belong to spatial-prioritized indexing
method which cannot well exploit the keyword
distribution. In addition, their textual filtering
techniques are built upon simple variants of
traditional inverted indexes, which do not perform well
for the textual constraint imposed by the problem. In
this paper, we address the above limitations and
provide a highly efficient solution based on a novel
adaptive index, named AP-Tree. AP-Tree adaptively
groups registered subscriptions using keyword and
spatial partitions, guided by a cost model. AP-Tree
also naturally indexes ordered keyword combinations.
Furthermore, we show that our techniques can be
extended to process moving spatial-keyword
subscriptions, where subscribers can continuously
update their locations. We present efficient algorithms
to process both stationary and moving subscriptions,
which can seamlessly and effectively integrate keyword
and spatial partitions. Our extensive experiments
demonstrate that AP-Tree and its variant AP ^{+}+ -Tree
can achieve up to an order of magnitude improvement on
efficiency compared with prior state-of-the-art
methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Basik:2015:STS,
author = "Fuat Bas{\i}k and Bu{\u{g}}ra Gedik and Hakan
Ferhatosmano{\u{g}}lu and Mert Emin Kalender",
title = "{S$^{33}$-TM}: scalable streaming short text
matching",
journal = j-VLDB-J,
volume = "24",
number = "6",
pages = "849--866",
month = dec,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0404-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Nov 25 15:38:42 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/string-matching.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Micro-blogging services have become major venues for
information creation, as well as channels of
information dissemination. Accordingly, monitoring them
for relevant information is a critical capability. This
is typically achieved by registering content-based
subscriptions with the micro-blogging service. Such
subscriptions are long-running queries that are
evaluated against the stream of posts. Given the
popularity and scale of micro-blogging services like
Twitter and Weibo, building a scalable infrastructure
to evaluate these subscriptions is a challenge. To
address this challenge, we present the S^33-TM system
for streaming short text matching. S^33-TM is organized
as a stream processing application, in the form of a
data parallel flow graph designed to be run on a data
center environment. It takes advantage of the structure
of the publications (posts) and subscriptions to
perform the matching in a scalable manner, without
broadcasting publications or subscriptions to all of
the matcher instances. The basic design of S^33-TM uses
a scoped multicast for publications and scoped anycast
for subscriptions. To further improve throughput, we
introduce publication routing algorithms that aim at
minimizing the scope of the multicasts. First set of
algorithms we develop are based on partitioning the
word co-occurrence frequency graph, with the aim of
routing posts that include commonly co-occurring words
to a small set of matchers. While effective, these
algorithms fell short in balancing the load. To address
this, we develop the SALB algorithm, which provides
better load balance by modeling the load more
accurately using the word-to-post bipartite graph. We
also develop a subscription placement algorithm, called
LASP, to group together similar subscriptions, in order
to minimize the subscription matching cost.
Furthermore, to achieve good scalability for increasing
number of nodes, we introduce techniques to handle
workload skew. Finally, we introduce load shedding
techniques for handling unexpected load spikes with
small impact on the accuracy. Our experimental results
show that S^33-TM is scalable. Furthermore, the SALB
algorithm provides more than 2.5\times 2.5$ \times $
throughput compared to the baseline multicast and
outperforms the graph partitioning-based approaches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Jagadish:2016:SIB,
author = "H. V. Jagadish and Aoying Zhou",
title = "Special issue on best papers of {VLDB 2014}",
journal = j-VLDB-J,
volume = "25",
number = "1",
pages = "1--2",
month = feb,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0399-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jan 21 17:41:55 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Jiang:2016:EES,
author = "Dawei Jiang and Sai Wu and Gang Chen and Beng Chin Ooi
and Kian-Lee Tan and Jun Xu",
title = "{epiC}: an extensible and scalable system for
processing {Big Data}",
journal = j-VLDB-J,
volume = "25",
number = "1",
pages = "3--26",
month = feb,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0393-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jan 21 17:41:55 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The Big Data problem is characterized by the so-called
3V features: volume --- a huge amount of data, velocity
--- a high data ingestion rate, and variety --- a mix
of structured data, semi-structured data, and
unstructured data. The state-of-the-art solutions to
the Big Data problem are largely based on the MapReduce
framework (aka its open source implementation Hadoop).
Although Hadoop handles the data volume challenge
successfully, it does not deal with the data variety
well since the programming interfaces and its
associated data processing model are inconvenient and
inefficient for handling structured data and graph
data. This paper presents epiC, an extensible system to
tackle the Big Data's data variety challenge. epiC
introduces a general Actor-like concurrent programming
model, independent of the data processing models, for
specifying parallel computations. Users process
multi-structured datasets with appropriate epiC
extensions, and the implementation of a data processing
model best suited for the data type and auxiliary code
for mapping that data processing model into epiC's
concurrent programming model. Like Hadoop, programs
written in this way can be automatically parallelized
and the runtime system takes care of fault tolerance
and inter-machine communications. We present the design
and implementation of epiC's concurrent programming
model. We also present two customized data processing
models, an optimized MapReduce extension and a
relational model, on top of epiC. We show how users can
leverage epiC to process heterogeneous data by linking
different types of operators together. To improve the
performance of complex analytic jobs, epiC supports a
partition-based optimization technique where data are
streamed between the operators to avoid the high I/O
overheads. Experiments demonstrate the effectiveness
and efficiency of our proposed epiC.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Schuhknecht:2016:EEA,
author = "Felix Martin Schuhknecht and Alekh Jindal and Jens
Dittrich",
title = "An experimental evaluation and analysis of database
cracking",
journal = j-VLDB-J,
volume = "25",
number = "1",
pages = "27--52",
month = feb,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0397-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jan 21 17:41:55 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Database cracking has been an area of active research
in recent years. The core idea of database cracking is
to create indexes adaptively and incrementally as a
side product of query processing. Several works have
proposed different cracking techniques for different
aspects including updates, tuple reconstruction,
convergence, concurrency control, and robustness. Our
2014 VLDB paper ``The Uncracked Pieces in Database
Cracking'' (PVLDB 7:97---108, 2013/VLDB 2014) was the
first comparative study of these different methods by
an independent group. In this article, we extend our
published experimental study on database cracking and
bring it to an up-to-date state. Our goal is to
critically review several aspects, identify the
potential, and propose promising directions in database
cracking. With this study, we hope to expand the scope
of database cracking and possibly leverage cracking in
database engines other than MonetDB. We repeat several
prior database cracking works including the core
cracking algorithms as well as three other works on
convergence (hybrid cracking), tuple reconstruction
(sideways cracking), and robustness (stochastic
cracking), respectively. Additionally to our conference
paper, we now also look at a recently published study
about CPU efficiency (predication cracking). We
evaluate these works and show possible directions to do
even better. As a further extension, we evaluate the
whole class of parallel cracking algorithms that were
proposed in three recent works. Altogether, in this
work we revisit 8 papers on database cracking and
evaluate in total 18 cracking methods, 6 sorting
algorithms, and 3 full index structures. Additionally,
we test cracking under a variety of experimental
settings, including high selectivity (Low selectivity
means that many entries qualify. Consequently, a high
selectivity means, that only few entries qualify)
queries, low selectivity queries, varying selectivity,
and multiple query access patterns. Finally, we compare
cracking against different sorting algorithms as well
as against different main memory optimized indexes,
including the recently proposed adaptive radix tree
(ART). Our results show that: (1) the previously
proposed cracking algorithms are repeatable, (2) there
is still enough room to significantly improve the
previously proposed cracking algorithms, (3)
parallelizing cracking algorithms efficiently is a hard
task, (4) cracking depends heavily on query
selectivity, (5) cracking needs to catch up with modern
indexing trends, and (6) different indexing algorithms
have different indexing signatures.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Jugel:2016:VAV,
author = "Uwe Jugel and Zbigniew Jerzak and Gregor Hackenbroich
and Volker Markl",
title = "{VDDA}: automatic visualization-driven data
aggregation in relational databases",
journal = j-VLDB-J,
volume = "25",
number = "1",
pages = "53--77",
month = feb,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0396-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jan 21 17:41:55 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Contemporary RDBMS-based systems for visualization of
high-volume numerical data have difficulty to cope with
the hard latency requirements and high ingestion rates
of interactive visualizations. Existing solutions for
lowering the volume of large data sets disregard the
spatial properties of visualizations, resulting in
visualization errors. In this work, we introduce VDDA,
a visualization-driven data aggregation that models
visual aggregation at the pixel level as data
aggregation at the query level. Based on the M4
aggregation for producing pixel-perfect line charts
from highly reduced data subsets, we define a complete
set of data reduction operators that simulate the
overplotting behavior of the most frequently used chart
types. Relying only on the relational algebra and the
common data aggregation functions, our approach is
generic and applicable to any visualization system that
consumes data stored in relational databases. We
demonstrate our visualization-driven data aggregation
using real-world data sets from high-tech
manufacturing, stock markets, and sports analytics,
reducing data volumes by up to two orders of magnitude,
while preserving pixel-perfect visualizations, as
producible from the raw data.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2016:EDL,
author = "Wei Wang and Xiaoyan Yang and Beng Chin Ooi and
Dongxiang Zhang and Yueting Zhuang",
title = "Effective deep learning-based multi-modal retrieval",
journal = j-VLDB-J,
volume = "25",
number = "1",
pages = "79--101",
month = feb,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0391-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jan 21 17:41:55 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Multi-modal retrieval is emerging as a new search
paradigm that enables seamless information retrieval
from various types of media. For example, users can
simply snap a movie poster to search for relevant
reviews and trailers. The mainstream solution to the
problem is to learn a set of mapping functions that
project data from different modalities into a common
metric space in which conventional indexing schemes for
high-dimensional space can be applied. Since the
effectiveness of the mapping functions plays an
essential role in improving search quality, in this
paper, we exploit deep learning techniques to learn
effective mapping functions. In particular, we first
propose a general learning objective that effectively
captures both intramodal and intermodal semantic
relationships of data from heterogeneous sources. Given
the general objective, we propose two learning
algorithms to realize it: (1) an unsupervised approach
that uses stacked auto-encoders and requires minimum
prior knowledge on the training data and (2) a
supervised approach using deep convolutional neural
network and neural language model. Our training
algorithms are memory efficient with respect to the
data volume. Given a large training dataset, we split
it into mini-batches and adjust the mapping functions
continuously for each batch. Experimental results on
three real datasets demonstrate that our proposed
methods achieve significant improvement in search
accuracy over the state-of-the-art solutions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Funke:2016:KPC,
author = "Stefan Funke and Andr{\'e} Nusser and Sabine
Storandt",
title = "On {$k$-Path Covers} and their applications",
journal = j-VLDB-J,
volume = "25",
number = "1",
pages = "103--123",
month = feb,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0392-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jan 21 17:41:55 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "For a directed graph G with vertex set V, we call a
subset $ C \subseteq V $ a $k$-(All-)Path Cover if $C$
contains a node from any simple path in $G$ consisting
of $k$ nodes. This paper considers the problem of
constructing small $k$-Path Covers in the context of
road networks with millions of nodes and edges. In many
application scenarios, the set C and its induced
overlay graph constitute a very compact synopsis of
$G$, which is the basis for the currently fastest data
structure for personalized shortest path queries,
visually pleasing overlays of subsampled paths, and
efficient reporting, retrieval and aggregation of
associated data in spatial network databases. Apart
from a theoretic investigation of the problem, we
provide efficient algorithms that produce very small
$k$-Path Covers for large real-world road networks
(with a posteriori guarantees via instance-based lower
bounds). We also apply our algorithms to other (social,
collaboration, web, etc.) networks and can improve in
several instances upon previous approaches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Quamar:2016:NNC,
author = "Abdul Quamar and Amol Deshpande and Jimmy Lin",
title = "{NScale}: neighborhood-centric large-scale graph
analytics in the cloud",
journal = j-VLDB-J,
volume = "25",
number = "2",
pages = "125--150",
month = apr,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0405-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 25 16:34:05 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "There is an increasing interest in executing complex
analyses over large graphs, many of which require
processing a large number of multi-hop neighborhoods or
subgraphs. Examples include ego network analysis, motif
counting, finding social circles, personalized
recommendations, link prediction, anomaly detection,
analyzing influence cascades, and others. These tasks
are not well served by existing vertex-centric graph
processing frameworks, where user programs are only
able to directly access the state of a single vertex at
a time, resulting in high communication, scheduling,
and memory overheads in executing such tasks. Further,
most existing graph processing frameworks ignore the
challenges in extracting the relevant portions of the
graph that an analysis task is interested in, and
loading those onto distributed memory. This paper
introduces NScale, a novel end-to-end graph processing
framework that enables the distributed execution of
complex subgraph-centric analytics over large-scale
graphs in the cloud. NScale enables users to write
programs at the level of subgraphs rather than at the
level of vertices. Unlike most previous graph
processing frameworks, which apply the user program to
the entire graph, NScale allows users to declaratively
specify subgraphs of interest. Our framework includes a
novel graph extraction and packing (GEP) module that
utilizes a cost-based optimizer to partition and pack
the subgraphs of interest into memory on as few
machines as possible. The distributed execution engine
then takes over and runs the user program in parallel
on those subgraphs, restricting the scope of the
execution appropriately, and utilizes novel techniques
to minimize memory consumption by exploiting overlaps
among the subgraphs. We present a comprehensive
empirical evaluation comparing against three
state-of-the-art systems, namely Giraph, GraphLab, and
GraphX, on several real-world datasets and a variety of
analysis tasks. Our experimental results show
orders-of-magnitude improvements in performance and
drastic reductions in the cost of analytics compared to
vertex-centric approaches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Soule:2016:RAS,
author = "Robert Soul{\'e} and Bugra Gedik",
title = "{RailwayDB}: adaptive storage of interaction graphs",
journal = j-VLDB-J,
volume = "25",
number = "2",
pages = "151--169",
month = apr,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0407-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 25 16:34:05 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We are living in an ever more connected world, where
data recording the interactions between people,
software systems, and the physical world is becoming
increasingly prevalent. These data often take the form
of a temporally evolving graph, where entities are the
vertices and the interactions between them are the
edges. We call such graphs interaction graphs. Various
domains, including telecommunications, transportation,
and social media, depend on analytics performed on
interaction graphs. The ability to efficiently support
historical analysis over interaction graphs requires
effective solutions for the problem of data layout on
disk. This paper presents an adaptive disk layout
called the railway layout for optimizing disk block
storage for interaction graphs. The key idea is to
divide blocks into one or more sub-blocks. Each
sub-block contains the entire graph structure, but only
a subset of the attributes. This improves query I/O, at
the cost of increased storage overhead. We introduce
optimal integer linear program (ILP) formulations for
partitioning disk blocks into sub-blocks with
overlapping and nonoverlapping attributes.
Additionally, we present greedy heuristics that can
scale better compared to the ILP alternatives, yet
achieve close to optimal query I/O. We provide an
implementation of the railway layout as part of
RailwayDB--an open-source graph database we have
developed. To demonstrate the benefits of the railway
layout, we provide an extensive experimental
evaluation, including model-based as well as empirical
results comparing our approach to baseline
alternatives.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yuan:2016:DTK,
author = "Long Yuan and Lu Qin and Xuemin Lin and Lijun Chang
and Wenjie Zhang",
title = "Diversified top-$k$ clique search",
journal = j-VLDB-J,
volume = "25",
number = "2",
pages = "171--196",
month = apr,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0408-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 25 16:34:05 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Maximal clique enumeration is a fundamental problem in
graph theory and has been extensively studied. However,
maximal clique enumeration is time-consuming in large
graphs and always returns enormous cliques with large
overlaps. Motivated by this, in this paper, we study
the diversified top-k clique search problem which is to
find top-k cliques that can cover most number of nodes
in the graph. Diversified top-k clique search can be
widely used in a lot of applications including
community search, motif discovery, and anomaly
detection in large graphs. A naive solution for
diversified top-k clique search is to keep all maximal
cliques in memory and then find k of them that cover
most nodes in the graph by using the approximate greedy
max k-cover algorithm. However, such a solution is
impractical when the graph is large. In this paper,
instead of keeping all maximal cliques in memory, we
devise an algorithm to maintain k candidates in the
process of maximal clique enumeration. Our algorithm
has limited memory footprint and can achieve a
guaranteed approximation ratio. We also introduce a
novel light-weight \mathsf {PNP}PNP-\mathsf
{Index}Index, based on which we design an optimal
maximal clique maintenance algorithm. We further
explore three optimization strategies to avoid
enumerating all maximal cliques and thus largely reduce
the computational cost. Besides, for the massive input
graph, we develop an I/O efficient algorithm to tackle
the problem when the input graph cannot fit in main
memory. We conduct extensive performance studies on
real graphs and synthetic graphs. One of the real
graphs contains 1.02 billion edges. The results
demonstrate the high efficiency and effectiveness of
our approach.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Pham:2016:ACW,
author = "Thao N. Pham and Panos K. Chrysanthis and Alexandros
Labrinidis",
title = "Avoiding class warfare: managing continuous queries
with differentiated classes of service",
journal = j-VLDB-J,
volume = "25",
number = "2",
pages = "197--221",
month = apr,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0411-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 25 16:34:05 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Data stream management systems (DSMSs) offer the most
effective solution for processing data streams by
efficiently executing continuous queries (CQs) over the
incoming data. CQs inherently have different levels of
criticality and hence different levels of expected
quality of service (QoS) and quality of data (QoD).
Adhering to such expected QoS/QoD metrics is even more
important in cases of multi-tenant data stream
management services. In this work, we propose DILoS, a
framework that, through priority-based scheduling and
load shedding, supports differentiated QoS and QoD for
multiple classes of CQs. Unlike existing works that
consider scheduling and load shedding separately, DILoS
is a novel unified framework that exploits the synergy
between scheduling and load shedding. We also propose
ALoMa, a general, adaptive load manager that DILoS is
built upon. By its design, ALoMa performs better than
the state-of-the-art alternatives in three dimensions:
(1) it automatically tunes the headroom factor, (2) it
honors the delay target, (3) it is applicable to
complex query networks with shared operators. We
implemented DILoS and ALoMa in our real DSMS prototype
system (AQSIOS) and evaluate their performance for a
variety of real and synthetic workloads. Our
experimental evaluation of ALoMa verified its clear
superiority over the state-of-the-art approaches. Our
experimental evaluation of the DILoS framework showed
that it (a) allows the scheduler and load shedder to
consistently honor CQs' priorities, (b) significantly
increases system capacity utilization by exploiting
batch processing, and (c) enables operator sharing
among query classes of different priorities while
avoiding priority inversion, i.e., a lower-priority
class never blocks a higher-priority one.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Langer:2016:EOD,
author = "Philipp Langer and Felix Naumann",
title = "Efficient order dependency detection",
journal = j-VLDB-J,
volume = "25",
number = "2",
pages = "223--241",
month = apr,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0412-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 25 16:34:05 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Order dependencies (ODs) describe a relationship of
order between lists of attributes in a relational
table. ODs can help to understand the semantics of
datasets and the applications producing them. They have
applications in the field of query optimization by
suggesting query rewrites. Also, the existence of an OD
in a table can provide hints on which integrity
constraints are valid for the domain of the data at
hand. This work is the first to describe the discovery
problem for order dependencies in a principled manner
by characterizing the search space, developing and
proving pruning rules, and presenting the algorithm
Order, which finds all order dependencies in a given
table. Order traverses the lattice of permutations of
attributes in a level-wise bottom-up manner. In a
comprehensive evaluation, we show that it is efficient
even for various large datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Peng:2016:PSQ,
author = "Peng Peng and Lei Zou and M. Tamer {\"O}zsu and Lei
Chen and Dongyan Zhao",
title = "Processing {SPARQL} queries over distributed {RDF}
graphs",
journal = j-VLDB-J,
volume = "25",
number = "2",
pages = "243--268",
month = apr,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0415-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 25 16:34:05 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We propose techniques for processing SPARQL queries
over a large RDF graph in a distributed environment. We
adopt a ``partial evaluation and assembly'' framework.
Answering a SPARQL query Q is equivalent to finding
subgraph matches of the query graph Q over RDF graph G.
Based on properties of subgraph matching over a
distributed graph, we introduce local partial match as
partial answers in each fragment of RDF graph G. For
assembly, we propose two methods: centralized and
distributed assembly. We analyze our algorithms from
both theoretically and experimentally. Extensive
experiments over both real and benchmark RDF
repositories of billions of triples confirm that our
method is superior to the state-of-the-art methods in
both the system's performance and scalability.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gao:2016:TCP,
author = "Jun Gao and Chang Zhou and Jeffrey Xu Yu",
title = "Toward continuous pattern detection over evolving
large graph with snapshot isolation",
journal = j-VLDB-J,
volume = "25",
number = "2",
pages = "269--290",
month = apr,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0416-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Mar 25 16:34:05 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper studies continuous pattern detection over
large evolving graphs, which plays an important role in
monitoring-related applications. The problem is
challenging due to the large size and dynamic updates
of graphs, the massive search space of pattern
detection and inconsistent query results on dynamic
graphs. This paper first introduces a snapshot
isolation requirement, which ensures that the query
results come from a consistent graph snapshot instead
of a mixture of partial evolving graphs. Second, we
propose an SSD (single sink directed acyclic graph)
plan friendly to vertex-centric-distributed graph
processing frameworks. SSD plan can guide the message
transformation and transfer among graph vertices, and
determine the satisfaction of the pattern on graph
vertices for the sink vertex. Third, we devise
strategies for major steps in the SSD evaluation,
including the location of valid messages to achieve
snapshot isolation, AO-List to determine the
satisfaction of transition rule over dynamic graph, and
message-on-change policy to reduce outgoing messages.
The experiments on billion-edge graphs using Giraph, an
open source implementation of Pregel, illustrate the
efficiency and effectiveness of our method.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Baumann:2016:BDC,
author = "Stephan Baumann and Peter Boncz and Kai-Uwe Sattler",
title = "Bitwise dimensional co-clustering for analytical
workloads",
journal = j-VLDB-J,
volume = "25",
number = "3",
pages = "291--316",
month = jun,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0417-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue May 24 16:31:54 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Analytical workloads in data warehouses often include
heavy joins where queries involve multiple fact tables
in addition to the typical star-patterns, dimensional
grouping and selections. In this paper we propose a new
processing and storage framework called bitwise
dimensional co-clustering (BDCC) that avoids
replication and thus keeps updates fast, yet is able to
accelerate all these foreign key joins, efficiently
support grouping and pushes down most dimensional
selections. The core idea of BDCC is to cluster each
table on a mix of dimensions, each possibly derived
from attributes imported over an incoming foreign key
and this way creating foreign key connected tables with
partially shared clusterings. These are later used to
accelerate any join between two tables that have some
dimension in common and additionally permit to push
down and propagate selections (reduce I/O) and
accelerate aggregation and ordering operations. Besides
the general framework, we describe an algorithm to
derive such a physical co-clustering database
automatically and describe query processing and query
optimization techniques that can easily be fitted into
existing relational engines. We present an experimental
evaluation on the TPC-H benchmark in the Vectorwise
system, showing that co-clustering can significantly
enhance its already high performance and at the same
time significantly reduce the memory consumption of the
system.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2016:EAF,
author = "Feifei Li and Ke Yi and Yufei Tao and Bin Yao and Yang
Li and Dong Xie and Min Wang",
title = "Exact and approximate flexible aggregate similarity
search",
journal = j-VLDB-J,
volume = "25",
number = "3",
pages = "317--338",
month = jun,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0418-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue May 24 16:31:54 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Aggregate similarity search, also known as aggregate
nearest-neighbor (Ann) query, finds many useful
applications in spatial and multimedia databases. Given
a group Q of M query objects, it retrieves from a
database the objects most similar to Q, where the
similarity is an aggregation (e.g.,
{{\mathrm{sum}}}sum, \max max) of the distances between
each retrieved object p and all the objects in Q. In
this paper, we propose an added flexibility to the
query definition, where the similarity is an
aggregation over the distances between p and any subset
of \phi M`?M objects in Q for some {support0$<$} \phi
\le 10{$<$}`?{$<$}=1. We call this new definition
flexible aggregate similarity search and accordingly
refer to a query as a flexible aggregate
nearest-neighbor ( Fann ) query. We present algorithms
for answering Fann queries exactly and approximately.
Our approximation algorithms are especially appealing,
which are simple, highly efficient, and work well in
both low and high dimensions. They also return
near-optimal answers with guaranteed constant-factor
approximations in any dimensions. Extensive experiments
on large real and synthetic datasets from 2 to 74
dimensions have demonstrated their superior efficiency
and high quality.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Guzun:2016:HQO,
author = "Gheorghi Guzun and Guadalupe Canahuate",
title = "Hybrid query optimization for hard-to-compress
bit-vectors",
journal = j-VLDB-J,
volume = "25",
number = "3",
pages = "339--354",
month = jun,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0419-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue May 24 16:31:54 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Bit-vectors are widely used for indexing and
summarizing data due to their efficient processing in
modern computers. Sparse bit-vectors can be further
compressed to reduce their space requirement. Special
compression schemes based on run-length encoders have
been designed to avoid explicit decompression and
minimize the decoding overhead during query execution.
Moreover, highly compressed bit-vectors can exhibit a
faster query time than the non-compressed ones.
However, for hard-to-compress bit-vectors, compression
does not speed up queries and can add considerable
overhead. In these cases, bit-vectors are often stored
verbatim (non-compressed). On the other hand, queries
are answered by executing a cascade of bit-wise
operations involving indexed bit-vectors and
intermediate results. Often, even when the original
bit-vectors are hard to compress, the intermediate
results become sparse. It could be feasible to improve
query performance by compressing these bit-vectors as
the query is executed. In this scenario, it would be
necessary to operate verbatim and compressed
bit-vectors together. In this paper, we propose a
hybrid framework where compressed and verbatim bitmaps
can coexist and design algorithms to execute queries
under this hybrid model. Our query optimizer is able to
decide at run time when to compress or decompress a
bit-vector. Our heuristics show that the applications
using higher-density bitmaps can benefit from using
this hybrid model, improving both their query time and
memory utilization.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Harbi:2016:ASQ,
author = "Razen Harbi and Ibrahim Abdelaziz and Panos Kalnis and
Nikos Mamoulis and Yasser Ebrahim and Majed Sahli",
title = "Accelerating {SPARQL} queries by exploiting hash-based
locality and adaptive partitioning",
journal = j-VLDB-J,
volume = "25",
number = "3",
pages = "355--380",
month = jun,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0420-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue May 24 16:31:54 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "State-of-the-art distributed RDF systems partition
data across multiple computer nodes (workers). Some
systems perform cheap hash partitioning, which may
result in expensive query evaluation. Others try to
minimize inter-node communication, which requires an
expensive data preprocessing phase, leading to a high
startup cost. Apriori knowledge of the query workload
has also been used to create partitions, which,
however, are static and do not adapt to workload
changes. In this paper, we propose AdPart, a
distributed RDF system, which addresses the
shortcomings of previous work. First, AdPart applies
lightweight partitioning on the initial data, which
distributes triples by hashing on their subjects; this
renders its startup overhead low. At the same time, the
locality-aware query optimizer of AdPart takes full
advantage of the partitioning to (1) support the fully
parallel processing of join patterns on subjects and
(2) minimize data communication for general queries by
applying hash distribution of intermediate results
instead of broadcasting, wherever possible. Second,
AdPart monitors the data access patterns and
dynamically redistributes and replicates the instances
of the most frequent ones among workers. As a result,
the communication cost for future queries is
drastically reduced or even eliminated. To control
replication, AdPart implements an eviction policy for
the redistributed patterns. Our experiments with
synthetic and real data verify that AdPart: (1) starts
faster than all existing systems; (2) processes
thousands of queries before other systems become
online; and (3) gracefully adapts to the query load,
being able to evaluate queries on billion-scale RDF
data in subseconds.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bonifati:2016:MEO,
author = "Angela Bonifati and Werner Nutt and Riccardo Torlone
and Jan {Van Den Bussche}",
title = "Mapping-equivalence and oid-equivalence of
single-function object-creating conjunctive queries",
journal = j-VLDB-J,
volume = "25",
number = "3",
pages = "381--397",
month = jun,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0421-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue May 24 16:31:54 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Conjunctive database queries have been extended with a
mechanism for object creation to capture important
applications such as data exchange, data integration,
and ontology-based data access. Object creation
generates new object identifiers in the result that do
not belong to the set of constants in the source
database. The new object identifiers can be also seen
as Skolem terms. Hence, object-creating conjunctive
queries can also be regarded as restricted second-order
tuple-generating dependencies (SO-tgds), considered in
the data exchange literature. In this paper, we focus
on the class of single-function object-creating
conjunctive queries, or sifo CQs for short. The
single-function symbol can be used only once in the
head of the query. We give a new characterization for
oid-equivalence of sifo CQs that is simpler than the
one given by Hull and Yoshikawa and places the problem
in the complexity class NP. Our characterization is
based on Cohen's equivalence notions for conjunctive
queries with multiplicities. We also solve the logical
entailment problem for sifo CQs, showing that also this
problem belongs to NP. Results by Pichler et al. have
shown that logical equivalence for more general classes
of SO-tgds is either undecidable or decidable with as
yet unknown complexity upper bounds.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lu:2016:DCE,
author = "Yue Lu and Yuguan Li and Mohamed Y. Eltabakh",
title = "Decorating the cloud: enabling annotation management
in {MapReduce}",
journal = j-VLDB-J,
volume = "25",
number = "3",
pages = "399--424",
month = jun,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0422-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue May 24 16:31:54 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Data curation and annotation are indispensable
mechanisms to a wide range of applications for
capturing various types of metadata information. This
metadata not only increases the data's credibility and
merit, and allows end users and applications to make
more informed decisions, but also enables advanced
processing over the data that is not feasible
otherwise. That is why annotation management has been
extensively studied in the context of scientific
repositories, web documents, and relational database
systems. In this paper, we make the case that
cloud-based applications that rely on the emerging
Hadoop infrastructure are also in need for data
curation and annotation and that the presence of such
mechanisms in Hadoop would bring value-added
capabilities to these applications. We propose the
``CloudNotes'' system, a full-fledged MapReduce-based
annotation management engine. CloudNotes addresses
several new challenges to annotation management
including: (1) scalable and distributed processing of
annotations over large clusters, (2) propagation of
annotations under the MapReduce's blackbox execution
model, and (3) annotation-driven optimizations ranging
from proactive prefetching and colocation of
annotations, annotation-aware task scheduling, novel
shared execution strategies among the annotation jobs,
and concurrency control mechanisms for annotation
management. These challenges have not been addressed or
explored before by the state-of-art technologies.
CloudNotes is built on top of the open-source
Hadoop/HDFS infrastructure and experimentally evaluated
to demonstrate the practicality and scalability of its
features, and the effectiveness of its optimizations
under large workloads.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sariyuce:2016:IKC,
author = "Ahmet Erdem Sariy{\"u}ce and Bugra Gedik and Gabriela
Jacques-Silva and Kun-Lung Wu and {\"U}mit V.
{\c{C}}ataly{\"u}rek",
title = "Incremental $k$-core decomposition: algorithms and
evaluation",
journal = j-VLDB-J,
volume = "25",
number = "3",
pages = "425--447",
month = jun,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0423-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue May 24 16:31:54 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A $k$-core of a graph is a maximal connected subgraph
in which every vertex is connected to at least k
vertices in the subgraph. $k$-core decomposition is
often used in large-scale network analysis, such as
community detection, protein function prediction,
visualization, and solving NP-hard problems on real
networks efficiently, like maximal clique finding. In
many real-world applications, networks change over
time. As a result, it is essential to develop efficient
incremental algorithms for dynamic graph data. In this
paper, we propose a suite of incremental $k$-core
decomposition algorithms for dynamic graph data. These
algorithms locate a small subgraph that is guaranteed
to contain the list of vertices whose maximum $k$-core
values have changed and efficiently process this
subgraph to update the $k$-core decomposition. We
present incremental algorithms for both insertion and
deletion operations, and propose auxiliary vertex state
maintenance techniques that can further accelerate
these operations. Our results show a significant
reduction in runtime compared to non-incremental
alternatives. We illustrate the efficiency of our
algorithms on different types of real and synthetic
graphs, at varying scales. For a graph of 16 million
vertices, we observe relative throughputs reaching a
million times, relative to the non-incremental
algorithms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Luo:2016:QDS,
author = "Ge Luo and Lu Wang and Ke Yi and Graham Cormode",
title = "Quantiles over data streams: experimental comparisons,
new analyses, and further improvements",
journal = j-VLDB-J,
volume = "25",
number = "4",
pages = "449--472",
month = aug,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0424-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 21 06:41:51 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A fundamental problem in data management and analysis
is to generate descriptions of the distribution of
data. It is most common to give such descriptions in
terms of the cumulative distribution, which is
characterized by the quantiles of the data. The design
and engineering of efficient methods to find these
quantiles has attracted much study, especially in the
case where the data are given incrementally, and we
must compute the quantiles in an online, streaming
fashion. While such algorithms have proved to be
extremely useful in practice, there has been limited
formal comparison of the competing methods, and no
comprehensive study of their performance. In this
paper, we remedy this deficit by providing a taxonomy
of different methods and describe efficient
implementations. In doing so, we propose new variants
that have not been studied before, yet which outperform
existing methods. To illustrate this, we provide
detailed experimental comparisons demonstrating the
trade-offs between space, time, and accuracy for
quantile computation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xie:2016:EEI,
author = "Xike Xie and Benjin Mei and Jinchuan Chen and Xiaoyong
Du and Christian S. Jensen",
title = "{Elite}: an elastic infrastructure for big
spatiotemporal trajectories",
journal = j-VLDB-J,
volume = "25",
number = "4",
pages = "473--493",
month = aug,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0425-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 21 06:41:51 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "As the volumes of spatiotemporal trajectory data
continue to grow at a rapid pace; a new generation of
data management techniques is needed in order to be
able to utilize these data to provide a range of
data-driven services, including geographic-type
services. Key challenges posed by spatiotemporal data
include the massive data volumes, the high velocity
with which the data are captured, the need for
interactive response times, and the inherent inaccuracy
of the data. We propose an infrastructure, Elite, that
leverages peer-to-peer and parallel computing
techniques to address these challenges. The
infrastructure offers efficient, parallel update and
query processing by organizing the data into a layered
index structure that is logically centralized, but
physically distributed among computing nodes. The
infrastructure is elastic with respect to storage,
meaning that it adapts to fluctuations in the storage
volume, and with respect to computation, meaning that
the degree of parallelism can be adapted to best match
the computational requirements. Further, the
infrastructure offers advanced functionality, including
probabilistic simulations, for contending with the
inaccuracy of the underlying data in query processing.
Extensive empirical studies offer insight into
properties of the infrastructure and indicate that it
meets its design goals, thus enabling the effective
management of big spatiotemporal data.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kanza:2016:ESF,
author = "Yaron Kanza and Hadas Yaari",
title = "External sorting on flash storage: reducing cell
wearing and increasing efficiency by avoiding
intermediate writes",
journal = j-VLDB-J,
volume = "25",
number = "4",
pages = "495--518",
month = aug,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0426-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 21 06:41:51 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper studies the problem of how to conduct
external sorting on flash drives while avoiding
intermediate writes to the disk. The focus is on sort
in portable electronic devices, where relations are
only larger than the main memory by a small factor, and
on sort as part of distributed processes where
relations are frequently partially sorted. In such
cases, sort algorithms that refrain from writing
intermediate results to the disk have three advantages
over algorithms that perform intermediate writes.
First, on devices in which read operations are much
faster than writes, such methods are efficient and
frequently outperform Merge Sort. Secondly, they reduce
flash cell degradation caused by writes. Thirdly, they
can be used in cases where there is not enough disk
space for the intermediate results. Novel sort
algorithms that avoid intermediate writes to the disk
are presented. An experimental evaluation, on different
flash storage devices, shows that in many cases the new
algorithms can extend the lifespan of the devices by
avoiding unnecessary writes to the disk, while
maintaining efficiency, in comparison with Merge
Sort.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Jeon:2016:MBS,
author = "Inah Jeon and Evangelos E. Papalexakis and Christos
Faloutsos and Lee Sael and U. Kang",
title = "Mining billion-scale tensors: algorithms and
discoveries",
journal = j-VLDB-J,
volume = "25",
number = "4",
pages = "519--544",
month = aug,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0427-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 21 06:45:26 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "How can we analyze large-scale real-world data with
various attributes? Many real-world data (e.g., network
traffic logs, web data, social networks, knowledge
bases, and sensor streams) with multiple attributes are
represented as multi-dimensional arrays, called
tensors. For analyzing a tensor, tensor decompositions
are widely used in many data mining applications:
detecting malicious attackers in network traffic logs
(with source IP, destination IP, port-number,
timestamp), finding telemarketers in a phone call
history (with sender, receiver, date), and identifying
interesting concepts in a knowledge base (with subject,
object, relation). However, current tensor
decomposition methods do not scale to large and sparse
real-world tensors with millions of rows and columns
and `fibers.' In this paper, we propose HaTen2, a
distributed method for large-scale tensor
decompositions that runs on the MapReduce framework.
Our careful design and implementation of HaTen2
dramatically reduce the size of intermediate data and
the number of jobs leading to achieve high scalability
compared with the state-of-the-art method. Thanks to
HaTen2, we analyze big real-world sparse tensors that
cannot be handled by the current state of the art, and
discover hidden concepts.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Islam:2016:KYC,
author = "Md. Saiful Islam and Chengfei Liu",
title = "Know your customer: computing $k$-most promising
products for targeted marketing",
journal = j-VLDB-J,
volume = "25",
number = "4",
pages = "545--570",
month = aug,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0428-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 21 06:45:26 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The advancement of World Wide Web has revolutionized
the way the manufacturers can do business. The
manufacturers can collect customer preferences for
products and product features from their sales and
other product-related Web sites to enter and sustain in
the global market. For example, the manufactures can
make intelligent use of these customer preference data
to decide on which products should be selected for
targeted marketing. However, the selected products must
attract as many customers as possible to increase the
possibility of selling more than their respective
competitors. This paper addresses this kind of product
selection problem. That is, given a database of
existing products P from the competitors, a set of
company's own products Q, a dataset C of customer
preferences and a positive integer k, we want to find
k-most promising products (k-MPP) from Q with maximum
expected number of total customers for targeted
marketing. We model k-MPP query and propose an
algorithmic framework for processing such query and its
variants. Our framework utilizes grid-based data
partitioning scheme and parallel computing techniques
to realize k-MPP query. The effectiveness and
efficiency of the framework are demonstrated by
conducting extensive experiments with real and
synthetic datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kohler:2016:PCK,
author = "Henning K{\"o}hler and Uwe Leck and Sebastian Link and
Xiaofang Zhou",
title = "Possible and certain keys for {SQL}",
journal = j-VLDB-J,
volume = "25",
number = "4",
pages = "571--596",
month = aug,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0430-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 21 06:45:26 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Driven by the dominance of the relational model and
the requirements of modern applications, we revisit the
fundamental notion of a key in relational databases
with NULL. In SQL, primary key columns are NOT NULL,
and UNIQUE constraints guarantee uniqueness only for
tuples without NULL. We investigate the notions of
possible and certain keys, which are keys that hold in
some or all possible worlds that originate from an SQL
table, respectively. Possible keys coincide with
UNIQUE, thus providing a semantics for their syntactic
definition in the SQL standard. Certain keys extend
primary keys to include NULL columns and can uniquely
identify entities whenever feasible, while primary keys
may not. In addition to basic characterization,
axiomatization, discovery, and extremal combinatorics
problems, we investigate the existence and construction
of Armstrong tables, and describe an indexing scheme
for enforcing certain keys. Our experiments show that
certain keys with NULLs occur in real-world data, and
related computational problems can be solved
efficiently. Certain keys are therefore semantically
well founded and able to meet Codd's entity integrity
rule while handling high volumes of incomplete data
from different formats.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mottin:2016:HPA,
author = "Davide Mottin and Alice Marascu and Senjuti Basu Roy
and Gautam Das and Themis Palpanas and Yannis
Velegrakis",
title = "A holistic and principled approach for the
empty-answer problem",
journal = j-VLDB-J,
volume = "25",
number = "4",
pages = "597--622",
month = aug,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0431-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 21 06:45:26 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We propose a principled optimization-based interactive
query relaxation framework for queries that return no
answers. Given an initial query that returns an
empty-answer set, our framework dynamically computes
and suggests alternative queries with fewer conditions
than those the user has initially requested, in order
to help the user arrive at a query with a
non-empty-answer, or at a query for which no matter how
many additional conditions are ignored, the answer will
still be empty. Our proposed approach for suggesting
query relaxations is driven by a novel probabilistic
framework based on optimizing a wide variety of
application-dependent objective functions. We describe
optimal and approximate solutions of different
optimization problems using the framework. Moreover, we
discuss two important extensions to the base framework:
the specification of a minimum size on the number of
results returned by a relaxed query and the possibility
of proposing multiple conditions at the same time. We
analyze the proposed solutions, experimentally verify
their efficiency and effectiveness, and illustrate
their advantages over the existing approaches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Boncz:2016:SIM,
author = "Peter Boncz and Wolfgang Lehner and Thomas Neumann",
title = "Special Issue: Modern Hardware",
journal = j-VLDB-J,
volume = "25",
number = "5",
pages = "623--624",
month = oct,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0440-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Sep 12 18:50:32 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Porobic:2016:CIH,
author = "Danica Porobic and Ippokratis Pandis and Miguel Branco
and Pinar T{\"o}z{\"u}n and Anastasia Ailamaki",
title = "Characterization of the Impact of Hardware Islands on
{OLTP}",
journal = j-VLDB-J,
volume = "25",
number = "5",
pages = "625--650",
month = oct,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0413-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Sep 12 18:50:32 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Modern hardware is abundantly parallel and
increasingly heterogeneous. The numerous processing
cores have non-uniform access latencies to the main
memory and processor caches, which causes variability
in the communication costs. Unfortunately, database
systems mostly assume that all processing cores are the
same and that microarchitecture differences are not
significant enough to appear in critical database
execution paths. As we demonstrate in this paper,
however, non-uniform core topology does appear in the
critical path and conventional database architectures
achieve suboptimal and even worse, unpredictable
performance. We perform a detailed performance analysis
of OLTP deployments in servers with multiple cores per
CPU (multicore) and multiple CPUs per server
(multisocket). We compare different database deployment
strategies where we vary the number and size of
independent database instances running on a single
server, from a single shared-everything instance to
fine-grained shared-nothing configurations. We quantify
the impact of non-uniform hardware on various
deployments by (a) examining how efficiently each
deployment uses the available hardware resources and
(b) measuring the impact of distributed transactions
and skewed requests on different workloads. We show
that no strategy is optimal for all cases and that the
best choice depends on the combination of hardware
topology and workload characteristics. Finally, we
argue that transaction processing systems must be aware
of the hardware topology in order to achieve
predictably high performance.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sadoghi:2016:ESO,
author = "Mohammad Sadoghi and Kenneth A. Ross and Mustafa Canim
and Bishwaranjan Bhattacharjee",
title = "Exploiting {SSDs} in operational multiversion
databases",
journal = j-VLDB-J,
volume = "25",
number = "5",
pages = "651--672",
month = oct,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0410-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Sep 12 18:50:32 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Multiversion databases store both current and
historical data. Rows are typically annotated with
timestamps representing the period when the row is/was
valid. We develop novel techniques to reduce index
maintenance in multiversion databases, so that indexes
can be used effectively for analytical queries over
current data without being a heavy burden on
transaction throughput. To achieve this end, we
re-design persistent index data structures in the
storage hierarchy to employ an extra level of
indirection. The indirection level is stored on
solid-state disks that can support very fast random
I/Os, so that traversing the extra level of indirection
incurs a relatively small overhead. The extra level of
indirection dramatically reduces the number of magnetic
disk I/Os that are needed for index updates and
localizes maintenance to indexes on updated attributes.
Additionally, we batch insertions within the
indirection layer in order to reduce physical disk I/Os
for indexing new records. In this work, we further
exploit SSDs by introducing novel DeltaBlock techniques
for storing the recent changes to data on SSDs. Using
our DeltaBlock, we propose an efficient method to
periodically flush the recently changed data from SSDs
to HDDs such that, on the one hand, we keep track of
every change (or delta) for every record, and, on the
other hand, we avoid redundantly storing the unchanged
portion of updated records. By reducing the index
maintenance overhead on transactions, we enable
operational data stores to create more indexes to
support queries. We have developed a prototype of our
indirection proposal by extending the widely used
generalized search tree open-source project, which is
also employed in PostgreSQL. Our working implementation
demonstrates that we can significantly reduce index
maintenance and/or query processing cost by a factor of
3. For the insertion of new records, our novel batching
technique can save up to 90 \% of the insertion time.
For updates, our prototype demonstrates that we can
significantly reduce the database size by up to 80 \%
even with a modest space allocated for DeltaBlocks on
SSDs.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kang:2016:FCE,
author = "Woon-Hak Kang and Sang-Won Lee and Bongki Moon",
title = "Flash as cache extension for online transactional
workloads",
journal = j-VLDB-J,
volume = "25",
number = "5",
pages = "673--694",
month = oct,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0414-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Sep 12 18:50:32 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Considering the current price gap between hard disk
and flash memory SSD storages, for applications dealing
with large-scale data, it will be economically more
sensible to use flash memory drives to supplement disk
drives rather than to replace them. This paper presents
FaCE, which is a new low-overhead caching strategy that
uses flash memory as an extension to the RAM buffer of
database systems. FaCE aims at improving the
transaction throughput as well as shortening the
recovery time from a system failure. To achieve the
goals, we propose two novel algorithms for flash cache
management, namely multi-version FIFO replacement and
group second chance. This was possible due to flash
write optimization as well as disk access reduction
obtained by the FaCE caching methods. In addition, FaCE
takes advantage of the nonvolatility of flash memory to
fully support database recovery by extending the scope
of a persistent database to include the data pages
stored in the flash cache. We have implemented FaCE in
the PostgreSQL open-source database server and
demonstrated its effectiveness for TPC-C benchmarks in
comparison with existing caching methods such as Lazy
Cleaning and Linux Bcache.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Jin:2016:RWO,
author = "Peiquan Jin and Chengcheng Yang and Christian S.
Jensen and Puyuan Yang and Lihua Yue",
title = "Read\slash write-optimized tree indexing for
solid-state drives",
journal = j-VLDB-J,
volume = "25",
number = "5",
pages = "695--717",
month = oct,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0406-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Sep 12 18:50:32 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Flash-memory-based solid-state drives (SSDs) are used
widely for secondary storage. To be effective for SSDs,
traditional indices have to be redesigned to cope with
the special properties of flash memory, such as
asymmetric read/write latencies (fast reads and slow
writes) and out-of-place updates. Previous
flash-optimized indices focus mainly on reducing random
writes to SSDs, which is typically accomplished at the
expense of a substantial number of extra reads.
However, modern SSDs show a narrowing gap between read
and write speeds, and read operations on SSDs
increasingly affect the overall performance of indices
on SSDs. As a consequence, how to optimize SSD-aware
indices by reducing both write and read costs is a
pertinent and open challenge. We propose a new tree
index for SSDs that is able to reduce both writes and
extra reads. In particular, we use an update buffer and
overflow pages to reduce random writes, and we further
exploit Bloom filters to reduce the extra reads to the
overflow nodes in the tree. With this mechanism, we
construct a read/write-optimized index that is capable
of offering better overall performance than previous
flash-aware indices. In addition, we present an
analysis of the proposed index and show that the read
and write costs of the operations on the index can be
balanced by only tuning the false-positive rate of the
Bloom filters. Our experimental results suggest that
our proposal is efficient and represents an improvement
over existing methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sitaridi:2016:GAS,
author = "Evangelia A. Sitaridi and Kenneth A. Ross",
title = "{GPU}-accelerated string matching for database
applications",
journal = j-VLDB-J,
volume = "25",
number = "5",
pages = "719--740",
month = oct,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-015-0409-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Sep 12 18:50:32 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/string-matching.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Implementations of relational operators on GPU
processors have resulted in order of magnitude speedups
compared to their multicore CPU counterparts. Here we
focus on the efficient implementation of string
matching operators common in SQL queries. Due to
different architectural features the optimal algorithm
for CPUs might be suboptimal for GPUs. GPUs achieve
high memory bandwidth by running thousands of threads,
so it is not feasible to keep the working set of all
threads in the cache in a naive implementation. In GPUs
the unit of execution is a group of threads and in the
presence of loops and branches, threads in a group have
to follow the same execution path; if some threads
diverge, then different paths are serialized. We study
the cache memory efficiency of single- and
multi-pattern string matching algorithms for
conventional and pivoted string layouts in the GPU
memory. We evaluate the memory efficiency in terms of
memory access pattern and achieved memory bandwidth for
different parallelization methods. To reduce thread
divergence, we split string matching into multiple
steps. We evaluate the different matching algorithms in
terms of average- and worst-case performance and
compare them against state-of-the-art CPU and GPU
libraries. Our experimental evaluation shows that
thread and memory efficiency affect performance
significantly and that our proposed methods outperform
previous CPU and GPU algorithms in terms of raw
performance and power efficiency. The
Knuth---Morris---Pratt algorithm is a good choice for
GPUs because its regular memory access pattern makes it
amenable to several GPU optimizations.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mottin:2016:EQN,
author = "Davide Mottin and Matteo Lissandrini and Yannis
Velegrakis and Themis Palpanas",
title = "Exemplar queries: a new way of searching",
journal = j-VLDB-J,
volume = "25",
number = "6",
pages = "741--765",
month = dec,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0429-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Nov 10 18:03:04 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Modern search engines employ advanced techniques that
go beyond the structures that strictly satisfy the
query conditions in an effort to better capture the
user intentions. In this work, we introduce a novel
query paradigm that considers a user query as an
example of the data in which the user is interested. We
call these queries exemplar queries. We provide a
formal specification of their semantics and show that
they are fundamentally different from notions like
queries by example, approximate queries and related
queries. We provide an implementation of these
semantics for knowledge graphs and present an exact
solution with a number of optimizations that improve
performance without compromising the result quality. We
study two different congruence relations, isomorphism
and strong simulation, for identifying the answers to
an exemplar query. We also provide an approximate
solution that prunes the search space and achieves
considerably better time performance with minimal or no
impact on effectiveness. The effectiveness and
efficiency of these solutions with synthetic and real
datasets are experimentally evaluated, and the
importance of exemplar queries in practice is
illustrated.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2016:EDL,
author = "Yuhong Li and Leong Hou U. and Man Lung Yiu and Zhiguo
Gong",
title = "Efficient discovery of longest-lasting correlation in
sequence databases",
journal = j-VLDB-J,
volume = "25",
number = "6",
pages = "767--790",
month = dec,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0432-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Nov 10 18:03:04 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The search for similar subsequences is a core module
for various analytical tasks in sequence databases.
Typically, the similarity computations require users to
set a length. However, there is no robust means by
which to define the proper length for different
application needs. In this study, we examine a new
query that is capable of returning the longest-lasting
highly correlated subsequences in a sequence database,
which is particularly helpful to analyses without prior
knowledge regarding the query length. A baseline, yet
expensive, solution is to calculate the correlations
for every possible subsequence length. To boost
performance, we study a space-constrained index that
provides a tight correlation bound for subsequences of
similar lengths and offset by intraobject and
interobject grouping techniques. To the best of our
knowledge, this is the first index to support a
normalized distance metric of arbitrary length
subsequences. In addition, we study the use of a smart
cache for disk-resident data (e.g., millions of
sequence objects) and a graph processing unit-based
parallel processing technique for frequently updated
data (e.g., nonindexable streaming sequences) to
compute the longest-lasting highly correlated
subsequences. Extensive experimental evaluation on both
real and synthetic sequence datasets verifies the
efficiency and effectiveness of our proposed methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fakas:2016:DPS,
author = "Georgios J. Fakas and Zhi Cai and Nikos Mamoulis",
title = "Diverse and proportional size-$l$ object summaries
using pairwise relevance",
journal = j-VLDB-J,
volume = "25",
number = "6",
pages = "791--816",
month = dec,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0433-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Nov 10 18:03:04 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The abundance and ubiquity of graphs (e.g., online
social networks such as Google++ and Facebook;
bibliographic graphs such as DBLP) necessitates the
effective and efficient search over them. Given a set
of keywords that can identify a data subject (DS), a
recently proposed keyword search paradigm produces a
set of object summaries (OSs) as results. An OS is a
tree structure rooted at the DS node (i.e., a node
containing the keywords) with surrounding nodes that
summarize all data held on the graph about the DS. OS
snippets, denoted as size-l OSs, have also been
investigated. A size-l OS is a partial OS containing l
nodes such that the summation of their importance
scores results in the maximum possible total score.
However, the set of nodes that maximize the total
importance score may result in an uninformative size-l
OSs, as very important nodes may be repeated in it,
dominating other representative information. In view of
this limitation, in this paper, we investigate the
effective and efficient generation of two novel types
of OS snippets, i.e., diverse and proportional size-l
OSs, denoted as DSize-l and PSize-l OSs. Namely,
besides the importance of each node, we also consider
its pairwise relevance (similarity) to the other nodes
in the OS and the snippet. We conduct an extensive
evaluation on two real graphs (DBLP and Google++). We
verify effectiveness by collecting user feedback, e.g.,
by asking DBLP authors (i.e., the DSs themselves) to
evaluate our results. In addition, we verify the
efficiency of our algorithms and evaluate the quality
of the snippets that they produce.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{BOgh:2016:SPW,
author = "Kenneth S. B{\O}gh and Sean Chester and Ira Assent",
title = "{SkyAlign}: a portable, work-efficient skyline
algorithm for multicore and {GPU} architectures",
journal = j-VLDB-J,
volume = "25",
number = "6",
pages = "817--841",
month = dec,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0438-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Nov 10 18:03:04 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The skyline operator determines points in a
multidimensional dataset that offer some optimal
trade-off. State-of-the-art CPU skyline algorithms
exploit quad-tree partitioning with complex branching
to minimise the number of point-to-point comparisons.
Branch-phobic GPU skyline algorithms rely on compute
throughput rather than partitioning, but fail to match
the performance of sequential algorithms. In this
paper, we introduce a new skyline algorithm, SkyAlign,
that is designed for the GPU, and a GPU-friendly,
grid-based tree structure upon which the algorithm
relies. The search tree allows us to dramatically
reduce the amount of work done by the GPU algorithm by
avoiding most point-to-point comparisons at the cost of
some compute throughput. This trade-off allows SkyAlign
to achieve orders of magnitude faster performance than
its predecessors. Moreover, a NUMA-oblivious port of
SkyAlign outperforms native multicore state of the art
on challenging workloads by an increasing margin as
more cores and sockets are utilised.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zoumpatianos:2016:AAD,
author = "Kostas Zoumpatianos and Stratos Idreos and Themis
Palpanas",
title = "{ADS}: the adaptive data series index",
journal = j-VLDB-J,
volume = "25",
number = "6",
pages = "843--866",
month = dec,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0442-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Nov 10 18:03:04 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Numerous applications continuously produce big amounts
of data series, and in several time critical scenarios
analysts need to be able to query these data as soon as
they become available. This, however, is not currently
possible with the state-of-the-art indexing methods and
for very large data series collections. In this paper,
we present the first adaptive indexing mechanism,
specifically tailored to solve the problem of indexing
and querying very large data series collections. We
present a detailed design and evaluation of our method
using approximate and exact query algorithms with both
synthetic and real data sets. Adaptive indexing
significantly outperforms previous solutions,
gracefully handling large data series collections,
reducing the data to query delay: By the time
state-of-the-art indexing techniques finish indexing 1
billion data series (and before answering even a single
query), our method has already answered 3*10^53`?105
queries.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Liu:2016:AWW,
author = "Qing Liu and Yunjun Gao and Gang Chen and Baihua Zheng
and Linlin Zhou",
title = "Answering why-not and why questions on reverse top-$k$
queries",
journal = j-VLDB-J,
volume = "25",
number = "6",
pages = "867--892",
month = dec,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0443-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Nov 10 18:03:04 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Why-not and why questions can be posed by database
users to seek clarifications on unexpected query
results. Specifically, why-not questions aim to explain
why certain expected tuples are absent from the query
results, while why questions try to clarify why certain
unexpected tuples are present in the query results.
This paper systematically explores the why-not and why
questions on reverse top-$k$ queries, owing to its
importance in multi-criteria decision making. We first
formalize why-not questions on reverse top-$k$ queries,
which try to include the missing objects in the reverse
top-$k$ query results, and then, we propose a unified
framework called WQRTQ to answer why-not questions on
reverse top-$k$ queries. Our framework offers three
solutions to cater for different application scenarios.
Furthermore, we study why questions on reverse top-$k$
queries, which aim to exclude the undesirable objects
from the reverse top-$k$ query results, and extend the
framework WQRTQ to efficiently answer why questions on
reverse top-$k$ queries, which demonstrates the
flexibility of our proposed algorithms. Extensive
experimental evaluation with both real and synthetic
data sets verifies the effectiveness and efficiency of
the presented algorithms under various experimental
settings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2016:SSL,
author = "Yang Chen and Daisy Zhe Wang and Sean Goldberg",
title = "{ScaLeKB}: scalable learning and inference over large
knowledge bases",
journal = j-VLDB-J,
volume = "25",
number = "6",
pages = "893--918",
month = dec,
year = "2016",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0444-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Nov 10 18:03:04 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Recent years have seen a drastic rise in the
construction of web knowledge bases (e.g., Freebase,
YAGO, DBPedia). These knowledge bases store structured
information about real-world people, places,
organizations, etc. However, due to the limitations of
human knowledge, web corpora, and information
extraction algorithms, the knowledge bases are still
far from complete. To infer the missing knowledge, we
propose the Ontological Pathfinding (OP) algorithm to
mine first-order inference rules from these web
knowledge bases. The OP algorithm scales up via a
series of optimization techniques, including a new
parallel-rule-mining algorithm, a pruning strategy to
eliminate unsound and inefficient rules before applying
them, and a novel partitioning algorithm to break the
learning task into smaller independent sub-tasks.
Combining these techniques, we develop a first rule
mining system that scales to Freebase, the largest
public knowledge base with 112 million entities and 388
million facts. We mine 36,625 inference rules in 34 h;
no existing system achieves this scale. Based on the
mining algorithm and the optimizations, we develop an
efficient inference engine. As a result, we infer 0.9
billion new facts from Freebase in 17.19 h. We use
cross validation to evaluate the inferred facts and
estimate a degree of expansion by 0.6 over Freebase,
with a precision approaching 1.0. Our approach
outperforms state-of-the-art mining algorithms and
inference engines in terms of both performance and
quality.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2017:SIB,
author = "Chen Li and Volker Markl",
title = "Special issue on best papers of {VLDB 2015}",
journal = j-VLDB-J,
volume = "26",
number = "1",
pages = "1--2",
month = feb,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0450-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Mar 12 10:52:26 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See erratum \cite{Li:2017:ESI}.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2017:ESI,
author = "Chen Li and Volker Markl",
title = "Erratum to: {Special issue on best papers of VLDB
2015}",
journal = j-VLDB-J,
volume = "26",
number = "1",
pages = "3--3",
month = feb,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0458-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Mar 12 10:52:26 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See \cite{Li:2017:SIB}.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gatterbauer:2017:DPA,
author = "Wolfgang Gatterbauer and Dan Suciu",
title = "Dissociation and propagation for approximate lifted
inference with standard relational database management
systems",
journal = j-VLDB-J,
volume = "26",
number = "1",
pages = "5--30",
month = feb,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0434-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Mar 12 10:52:26 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Probabilistic inference over large data sets is a
challenging data management problem since exact
inference is generally \#P-hard and is most often
solved approximately with sampling-based methods today.
This paper proposes an alternative approach for
approximate evaluation of conjunctive queries with
standard relational databases: In our approach, every
query is evaluated entirely in the database engine by
evaluating a fixed number of query plans, each
providing an upper bound on the true probability, then
taking their minimum. We provide an algorithm that
takes into account important schema information to
enumerate only the minimal necessary plans among all
possible plans. Importantly, this algorithm is a strict
generalization of all known PTIME self-join-free
conjunctive queries: A query is in PTIME if and only if
our algorithm returns one single plan. Furthermore, our
approach is a generalization of a family of efficient
ranking methods from graphs to hypergraphs. We also
adapt three relational query optimization techniques to
evaluate all necessary plans very fast. We give a
detailed experimental evaluation of our approach and,
in the process, provide a new way of thinking about the
value of probabilistic methods over non-probabilistic
methods for ranking query answers. We also note that
the techniques developed in this paper apply
immediately to lifted inference from statistical
relational models since lifted inference corresponds to
PTIME plans in probabilistic databases.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2017:RBR,
author = "Jiexing Li and Jeffrey F. Naughton and Rimma V.
Nehme",
title = "Resource bricolage and resource selection for parallel
database systems",
journal = j-VLDB-J,
volume = "26",
number = "1",
pages = "31--54",
month = feb,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0435-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Mar 12 10:52:26 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Running parallel database systems in an environment
with heterogeneous resources has become increasingly
common, due to cluster evolution and increasing
interest in moving applications into public clouds.
Performance differences among machines in the same
cluster pose new challenges for parallel database
systems. First, for database systems running in a
heterogeneous cluster, the default uniform data
partitioning strategy may overload some of the slow
machines, while at the same time it may underutilize
the more powerful machines. Since the processing time
of a parallel query is determined by the slowest
machine, such an allocation strategy may result in a
significant query performance degradation. Second,
since machines might have varying resources or
performance, different choices of machines may lead to
different costs or performance for executing the same
workload. By carefully selecting the most suitable
machines for running a workload, we may achieve better
performance with the same budget, or we may meet the
same performance requirements with a lower cost. We
address these challenges by introducing techniques we
call resource bricolage and resource selection that
improve database performance in heterogeneous
environments. Our approaches quantify the performance
differences among machines with various resources as
they process workloads with diverse resource
requirements. For the purpose of better resource
utilization, we formalize the problem of minimizing
workload execution time and view it as an optimization
problem, and then, we employ linear programming to
obtain a recommended data partitioning scheme. For the
purpose of better resource selection, we formalize two
problems: One minimizes the total workload execution
time with a given budget, and the other minimizes the
total budget with a given performance target. We then
employ different mixed-integer programs to search for
the optimal resource selection decisions. We verify the
effectiveness of both resource bricolage and resource
selection techniques with an extensive experimental
study.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Finis:2017:OIS,
author = "Jan Finis and Robert Brunel and Alfons Kemper and
Thomas Neumann and Norman May and Franz Faerber",
title = "{Order Indexes}: supporting highly dynamic
hierarchical data in relational main-memory database
systems",
journal = j-VLDB-J,
volume = "26",
number = "1",
pages = "55--80",
month = feb,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0436-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Mar 12 10:52:26 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Maintaining and querying hierarchical data in a
relational database system is an important task in many
business applications. This task is especially
challenging when considering dynamic use cases with a
high rate of complex, possibly skewed structural
updates. Labeling schemes are widely considered the
indexing technique of choice for hierarchical data, and
many different schemes have been proposed. However,
they cannot handle dynamic use cases well due to
various problems, which we investigate in this paper.
We therefore propose Order Indexes--a dynamic
representation of the nested intervals encoding--which
offer competitive query performance, unprecedented
update efficiency, and robustness for highly dynamic
workloads.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sa:2017:IKB,
author = "Christopher Sa and Alex Ratner and Christopher R{\'e}
and Jaeho Shin and Feiran Wang and Sen Wu and Ce
Zhang",
title = "Incremental knowledge base construction using
{DeepDive}",
journal = j-VLDB-J,
volume = "26",
number = "1",
pages = "81--105",
month = feb,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0437-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Mar 12 10:52:26 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Populating a database with information from
unstructured sources--also known as knowledge base
construction (KBC)--is a long-standing problem in
industry and research that encompasses problems of
extraction, cleaning, and integration. In this work, we
describe DeepDive, a system that combines database and
machine learning ideas to help develop KBC systems, and
we present techniques to make the KBC process more
efficient. We observe that the KBC process is
iterative, and we develop techniques to incrementally
produce inference results for KBC systems. We propose
two methods for incremental inference, based,
respectively, on sampling and variational techniques.
We also study the trade-off space of these methods and
develop a simple rule-based optimizer. DeepDive
includes all of these contributions, and we evaluate
DeepDive on five KBC systems, showing that it can speed
up KBC inference tasks by up to two orders of magnitude
with negligible impact on quality.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Trummer:2017:MOP,
author = "Immanuel Trummer and Christoph Koch",
title = "Multi-objective parametric query optimization",
journal = j-VLDB-J,
volume = "26",
number = "1",
pages = "107--124",
month = feb,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0439-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Mar 12 10:52:26 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Classical query optimization compares query plans
according to one cost metric and associates each plan
with a constant cost value. In this paper, we introduce
the multi-objective parametric query optimization
(MPQO) problem where query plans are compared according
to multiple cost metrics and the cost of a given plan
according to a given metric is modeled as a function
that depends on multiple parameters. The cost metrics
may, for instance, include execution time or monetary
fees; a parameter may represent the selectivity of a
query predicate that is unspecified at optimization
time. MPQO generalizes parametric query optimization
(which allows multiple parameters but only one cost
metric) and multi-objective query optimization (which
allows multiple cost metrics but no parameters). We
formally analyze the novel MPQO problem and show why
existing algorithms are inapplicable. We present a
generic algorithm for MPQO and a specialized version
for MPQO with piecewise-linear plan cost functions. We
prove that both algorithms find all relevant query
plans and experimentally evaluate the performance of
our second algorithm in multiple scenarios.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Khayyat:2017:FSI,
author = "Zuhair Khayyat and William Lucia and Meghna Singh and
Mourad Ouzzani and Paolo Papotti and Jorge-Arnulfo
Quian{\'e}-Ruiz and Nan Tang and Panos Kalnis",
title = "Fast and scalable inequality joins",
journal = j-VLDB-J,
volume = "26",
number = "1",
pages = "125--150",
month = feb,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0441-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Mar 12 10:52:26 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Inequality joins, which is to join relations with
inequality conditions, are used in various
applications. Optimizing joins has been the subject of
intensive research ranging from efficient join
algorithms such as sort-merge join, to the use of
efficient indices such as B^+B+-tree, R^*R`?-tree and
Bitmap. However, inequality joins have received little
attention and queries containing such joins are notably
very slow. In this paper, we introduce fast inequality
join algorithms based on sorted arrays and
space-efficient bit-arrays. We further introduce a
simple method to estimate the selectivity of inequality
joins which is then used to optimize multiple predicate
queries and multi-way joins. Moreover, we study an
incremental inequality join algorithm to handle
scenarios where data keeps changing. We have
implemented a centralized version of these algorithms
on top of PostgreSQL, a distributed version on top of
Spark SQL, and an existing data cleaning system,
Nadeef. By comparing our algorithms against well-known
optimization techniques for inequality joins, we show
our solution is more scalable and several orders of
magnitude faster.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yang:2017:RKN,
author = "Shiyu Yang and Muhammad Aamir Cheema and Xuemin Lin
and Ying Zhang and Wenjie Zhang",
title = "Reverse $k$ nearest neighbors queries and spatial
reverse top-$k$ queries",
journal = j-VLDB-J,
volume = "26",
number = "2",
pages = "151--176",
month = apr,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0445-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Mar 27 20:55:44 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Given a set of facilities and a set of users, a
reverse k nearest neighbors (RkNN) query q returns
every user for which the query facility is one of the k
closest facilities. Almost all of the existing
techniques to answer RkNN queries adopt a
pruning-and-verification framework. Regions-based
pruning and half-space pruning are the two most notable
pruning strategies. The half-space-based approach
prunes a larger area and is generally believed to be
superior. Influenced by this perception, almost all
existing RkNN algorithms utilize and improve the
half-space pruning strategy. We observe the weaknesses
and strengths of both strategies and discover that the
regions-based pruning has certain strengths that have
not been exploited in the past. Motivated by this, we
present a new regions-based pruning algorithm called
Slice that utilizes the strength of regions-based
pruning and overcomes its limitations. We also study
spatial reverse top-$k$ (SRTk) queries that return
every user u for which the query facility is one of the
top-$k$ facilities according to a given linear scoring
function. We first extend half-space-based pruning to
answer SRTk queries. Then, we propose a novel
regions-based pruning algorithm following Slice
framework to solve the problem. Our extensive
experimental study on synthetic and real data sets
demonstrates that Slice is significantly more efficient
than all existing RkNN and SRTk algorithms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2017:DBQ,
author = "Kun Li and Xiaofeng Zhou and Daisy Zhe Wang and
Christan Grant and Alin Dobra and Christopher Dudley",
title = "In-database batch and query-time inference over
probabilistic graphical models using {UDA} --- {GIST}",
journal = j-VLDB-J,
volume = "26",
number = "2",
pages = "177--201",
month = apr,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0446-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Mar 27 20:55:44 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "To meet customers' pressing demands, enterprise
database vendors have been pushing advanced analytical
techniques into databases. Most major DBMSes use
user-defined aggregates (UDAs), a data-driven operator,
to implement analytical techniques in parallel.
However, UDAs alone are not sufficient to implement
statistical algorithms where most of the work is
performed by iterative transitions over a large state
that cannot be naively partitioned due to data
dependency. Typically, this type of statistical
algorithm requires pre-processing to set up the large
state in the first place and demands post-processing
after the statistical inference. This paper presents
general iterative state transition (GIST), a new
database operator for parallel iterative state
transitions over large states. GIST receives a state
constructed by a UDA and then performs rounds of
transitions on the state until it converges. A final
UDA performs post-processing and result extraction. We
argue that the combination of UDA and GIST (UDA---GIST)
unifies data-parallel and state-parallel processing in
a single system, thus significantly extending the
analytical capabilities of DBMSes. We exemplify the
framework through two high-profile batch applications:
cross-document coreference, image denoising and one
query-time inference application: marginal inference
queries over probabilistic knowledge graphs. The 3
applications use probabilistic graphical models, which
encode complex relationships of different variables and
are powerful for a wide range of problems. We show that
the in-database framework allows us to tackle a 27
times larger problem than a scalable distributed
solution for the first application and achieves 43
times speedup over the state-of-the-art for the second
application. For the third application, we implement
query-time inference using the UDA---GIST framework and
apply over a probabilistic knowledge graph, achieving
10 times speedup over sequential inference. To the best
of our knowledge, this is the first in-database
query-time inference engine over large probabilistic
knowledge base. We show that the UDA---GIST framework
for data- and graph-parallel computations can support
both batch and query-time inference efficiently in
databases.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xie:2017:PTP,
author = "Miao Xie and Sourav S. Bhowmick and Gao Cong and Qing
Wang",
title = "{PANDA}: toward partial topology-based search on large
networks in a single machine",
journal = j-VLDB-J,
volume = "26",
number = "2",
pages = "203--228",
month = apr,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0447-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Mar 27 20:55:44 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A large body of research has focused on efficient and
scalable processing of subgraph search queries on large
networks. In these efforts, a query is posed in the
form of a connected query graph. Unfortunately, in
practice end users may not always have precise
knowledge about the topological relationships between
nodes in a query graph to formulate a connected query.
In this paper, we present a novel graph querying
paradigm called partial topology-based network search
and propose a query processing framework called panda
to efficiently process partial topology query (ptq) in
a single machine. A ptq is a disconnected query graph
containing multiple connected query components. ptqs
allow an end user to formulate queries without
demanding precise information about the complete
topology of a query graph. To this end, we propose an
exact and an approximate algorithm called sen-panda and
po-panda, respectively, to generate top-$k$ matches of
a ptq. We also present a subgraph simulation-based
optimization technique to further speedup the
processing of ptqs. Using real-life networks with
millions of nodes, we experimentally verify that our
proposed algorithms are superior to several baseline
techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yang:2017:SPM,
author = "Mohan Yang and Alexander Shkapsky and Carlo Zaniolo",
title = "Scaling up the performance of more powerful {Datalog}
systems on multicore machines",
journal = j-VLDB-J,
volume = "26",
number = "2",
pages = "229--248",
month = apr,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0448-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Mar 27 20:55:44 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Extending RDBMS technology to achieve performance and
scalability for queries that are much more powerful
than those of SQL-2 has been the goal of deductive
database research for more than thirty years. The
\mathcal{D}e\mathcal{A}\mathcal{L}\mathcal{S}DeALS
system has made major progress toward this goal, by (1)
Datalog extensions that support the more powerful
recursive queries needed in advanced applications, and
(2) superior performance for both traditional recursive
queries and those made possible by the new extensions,
while (3) delivering competitive performance with
commercial RDBMSs on non-recursive queries. In this
paper, we focus on the techniques used to support the
in-memory evaluation of Datalog programs on multicore
machines. In \mathcal{D}e\mathcal{A}\mathcal
{L}\mathcal{S}DeALS, a Datalog program is represented
as an AND/OR tree, and multiple copies of the same
AND/OR tree are used to access the tables in the
database concurrently during the parallel evaluation.
We describe compilation techniques that (1) recognize
when the given program is lock-free, (2) transform a
locking program into a lock-free program, and (3) find
an efficient parallel plan that correctly evaluates the
program while minimizing the use of locks and other
overhead required for parallel evaluation. Extensive
experiments demonstrate the effectiveness of the
proposed techniques.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yu:2017:UFS,
author = "Minghe Yu and Jin Wang and Guoliang Li and Yong Zhang
and Dong Deng and Jianhua Feng",
title = "A unified framework for string similarity search with
edit-distance constraint",
journal = j-VLDB-J,
volume = "26",
number = "2",
pages = "249--274",
month = apr,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0449-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Mar 27 20:55:44 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "String similarity search is a fundamental operation in
data cleaning and integration. It has two variants:
threshold-based string similarity search and top-$ k k
$ string similarity search. Existing algorithms are
efficient for either the former or the latter; most of
them cannot support both two variants. To address this
limitation, we propose a unified framework. We first
recursively partition strings into disjoint segments
and build a hierarchical segment tree index ({\textsf
{HS}}{\text {-}}{\textsf {Tree}}HS-Tree) on top of the
segments. Then, we utilize the {\textsf {HS}}{\text
{-}}{\textsf {Tree}}HS-Tree to support similarity
search. For threshold-based search, we identify
appropriate tree nodes based on the threshold to answer
the query and devise an efficient algorithm
(HS-Search). For top-$ k k $ search, we identify
promising strings with large possibility to be similar
to the query, utilize these strings to estimate an
upper bound which is used to prune dissimilar strings
and propose an algorithm (HS-Topk). We develop
effective pruning techniques to further improve the
performance. To support large data sets, we extend our
techniques to support the disk-based setting.
Experimental results on real-world data sets show that
our method achieves high performance on the two
problems and outperforms state-of-the-art algorithms by
5---10 times.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yuan:2017:EEG,
author = "Long Yuan and Lu Qin and Xuemin Lin and Lijun Chang
and Wenjie Zhang",
title = "{I/O} efficient {ECC} graph decomposition via graph
reduction",
journal = j-VLDB-J,
volume = "26",
number = "2",
pages = "275--300",
month = apr,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0451-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Mar 27 20:55:44 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The problem of computing $k$-edge connected components
($k$-\mathsf {ECC}ECCs) of a graph G for a specific $k$
is a fundamental graph problem and has been
investigated recently. In this paper, we study the
problem of \mathsf {ECC}ECC decomposition, which
computes the $k$-\mathsf {ECC}ECCs of a graph G for all
possible k values. \mathsf {ECC}ECC decomposition can
be widely applied in a variety of applications such as
graph-topology analysis, community detection, Steiner
Component Search, and graph visualization. A
straightforward solution for \mathsf {ECC}ECC
decomposition is to apply the existing $k$-\mathsf
{ECC}ECC computation algorithm to compute the
$k$-\mathsf {ECC}ECCs for all $k$ values. However, this
solution is not applicable to large graphs for two
challenging reasons. First, all existing $k$-\mathsf
{ECC}ECC computation algorithms are highly memory
intensive due to the complex data structures used in
the algorithms. Second, the number of possible $k$
values can be very large, resulting in a high
computational cost when each $k$ value is independently
considered. In this paper, we address the above
challenges, and study I/O efficient \mathsf {ECC}ECC
decomposition via graph reduction. We introduce two
elegant graph reduction operators which aim to reduce
the size of the graph loaded in memory while preserving
the connectivity information of a certain set of edges
to be computed for a specific k. We also propose three
novel I/O efficient algorithms, \mathsf{Bottom}-\mathsf
{Up}, \mathsf {Top}-\mathsf {Down}, and \mathsf
{Hybrid}, that explore the $k$ values in different
orders to reduce the redundant computations between
different $k$ values. We analyze the I/O and memory
costs for all proposed algorithms. In addition, we
extend our algorithm to build an efficient index for
Steiner Component Search. We show that our index can be
used to perform Steiner Component Search in optimal
I/Os when only the node information of the graph is
allowed to be loaded in memory. In our experiments, we
evaluate our algorithms using seven real large datasets
with various graph properties, one of which contains
1.95 billion edges. The experimental results show that
our proposed algorithms are scalable and efficient.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2017:TKS,
author = "Xiang Wang and Wenjie Zhang and Ying Zhang and Xuemin
Lin and Zengfeng Huang",
title = "Top-$k$ spatial-keyword publish\slash subscribe over
sliding window",
journal = j-VLDB-J,
volume = "26",
number = "3",
pages = "301--326",
month = jun,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0453-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 24 11:54:27 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "With the prevalence of social media and GPS-enabled
devices, a massive amount of geo-textual data have been
generated in a stream fashion, leading to a variety of
applications such as location-based recommendation and
information dissemination. In this paper, we
investigate a novel real-time top-kk monitoring problem
over sliding window of streaming data; that is, we
continuously maintain the top-$k$ most relevant
geo-textual messages (e.g., geo-tagged tweets) for a
large number of spatial-keyword subscriptions (e.g.,
registered users interested in local events)
simultaneously. To provide the most recent information
under controllable memory cost, sliding window model is
employed on the streaming geo-textual data. To the best
of our knowledge, this is the first work to study
top-kk spatial-keyword publish/subscribe over sliding
window. A novel centralized system, called Skype
(Top-kSpatial-keyword Publish/Subscribe), is proposed
in this paper. In Skype, to continuously maintain
top-kk results for massive subscriptions, we devise a
novel indexing structure upon subscriptions such that
each incoming message can be immediately delivered on
its arrival. To reduce the expensive top-kk
re-evaluation cost triggered by message expiration, we
develop a novel cost-basedk-skyband technique to reduce
the number of re-evaluations in a cost-effective way.
Extensive experiments verify the great efficiency and
effectiveness of our proposed techniques. Furthermore,
to support better scalability and higher throughput, we
propose a distributed version of Skype, namely DSkype,
on top of Storm, which is a popular distributed stream
processing system. With the help of fine-tuned
subscription/message distribution mechanisms, DSkype
can achieve orders of magnitude speed-up than its
centralized version.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gao:2017:PBH,
author = "Jun Gao and Yuqiong Liu and Chang Zhou and Jeffrey Xu
Yu",
title = "Path-based holistic detection plan for multiple
patterns in distributed graph frameworks",
journal = j-VLDB-J,
volume = "26",
number = "3",
pages = "327--345",
month = jun,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-016-0452-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 24 11:54:27 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Multiple pattern detection is needed in applications
like disease analysis over gene networks, bug detection
in program flow networks. This paper takes pattern
detection to investigate the evaluation and
optimization of multiple jobs in existing distributed
graph processing frameworks. The evaluation plan for
multiple pattern detection should be parallelizable and
can capture and reuse the shared parts among pattern
queries easily. In this paper, we design a path-based
holistic plan for multiple pattern queries.
Specifically, (1) we design a path-based edge-covered
plan for an individual pattern. The paths in the plan
can be easily captured and reused among different
queries. Additionally, the evaluation plan is fully
parallelizable, in which each data vertex performs
necessary join operations independently during
exploring graph. (2) We extend the individual plan to a
holistic evaluation plan for multiple queries, whose
results are equivalent to those of individual queries.
The plan reduces the overall cost by finding frequent
paths among queries and reusing the shared part in the
holistic plan. (3) We devise various optimization
strategies over the holistic plan. The experimental
studies, conducted on Giraph, illustrate the high
effectiveness of our holistic approaches.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yi:2017:AVQ,
author = "Peipei Yi and Byron Choi and Sourav S. Bhowmick and
Jianliang Xu",
title = "{AutoG}: a visual query autocompletion framework for
graph databases",
journal = j-VLDB-J,
volume = "26",
number = "3",
pages = "347--372",
month = jun,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0454-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 24 11:54:27 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Composing queries is evidently a tedious task. This is
particularly true of graph queries as they are
typically complex and prone to errors, compounded by
the fact that graph schemas can be missing or too loose
to be helpful for query formulation. Despite the great
success of query formulation aids, in particular,
automatic query completion, graph query autocompletion
has received much less research attention. In this
paper, we propose a novel framework for subgraph query
autocompletion (called AutoG). Given an initial query q
and a user's preference as input, AutoG returns ranked
query suggestions Q'Q'z as output. Users may choose a
query from Q'Q'z and iteratively apply AutoG to compose
their queries. The novelties of AutoG are as follows:
First, we formalize query composition. Second, we
propose to increment a query with the logical units
called c-prime features that are (i) frequent subgraphs
and (ii) constructed from smaller c-prime features in
no more than c ways. Third, we propose algorithms to
rank candidate suggestions. Fourth, we propose a novel
index called feature Dag (FDag) to optimize the
ranking. We study the query suggestion quality with
simulations and real users and conduct an extensive
performance evaluation. The results show that the query
suggestions are useful (saved roughly 40\% of users'
mouse clicks), and AutoG returns suggestions shortly
under a large variety of parameter settings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Aljubayrin:2017:FLC,
author = "Saad Aljubayrin and Jianzhong Qi and Christian S.
Jensen and Rui Zhang and Zhen He and Yuan Li",
title = "Finding lowest-cost paths in settings with safe and
preferred zones",
journal = j-VLDB-J,
volume = "26",
number = "3",
pages = "373--397",
month = jun,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0455-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 24 11:54:27 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We define and study Euclidean and spatial network
variants of a new path finding problem: given a set of
safe or preferred zones with zero or low cost, find
paths that minimize the cost of travel from an origin
to a destination. In this problem, the entire space is
passable, with preference given to safe or preferred
zones. Existing algorithms for problems that involve
unsafe regions to be avoided strictly are not effective
for this new problem. To solve the Euclidean variant,
we devise a transformation of the continuous data space
with safe zones into a discrete graph upon which
shortest path algorithms apply. A naive transformation
yields a large graph that is expensive to search. In
contrast, our transformation exploits properties of
hyperbolas in Euclidean space to safely eliminate graph
edges, thus improving performance without affecting
correctness. To solve the spatial network variant, we
propose a different graph-to-graph transformation that
identifies critical points that serve the same purpose
as do the hyperbolas, thus also avoiding the extraneous
edges. Having solved the problem for safe zones with
zero costs, we extend the transformations to the
weighted version of the problem, where travel in
preferred zones has nonzero costs. Experiments on both
real and synthetic data show that our approaches
outperform baseline approaches by more than an order of
magnitude in graph construction time, storage space,
and query response time.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2017:DSP,
author = "Dongxiang Zhang and Dingyu Yang and Yuan Wang and
Kian-Lee Tan and Jian Cao and Heng Tao Shen",
title = "Distributed shortest path query processing on dynamic
road networks",
journal = j-VLDB-J,
volume = "26",
number = "3",
pages = "399--419",
month = jun,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0457-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 24 11:54:27 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Shortest path query processing on dynamic road
networks is a fundamental component for real-time
navigation systems. In the face of an enormous volume
of customer demand from Uber and similar apps, it is
desirable to study distributed shortest path query
processing that can be deployed on elastic and
fault-tolerant cloud platforms. In this paper, we
combine the merits of distributed streaming computing
systems and lightweight indexing to build an efficient
shortest path query processing engine on top of Yahoo
S4. We propose two types of asynchronous communication
algorithms for early termination. One is
first-in-first-out message propagation with certain
optimizations, and the other is prioritized message
propagation with the help of navigational intelligence.
Extensive experiments were conducted on large-scale
real road networks, and the results show that the query
efficiency of our methods can meet the real-time
requirement and is superior to Pregel and Pregel+. The
source code of our system is publicly available at
https://github.com/yangdingyu/cands.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lai:2017:SSE,
author = "Longbin Lai and Lu Qin and Xuemin Lin and Lijun
Chang",
title = "Scalable subgraph enumeration in {MapReduce}: a
cost-oriented approach",
journal = j-VLDB-J,
volume = "26",
number = "3",
pages = "421--446",
month = jun,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0459-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 24 11:54:27 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Subgraph enumeration, which aims to find all the
subgraphs of a large data graph that are isomorphic to
a given pattern graph, is a fundamental graph problem
with a wide range of applications. However, existing
sequential algorithms for subgraph enumeration fall
short in handling large graphs due to the involvement
of computationally intensive subgraph isomorphism
operations. Thus, some recent researches focus on
solving the problem using MapReduce. Nevertheless,
exiting MapReduce approaches are not scalable to handle
very large graphs since they either produce a huge
number of partial results or consume a large amount of
memory. Motivated by this, in this paper, we propose a
new algorithm \mathsf {Twin}Twin\mathsf
{Twig}Twig\mathsf {Join}Join based on a left-deep-join
framework in MapReduce, in which the basic join unit is
a \mathsf {Twin}Twin\mathsf {Twig}Twig (an edge or two
incident edges of a node). We show that in the
Erd{\"o}s---R{\'e}nyi random graph model, \mathsf
{Twin}Twin\mathsf {Twig}Twig\mathsf {Join}Join is
instance optimal in the left-deep-join framework under
reasonable assumptions, and we devise an algorithm to
compute the optimal join plan. We further discuss how
our approach can be adapted to handle the power-law
random graph model. Three optimization strategies are
explored to improve our algorithm. Ultimately, by
aggregating equivalent nodes into a compressed node, we
construct the compressed graph, upon which the subgraph
enumeration is further improved. We conduct extensive
performance studies in several real graphs, one of
which contains billions of edges. Our approach
significantly outperforms existing solutions in all
tests.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Cafagna:2017:DIP,
author = "Francesco Cafagna and Michael H. B{\"o}hlen",
title = "Disjoint interval partitioning",
journal = j-VLDB-J,
volume = "26",
number = "3",
pages = "447--466",
month = jun,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0456-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 24 11:54:27 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In databases with time interval attributes, query
processing techniques that are based on sort-merge or
sort-aggregate deteriorate. This happens because for
intervals no total order exists and either the start or
end point is used for the sorting. Doing so leads to
inefficient solutions with lots of unproductive
comparisons that do not produce an output tuple. Even
if just one tuple with a long interval is present in
the data, the number of unproductive comparisons of
sort-merge and sort-aggregate gets quadratic. In this
paper we propose disjoint interval partitioning
(\mathcal {DIP}DIP), a technique to efficiently perform
sort-based operators on interval data. \mathcal
{DIP}DIP divides an input relation into the minimum
number of partitions, such that all tuples in a
partition are non-overlapping. The absence of
overlapping tuples guarantees efficient sort-merge
computations without backtracking. With \mathcal
{DIP}DIP the number of unproductive comparisons is
linear in the number of partitions. In contrast to
current solutions with inefficient random accesses to
the active tuples, \mathcal {DIP}DIP fetches the tuples
in a partition sequentially. We illustrate the
generality and efficiency of \mathcal {DIP}DIP by
describing and evaluating three basic database
operators over interval data: join, anti-join and
aggregation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gao:2017:EFR,
author = "Yunjun Gao and Xiaoye Miao and Gang Chen and Baihua
Zheng and Deng Cai and Huiyong Cui",
title = "On efficiently finding reverse $k$-nearest neighbors
over uncertain graphs",
journal = j-VLDB-J,
volume = "26",
number = "4",
pages = "467--492",
month = aug,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0460-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 27 16:38:23 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Reverse $k$-nearest neighbor (\hbox {R}k\hbox
{NN}RkNN) query on graphs returns the data objects that
take a specified query object q as one of their
$k$-nearest neighbors. It has significant influence in
many real-life applications including resource
allocation and profile-based marketing. However, to the
best of our knowledge, there is little previous work on
\hbox {R}k\hbox {NN}RkNN search over uncertain graph
data, even though many complex networks such as traffic
networks and protein---protein interaction networks are
often modeled as uncertain graphs. In this paper, we
systematically study the problem of reverse $k$-nearest
neighbor search on uncertain graphs (\hbox {UG-R}k\hbox
{NN}UG-RkNN search for short), where graph edges
contain uncertainty. First, to address \hbox
{UG-R}k\hbox {NN}UG-RkNN search, we propose three
effective heuristics, i.e., GSP, EGR, and PBP, which
minimize the original large uncertain graph as a much
smaller essential uncertain graph, cut down the number
of possible graphs via the newly introduced graph
conditional dominance relationship, and reduce the
validation cost of data nodes in order to improve query
efficiency. Then, we present an efficient algorithm,
termed as SDP, to support \hbox {UG-R}k\hbox
{NN}UG-RkNN retrieval by seamlessly integrating the
three heuristics together. In view of the high
complexity of \hbox {UG-R}k\hbox {NN}UG-RkNN search, we
further present a novel algorithm called TripS, with
the help of an adaptive stratified sampling technique.
Extensive experiments using both real and synthetic
graphs demonstrate the performance of our proposed
algorithms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Tao:2017:SSW,
author = "Yufei Tao and Xiaocheng Hu and Miao Qiao",
title = "Stream sampling over windows with worst-case
optimality and $ \ell $-overlap independence",
journal = j-VLDB-J,
volume = "26",
number = "4",
pages = "493--510",
month = aug,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0461-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 27 16:38:23 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Sampling provides fundamental support to numerous
applications that cannot afford to materialize all the
objects arriving at a rapid speed. Existing stream
sampling algorithms guarantee small space and query
overhead, but all require worst-case update time
proportional to the number of samples. This creates a
performance issue when a large sample set is required.
In this paper, we propose a new sampling algorithm that
is optimal simultaneously in all the three aspects:
space, query time, and update time. In particular, the
algorithm handles an update in $ O(1) $ worst-case time
with a very small hidden constant. Our algorithm also
ensures a strong independence guarantee: the sample
sets of all the queries are mutually independent as
long as the overlap between two query windows is
small.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Nguyen:2017:ADC,
author = "Quoc Viet Nguyen and Chi Thang Duong and Thanh Tam
Nguyen and Matthias Weidlich and Karl Aberer and
Hongzhi Yin and Xiaofang Zhou",
title = "Argument discovery via crowdsourcing",
journal = j-VLDB-J,
volume = "26",
number = "4",
pages = "511--535",
month = aug,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0462-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 27 16:38:23 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The amount of controversial issues being discussed on
the Web has been growing dramatically. In articles,
blogs, and wikis, people express their points of view
in the form of arguments, i.e., claims that are
supported by evidence. Discovery of arguments has a
large potential for informing decision-making. However,
argument discovery is hindered by the sheer amount of
available Web data and its unstructured, free-text
representation. The former calls for automatic
text-mining approaches, whereas the latter implies a
need for manual processing to extract the structure of
arguments. In this paper, we propose a
crowdsourcing-based approach to build a corpus of
arguments, an argumentation base, thereby mediating the
trade-off of automatic text-mining and manual
processing in argument discovery. We develop an
end-to-end process that minimizes the crowd cost while
maximizing the quality of crowd answers by: (1) ranking
argumentative texts, (2) pro-actively eliciting user
input to extract arguments from these texts, and (3)
aggregating heterogeneous crowd answers. Our
experiments with real-world datasets highlight that our
method discovers virtually all arguments in documents
when processing only 25\% of the text with more than
80\% precision, using only 50\% of the budget consumed
by a baseline algorithm.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2017:EMA,
author = "Tianzheng Wang and Ryan Johnson and Alan Fekete and
Ippokratis Pandis",
title = "Efficiently making (almost) any concurrency control
mechanism serializable",
journal = j-VLDB-J,
volume = "26",
number = "4",
pages = "537--562",
month = aug,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0463-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 27 16:38:23 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See erratum \cite{Wang:2018:EEM}.",
abstract = "Concurrency control (CC) algorithms must trade off
strictness for performance. In particular, serializable
CC schemes generally pay higher cost to prevent
anomalies, both in runtime overhead such as the
maintenance of lock tables and in efforts wasted by
aborting transactions. We propose the serial safety net
(SSN), a serializability-enforcing certifier which can
be applied on top of various CC schemes that offer
higher performance but admit anomalies, such as
snapshot isolation and read committed. The underlying
CC mechanism retains control of scheduling and
transactional accesses, while SSN tracks the resulting
dependencies. At commit time, SSN performs a validation
test by examining only direct dependencies of the
committing transaction to determine whether it can
commit safely or must abort to avoid a potential
dependency cycle. SSN performs robustly for a variety
of workloads. It maintains the characteristics of the
underlying CC without biasing toward a certain type of
transactions, though the underlying CC scheme might.
Besides traditional OLTP workloads, SSN also
efficiently handles heterogeneous workloads which
include a significant portion of long, read-mostly
transactions. SSN can avoid tracking the vast majority
of reads (thus reducing the overhead of serializability
certification) and still produce serializable
executions with little overhead. The dependency
tracking and validation tests can be done efficiently,
fully parallel and latch-free, for multi-version
systems on modern hardware with substantial core count
and large main memory. We demonstrate the efficiency,
accuracy and robustness of SSN using extensive
simulations and an implementation that overlays
snapshot isolation in ERMIA, a memory-optimized OLTP
engine that supports multiple CC schemes. Evaluation
results confirm that SSN is a promising approach to
serializability with robust performance and low
overhead for various workloads.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhu:2017:EAT,
author = "Qiankun Zhu and Hong Cheng and Xin Huang",
title = "{I/O}-efficient algorithms for top-$k$ nearest keyword
search in massive graphs",
journal = j-VLDB-J,
volume = "26",
number = "4",
pages = "563--583",
month = aug,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0464-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 27 16:38:23 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Networks emerging nowadays usually have labels or
textual content on the nodes. We model such commonly
seen network as an undirected graph G, in which each
node is attached with zero or more keywords, and each
edge is assigned with a length. On such networks, a
novel and useful query is called top-k nearest keyword
(\mathsf {k\text {-}NK}k-NK) search. Given a query node
q in G and a keyword \lambda `?, a \mathsf {k\text
{-}NK}k-NK query searches k nodes which contain \lambda
`? and are nearest to q. The \mathsf {k\text {-}NK}k-NK
problem has been studied recently in the literature.
But most existing solutions assume that the graph as
well as the constructed index can fit entirely in
memory. As a result, they cannot be applied directly to
very large-scale networks which are commonly found in
practice, but cannot fit in memory. In this work, we
design an I/O-efficient solution, which uses a compact
disk index to answer a \mathsf {k\text {-}NK}k-NK query
with constant I/Os. The key to an accurate \mathsf
{k\text {-}NK}k-NK result is a precise shortest
distance estimation in a graph. In our solution, we
follow our previous work Qiao et al. (PVLDB
6:901---912, 2013) which uses the shortest path tree as
an approximate representation of a graph and uses the
tree distance between two nodes as an accurate
estimation of the shortest distance between them on a
graph. With such representation, the original \mathsf
{k\text {-}NK}k-NK query on a graph can be reduced to
answering the query on a set of trees and then
assembling the results obtained from the trees. We
exploit a compact tree-based index and study how to lay
out the index to disk. We design a novel technique
which decomposes the index tree into paths and subtrees
and stores them in disk. Our theoretical analysis shows
that the disk-based index is small in size and supports
constant query I/Os. Extensive experimental study on
massive trees and graphs with billions of edges and
keywords verifies our theoretical findings and
demonstrates the superiority of our method over the
state-of-the-art methods in the literature.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2017:IMU,
author = "Lu Chen and Yunjun Gao and Aoxiao Zhong and Christian
S. Jensen and Gang Chen and Baihua Zheng",
title = "Indexing metric uncertain data for range queries and
range joins",
journal = j-VLDB-J,
volume = "26",
number = "4",
pages = "585--610",
month = aug,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0465-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jul 27 16:38:23 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Range queries and range joins in metric spaces have
applications in many areas, including GIS,
computational biology, and data integration, where
metric uncertain data exist in different forms,
resulting from circumstances such as equipment
limitations, high-throughput sequencing technologies,
and privacy preservation. We represent metric uncertain
data by using an object-level model and a bi-level
model, respectively. Two novel indexes, the uncertain
pivot B^{+}+-tree (UPB-tree) and the uncertain pivot
B^{+}+-forest (UPB-forest), are proposed in order to
support probabilistic range queries and range joins for
a wide range of uncertain data types and similarity
metrics. Both index structures use a small set of
effective pivots chosen based on a newly defined
criterion and employ the B^{+}+-tree(s) as the
underlying index. In addition, we present efficient
metric probabilistic range query and metric
probabilistic range join algorithms, which utilize
validation and pruning techniques based on derived
probability lower and upper bounds. Extensive
experiments with both real and synthetic data sets
demonstrate that, compared against existing
state-of-the-art indexes for metric uncertain data, the
UPB-tree and the UPB-forest incur much lower
construction costs, consume less storage space, and can
support more efficient metric probabilistic range
queries and metric probabilistic range joins.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Song:2017:GRU,
author = "Shaoxu Song and Boge Liu and Hong Cheng and Jeffrey Xu
Yu and Lei Chen",
title = "Graph repairing under neighborhood constraints",
journal = j-VLDB-J,
volume = "26",
number = "5",
pages = "611--635",
month = oct,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0466-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 2 16:14:05 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "A broad class of data, ranging from similarity
networks, workflow networks to protein networks, can be
modeled as graphs with data values as vertex labels.
Both vertex labels and neighbors could be dirty for
various reasons such as typos or erroneous reporting of
results in scientific experiments. Neighborhood
constraints, specifying label pairs that are allowed to
appear on adjacent vertices in the graph, are employed
to detect and repair erroneous vertex labels and
neighbors. In this paper, we study the problem of
repairing vertex labels and neighbors to make graphs
satisfy neighborhood constraints. Unfortunately, the
problem is generally hard, which motivates us to devise
approximation methods for repairing and identify
interesting special cases (star and clique constraints)
that can be efficiently solved. First, we propose
several label repairing approximation algorithms
including greedy heuristics, contraction method and an
approach combining both. The performances of algorithms
are also analyzed for the special case. Moreover, we
devise a cubic-time constant-factor graph repairing
algorithm with both label and neighbor repairs (given
degree-bounded instance graphs). Our extensive
experimental evaluation on real data demonstrates the
effectiveness of eliminating frauds in several types of
application networks.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhou:2017:EOV,
author = "Xiangmin Zhou and Lei Chen and Yanchun Zhang and Dong
Qin and Longbing Cao and Guangyan Huang and Chen Wang",
title = "Enhancing online video recommendation using social
user interactions",
journal = j-VLDB-J,
volume = "26",
number = "5",
pages = "637--656",
month = oct,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0469-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 2 16:14:05 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The creation of media sharing communities has resulted
in the astonishing increase of digital videos, and
their wide applications in the domains like online news
broadcasting, entertainment and advertisement. The
improvement of these applications relies on effective
solutions for social user access to videos. This fact
has driven the research interest in the recommendation
in shared communities. Though effort has been put into
social video recommendation, the contextual information
on social users has not been well exploited for
effective recommendation. Motivated by this, in this
paper, we propose a novel approach based on the video
content and user information for the recommendation in
shared communities. A new solution is developed by
allowing batch video recommendation to multiple new
users and optimizing the subcommunity extraction. We
first propose an effective technique that reduces the
subgraph partition cost based on graph decomposition
and reconstruction for efficient subcommunity
extraction. Then, we design a summarization-based
algorithm which groups the clicked videos of multiple
unregistered users and simultaneously provide
recommendation to each of them. Finally, we present a
nontrivial social updates maintenance approach for
social data based on user connection summarization. We
evaluate the performance of our solution over a large
dataset considering different strategies for group
video recommendation in sharing communities.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Attasena:2017:SSC,
author = "Varunya Attasena and J{\'e}r{\^o}me Darmont and Nouria
Harbi",
title = "Secret sharing for cloud data security: a survey",
journal = j-VLDB-J,
volume = "26",
number = "5",
pages = "657--681",
month = oct,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0470-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 2 16:14:05 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Cloud computing helps reduce costs, increase business
agility and deploy solutions with a high return on
investment for many types of applications. However,
data security is of premium importance to many users
and often restrains their adoption of cloud
technologies. Various approaches, i.e., data
encryption, anonymization, replication and
verification, help enforce different facets of data
security. Secret sharing is a particularly interesting
cryptographic technique. Its most advanced variants
indeed simultaneously enforce data privacy,
availability and integrity, while allowing computation
on encrypted data. The aim of this paper is thus to
wholly survey secret sharing schemes with respect to
data security, data access and costs in the
pay-as-you-go paradigm.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Huang:2017:QAL,
author = "Qiang Huang and Jianlin Feng and Qiong Fang and
Wilfred Ng and Wei Wang",
title = "Query-aware locality-sensitive hashing scheme for $
l_p $ norm",
journal = j-VLDB-J,
volume = "26",
number = "5",
pages = "683--708",
month = oct,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0472-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 2 16:14:05 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/hash.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The problem of c-Approximate Nearest Neighbor (c-ANN)
search in high-dimensional space is fundamentally
important in many applications, such as image database
and data mining. Locality-Sensitive Hashing (LSH) and
its variants are the well-known indexing schemes to
tackle the c-ANN search problem. Traditionally, LSH
functions are constructed in a query-oblivious manner,
in the sense that buckets are partitioned before any
query arrives. However, objects closer to a query may
be partitioned into different buckets, which is
undesirable. Due to the use of query-oblivious bucket
partition, the state-of-the-art LSH schemes for
external memory, namely C2LSH and LSB-Forest, only work
with approximation ratio of integer $ c \ge 2 c'z2 $.
In this paper, we introduce a novel concept of
query-aware bucket partition which uses a given query
as the ``anchor'' for bucket partition. Accordingly, a
query-aware LSH function under a specific $ l_p $ norm
with $ p \in (0, 2]p'z(0, 2] $ is a random projection
coupled with query-aware bucket partition, which
removes random shift required by traditional
query-oblivious LSH functions. The query-aware bucket
partitioning strategy can be easily implemented so that
query performance is guaranteed. For each $ l_p $ norm
$ (p \in (0, 2])(p'z(0, 2]) $, based on the
corresponding p-stable distribution, we propose a novel
LSH scheme named query-aware LSH (QALSH) for c-ANN
search over external memory. Our theoretical studies
show that QALSH enjoys a guarantee on query quality.
The use of query-aware LSH function enables QALSH to
work with any approximation ratio $ c > 1 $. In
addition, we propose a heuristic variant named QALSH^++
to improve the scalability of QALSH. Extensive
experiments show that QALSH and QALSH^++ outperform the
state-of-the-art schemes, especially in
high-dimensional space. Specifically, by using a ratio
$ c < 2 $, QALSH can achieve much better query
quality.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhu:2017:GSG,
author = "Qijun Zhu and Haibo Hu and Cheng Xu and Jianliang Xu
and Wang-Chien Lee",
title = "Geo-social group queries with minimum acquaintance
constraints",
journal = j-VLDB-J,
volume = "26",
number = "5",
pages = "709--727",
month = oct,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0473-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 2 16:14:05 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The prosperity of location-based social networking has
paved the way for new applications of group-based
activity planning and marketing. While such
applications heavily rely on geo-social group queries
(GSGQs), existing studies fail to produce a cohesive
group in terms of user acquaintance. In this paper, we
propose a new family of GSGQs with minimum acquaintance
constraints. They are more appealing to users as they
guarantee a worst-case acquaintance level in the result
group. For efficient processing of GSGQs on large
location-based social networks, we devise two
social-aware spatial index structures, namely SaR-tree
and SaR*-tree. The latter improves on the former by
considering both spatial and social distances when
clustering objects. Based on SaR-tree and SaR*-tree,
novel algorithms are developed to process various
GSGQs. Extensive experiments on real datasets Gowalla
and Twitter show that our proposed methods
substantially outperform the baseline algorithms under
various system settings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2017:DMK,
author = "Kai Zhang and Kaibo Wang and Yuan Yuan and Lei Guo and
Rubao Li and Xiaodong Zhang and Bingsheng He and Jiayu
Hu and Bei Hua",
title = "A distributed in-memory key-value store system on
heterogeneous {CPU--GPU} cluster",
journal = j-VLDB-J,
volume = "26",
number = "5",
pages = "729--750",
month = oct,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0479-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Oct 2 16:14:05 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In-memory key-value stores play a critical role in
many data-intensive applications to provide
high-throughput and low latency data accesses.
In-memory key-value stores have several unique
properties that include (1) data-intensive operations
demanding high memory bandwidth for fast data accesses,
(2) high data parallelism and simple computing
operations demanding many slim parallel computing
units, and (3) a large working set. However, our
experiments show that homogeneous multicore CPU systems
are increasingly mismatched to the special properties
of key-value stores because they do not provide massive
data parallelism and high memory bandwidth; the
powerful but the limited number of computing cores does
not satisfy the demand of the unique data processing
task; and the cache hierarchy may not well benefit to
the large working set. In this paper, we present the
design and implementation of Mega-KV, a distributed
in-memory key-value store system on a heterogeneous
CPU---GPU cluster. Effectively utilizing the high
memory bandwidth and latency hiding capability of GPUs,
Mega-KV provides fast data accesses and significantly
boosts overall performance and energy efficiency over
the homogeneous CPU architectures. Mega-KV shows
excellent scalability and processes up to 623-million
key-value operations per second on a cluster installed
with eight CPUs and eight GPUs, while delivering an
efficiency of up to 299-thousand operations per Watt
(KOPS/W).",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2017:FIC,
author = "Rong-Hua Li and Lu Qin and Jeffrey Xu Yu and Rui Mao",
title = "Finding influential communities in massive networks",
journal = j-VLDB-J,
volume = "26",
number = "6",
pages = "751--776",
month = dec,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0467-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Nov 10 08:53:24 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Community search is a problem of finding densely
connected subgraphs that satisfy the query conditions
in a network, which has attracted much attention in
recent years. However, all the previous studies on
community search do not consider the influence of a
community. In this paper, we introduce a novel
community model called k-influential community based on
the concept of k-core to capture the influence of a
community. Based on this community model, we propose a
linear time online search algorithm to find the
top-rk-influential communities in a network. To further
speed up the influential community search algorithm, we
devise a linear space data structure which supports
efficient search of the top-rk-influential communities
in optimal time. We also propose an efficient algorithm
to maintain the data structure when the network is
frequently updated. Additionally, we propose a novel
I/O-efficient algorithm to find the top-rk-influential
communities in a disk-resident graph under the
assumption of {{\mathcal {U}}}=O(n)U=O(n), where
{{\mathcal {U}}}U and n denote the size of the main
memory and the number of nodes, respectively. Finally,
we conduct extensive experiments on six real-world
massive networks, and the results demonstrate the
efficiency and effectiveness of the proposed methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ali:2017:CDP,
author = "Syed Muhammad Ali and Robert Wrembel",
title = "From conceptual design to performance optimization of
{ETL} workflows: current state of research and open
problems",
journal = j-VLDB-J,
volume = "26",
number = "6",
pages = "777--801",
month = dec,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0477-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Nov 10 08:53:24 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we discuss the state of the art and
current trends in designing and optimizing ETL
workflows. We explain the existing techniques for: (1)
constructing a conceptual and a logical model of an ETL
workflow, (2) its corresponding physical
implementation, and (3) its optimization, illustrated
by examples. The discussed techniques are analyzed
w.r.t. their advantages, disadvantages, and challenges
in the context of metrics such as autonomous behavior,
support for quality metrics, and support for ETL
activities as user-defined functions. We draw
conclusions on still open research and technological
issues in the field of ETL. Finally, we propose a
theoretical ETL framework for ETL optimization.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fang:2017:EEA,
author = "Yixiang Fang and Reynold Cheng and Yankai Chen and
Siqiang Luo and Jiafeng Hu",
title = "Effective and efficient attributed community search",
journal = j-VLDB-J,
volume = "26",
number = "6",
pages = "803--828",
month = dec,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0482-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Nov 10 08:53:24 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Given a graph G and a vertex q \in Gq'zG, the
community search query returns a subgraph of G that
contains vertices related to q. Communities, which are
prevalent in attributed graphs such as social networks
and knowledge bases, can be used in emerging
applications such as product advertisement and setting
up of social events. In this paper, we investigate the
attributed community query (or ACQ), which returns an
attributed community (AC) for an attributed graph. The
AC is a subgraph of G, which satisfies both structure
cohesiveness (i.e., its vertices are tightly connected)
and keyword cohesiveness (i.e., its vertices share
common keywords). The AC enables a better understanding
of how and why a community is formed (e.g., members of
an AC have a common interest in music, because they all
have the same keyword ``music''). An AC can be
``personalized''; for example, an ACQ user may specify
that an AC returned should be related to some specific
keywords like ``research'' and ``sports''. To enable
efficient AC search, we develop the CL-tree index
structure and three algorithms based on it. We further
propose efficient algorithms for maintaining the index
on dynamic graphs. Moreover, we study two problems that
are related to the ACQ problem. We evaluate our
solutions on six large graphs. Our results show that
ACQ is more effective and efficient than existing
community retrieval approaches. Moreover, an AC
contains more precise and personalized information than
that of existing community search and detection
methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lu:2017:MES,
author = "Wei Lu and Jiajia Hou and Ying Yan and Meihui Zhang
and Xiaoyong Du and Thomas Moscibroda",
title = "{MSQL}: efficient similarity search in metric spaces
using {SQL}",
journal = j-VLDB-J,
volume = "26",
number = "6",
pages = "829--854",
month = dec,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0481-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Nov 10 08:53:24 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Similarity search is a primitive operation that arises
in a large variety of database applications. Typical
examples include identifying articles with similar
titles, finding similar images and music in a large
digital object repository, etc. While there exist a
wide spectrum of access methods for similarity queries
in metric spaces, a practical solution that can be
fully supported by existing RDBMS with high efficiency
still remains an open problem. In this paper, we
present MSQL, a practical solution for answering
similarity queries in metric spaces fully using SQL. To
the best of our knowledge, MSQL enables users to find
similar objects by submitting SELECT-FROM-WHERE
statements only. MSQL provides a uniform indexing
scheme based on a standard built-in B^+B+-tree index,
with the ability to accelerate the query processing
using index seek. Various query optimization techniques
are incorporated in MSQL to significantly reduce CPU
and I/O cost. We deploy MSQL on top of PostgreSQL.
Extensive experiments on various real data sets
demonstrate MSQL's benefits, performing up to two
orders of magnitude faster than existing
domain-specific SQL-based solutions and being
comparable to native solutions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hung:2017:AVG,
author = "Nguyen Quoc Hung and Duong Chi Thang and Nguyen Thanh
Tam and Matthias Weidlich and Karl Aberer and Hongzhi
Yin and Xiaofang Zhou",
title = "Answer validation for generic crowdsourcing tasks with
minimal efforts",
journal = j-VLDB-J,
volume = "26",
number = "6",
pages = "855--880",
month = dec,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0484-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Nov 10 08:53:24 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Crowdsourcing has been established as an essential
means to scale human computation in diverse Web
applications, reaching from data integration to
information retrieval. Yet, crowd workers have
wide-ranging levels of expertise. Large worker
populations are heterogeneous and comprise a
significant amount of faulty workers. As a consequence,
quality insurance for crowd answers is commonly seen as
the Achilles heel of crowdsourcing. Although various
techniques for quality control have been proposed in
recent years, a post-processing phase in which crowd
answers are validated is still required. Such
validation, however, is typically conducted by experts,
whose availability is limited and whose work incurs
comparatively high costs. This work aims at guiding an
expert in the validation of crowd answers. We present a
probabilistic model that helps to identify the most
beneficial validation questions in terms of both
improvement in result correctness and detection of
faulty workers. By seeking expert feedback on the most
problematic cases, we are able to obtain a set of
high-quality answers, even if the expert does not
validate the complete answer set. Our approach is
applicable for a broad range of crowdsourcing tasks,
including classification and counting. Our
comprehensive evaluation using both real-world and
synthetic datasets demonstrates that our techniques
save up to 60\% of expert efforts compared to baseline
methods when striving for perfect result correctness.
In absolute terms, for most cases, we achieve close to
perfect correctness after expert input has been sought
for only 15\% of the crowdsourcing tasks.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Herschel:2017:SPW,
author = "Melanie Herschel and Ralf Diestelk{\"a}mper and
Houssem Ben Lahmar",
title = "A survey on provenance: {What} for? {What} form?
{What} from?",
journal = j-VLDB-J,
volume = "26",
number = "6",
pages = "881--906",
month = dec,
year = "2017",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0486-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Nov 10 08:53:24 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Provenance refers to any information describing the
production process of an end product, which can be
anything from a piece of digital data to a physical
object. While this survey focuses on the former type of
end product, this definition still leaves room for many
different interpretations of and approaches to
provenance. These are typically motivated by different
application domains for provenance (e.g.,
accountability, reproducibility, process debugging) and
varying technical requirements such as runtime,
scalability, or privacy. As a result, we observe a wide
variety of provenance types and provenance-generating
methods. This survey provides an overview of the
research field of provenance, focusing on what
provenance is used for (what for?), what types of
provenance have been defined and captured for the
different applications (what form?), and which
resources and system requirements impact the choice of
deploying a particular provenance solution (what
from?). For each of these three key questions, we
provide a classification and review the state of the
art for each class. We conclude with a summary and
possible future research challenges.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wei:2018:RQI,
author = "Hao Wei and Jeffrey Xu Yu and Can Lu and Ruoming Jin",
title = "Reachability querying: an independent permutation
labeling approach",
journal = j-VLDB-J,
volume = "27",
number = "1",
pages = "1--26",
month = feb,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0468-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 6 18:41:42 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Reachability query is a fundamental graph operation
which answers whether a vertex can reach another vertex
over a large directed graph $G$ with $n$ vertices and m
edges and has been extensively studied. In the
literature, all the approaches compute a label for
every vertex in a graph $G$ by index construction
offline. The query time for answering reachability
queries online is affected by the quality of the labels
computed in index construction. The three main costs
are the index construction time, the index size, and
the query time. Some of the up-to-date approaches can
answer reachability queries efficiently, but spend
nonlinear time to construct an index. Some of the
up-to-date approaches construct an index in linear time
and space, but may need to depth-first search $G$ at
run-time in $ O(n + m)$. In this paper, we discuss a
new randomized labeling approach, named IP label, to
answer reachability queries with probability guarantee,
and the randomness is by independent permutation. Two
additional labels are also proposed to further enhance
the query processing. In addition, to deal with dynamic
graphs, we discuss the label maintenance over dynamic
graphs and give efficient algorithms for the labels
proposed. We conduct extensive experimental studies to
compare with the up-to-date approaches using 19 large
real datasets used in the existing work and synthetic
datasets. We confirm the efficiency and scalability of
our approach in static graphs testing, and our
maintenance algorithms are about one order of magnitude
faster than the existing ones in dynamic graphs
testing.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lin:2018:OAS,
author = "Chunbin Lin and Jiaheng Lu and Zhewei Wei and Jianguo
Wang and Xiaokui Xiao",
title = "Optimal algorithms for selecting top-$k$ combinations
of attributes: theory and applications",
journal = j-VLDB-J,
volume = "27",
number = "1",
pages = "27--52",
month = feb,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0485-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 6 18:41:42 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Traditional top-$k$ algorithms, e.g., TA and NRA, have
been successfully applied in many areas such as
information retrieval, data mining and databases. They
are designed to discover k objects, e.g., top-$k$
restaurants, with highest overall scores aggregated
from different attributes, e.g., price and location.
However, new emerging applications like query
recommendation require providing the best combinations
of attributes, instead of objects. The straightforward
extension based on the existing top-$k$ algorithms is
prohibitively expensive to answer top-$k$ combinations
because they need to enumerate all the possible
combinations, which is exponential to the number of
attributes. In this article, we formalize a novel type
of top-$k$ query, called top-$k$, m, which aims to find
top-$k$ combinations of attributes based on the overall
scores of the top-m objects within each combination,
where m is the number of objects forming a combination.
We propose a family of efficient top-$k$, m algorithms
with different data access methods, i.e., sorted
accesses and random accesses and different query
certainties, i.e., exact query processing and
approximate query processing. Theoretically, we prove
that our algorithms are instance optimal and analyze
the bound of the depth of accesses. We further develop
optimizations for efficient query evaluation to reduce
the computational and the memory costs and the number
of accesses. We provide a case study on the real
applications of top-$k$, m queries for an online
biomedical search engine. Finally, we perform
comprehensive experiments to demonstrate the
scalability and efficiency of top-$k$, m algorithms on
multiple real-life datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhao:2018:ESS,
author = "Xiang Zhao and Chuan Xiao and Xuemin Lin and Wenjie
Zhang and Yang Wang",
title = "Efficient structure similarity searches: a
partition-based approach",
journal = j-VLDB-J,
volume = "27",
number = "1",
pages = "53--78",
month = feb,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0487-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 6 18:41:42 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Graphs are widely used to model complex data in many
applications, such as bioinformatics, chemistry, social
networks, pattern recognition. A fundamental and
critical query primitive is to efficiently search
similar structures in a large collection of graphs.
This article mainly studies threshold-based graph
similarity search with edit distance constraints.
Existing solutions to the problem utilize fixed-size
overlapping substructures to generate candidates, and
thus become susceptible to large vertex degrees and
distance thresholds. In this article, we present a
partition-based approach to tackle the problem. By
dividing data graphs into variable-size non-overlapping
partitions, the edit distance constraint is converted
to a graph containment constraint for candidate
generation. We develop efficient query processing
algorithms based on the novel paradigm. Moreover,
candidate-pruning techniques and an improved graph edit
distance verification algorithm are developed to boost
the performance. In addition, a cost-aware graph
partitioning method is devised to optimize the index.
Extending the partition-based filtering paradigm, we
present a solution to the top-$k$ k graph similarity
search problem, where tailored filtering, look-ahead
and computation-sharing strategies are exploited. Using
both public real-life and synthetic datasets, extensive
experiments demonstrate that our approaches
significantly outperform the baseline and its
alternatives.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yu:2018:DSS,
author = "Weiren Yu and Xuemin Lin and Wenjie Zhang and Julie A.
Mccann",
title = "Dynamical {SimRank} search on time-varying networks",
journal = j-VLDB-J,
volume = "27",
number = "1",
pages = "79--104",
month = feb,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0488-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 6 18:41:42 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "SimRank is an appealing pair-wise similarity measure
based on graph structure. It iteratively follows the
intuition that two nodes are assessed as similar if
they are pointed to by similar nodes. Many real graphs
are large, and links are constantly subject to minor
changes. In this article, we study the efficient
dynamical computation of all-pairs SimRanks on
time-varying graphs. Existing methods for the dynamical
SimRank computation [e.g., LTSF (Shao et al. in PVLDB
8(8):838--849, 2015) and READS (Zhang et al. in PVLDB
10(5):601--612, 2017)] mainly focus on top-$k$ search
with respect to a given query. For all-pairs dynamical
SimRank search, Li et al.'s approach (Li et al. in
EDBT, 2010) was proposed for this problem. It first
factorizes the graph via a singular value decomposition
(SVD) and then incrementally maintains such a
factorization in response to link updates at the
expense of exactness. As a result, all pairs of
SimRanks are updated approximately, yielding $ O(r^4
n^2) $ time and $ O(r^2 n^2) $ memory in a graph with
$n$ nodes, where r is the target rank of the low-rank
SVD. Our solution to the dynamical computation of
SimRank comprises of five ingredients: (1) We first
consider edge update that does not accompany new node
insertions. We show that the SimRank update
{\varvec{\Delta }}{} \mathbf{S} \Delta S in response to
every link update is expressible as a rank-one
Sylvester matrix equation. This provides an incremental
method requiring $ O(K n^2)$ time and $ O(n^2)$ memory
in the worst case to update n^2n2 pairs of similarities
for $K$ iterations. (2) To speed up the computation
further, we propose a lossless pruning strategy that
captures the ``affected areas'' of {\varvec{\Delta }}{}
\mathbf{S} \Delta S to eliminate unnecessary retrieval.
This reduces the time of the incremental SimRank to $
O(K(m + |{\textsf {AFF}}|))$, where $m$ is the number
of edges in the old graph, and $ |{\textsf {AFF}}| (\le
n^2)$ is the size of ``affected areas'' in $ \Delta S$,
and in practice, $ |{\textsf {AFF}}| \ll n^2$. (3) We
also consider edge updates that accompany node
insertions, and categorize them into three cases,
according to which end of the inserted edge is a new
node. For each case, we devise an efficient incremental
algorithm that can support new node insertions and
accurately update the affected SimRanks. (4) We next
study batch updates for dynamical SimRank computation,
and design an efficient batch incremental method that
handles ``similar sink edges'' simultaneously and
eliminates redundant edge updates. (5) To achieve
linear memory, we devise a memory-efficient strategy
that dynamically updates all pairs of SimRanks column
by column in just $ O(K n + m)$ memory, without the
need to store all $ (n^2)$ pairs of old SimRank scores.
Experimental studies on various datasets demonstrate
that our solution substantially outperforms the
existing incremental SimRank methods and is faster and
more memory-efficient than its competitors on
million-scale graphs.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sagi:2018:NBE,
author = "Tomer Sagi and Avigdor Gal",
title = "Non-binary evaluation measures for big data
integration",
journal = j-VLDB-J,
volume = "27",
number = "1",
pages = "105--126",
month = feb,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0489-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 6 18:41:42 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The evolution of data accumulation, management,
analytics, and visualization has led to the coining of
the term big data, which challenges the task of data
integration. This task, common to any matching problem
in computer science involves generating alignments
between structured data in an automated fashion.
Historically, set-based measures, based upon binary
similarity matrices (match/non-match), have dominated
evaluation practices of matching tasks. However, in the
presence of big data, such measures no longer suffice.
In this work, we propose evaluation methods for
non-binary matrices as well. Non-binary evaluation is
formally defined together with several new, non-binary
measures using a vector space representation of
matching outcome. We provide empirical analyses of the
usefulness of non-binary evaluation and show its
superiority over its binary counterparts in several
problem domains.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wu:2018:SOR,
author = "Yubao Wu and Xiang Zhang and Yuchen Bian and Zhipeng
Cai and Xiang Lian and Xueting Liao and Fengpan Zhao",
title = "Second-order random walk-based proximity measures in
graph analysis: formulations and algorithms",
journal = j-VLDB-J,
volume = "27",
number = "1",
pages = "127--152",
month = feb,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0490-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 6 18:41:42 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Measuring the proximity between different nodes is a
fundamental problem in graph analysis. Random
walk-based proximity measures have been shown to be
effective and widely used. Most existing random walk
measures are based on the first-order Markov model,
i.e., they assume that the next step of the random
surfer only depends on the current node. However, this
assumption neither holds in many real-life applications
nor captures the clustering structure in the graph. To
address the limitation of the existing first-order
measures, in this paper, we study the second-order
random walk measures, which take the previously visited
node into consideration. While the existing first-order
measures are built on node-to-node transition
probabilities, in the second-order random walk, we need
to consider the edge-to-edge transition probabilities.
Using incidence matrices, we develop simple and elegant
matrix representations for the second-order proximity
measures. A desirable property of the developed
measures is that they degenerate to their original
first-order forms when the effect of the previous step
is zero. We further develop Monte Carlo methods to
efficiently compute the second-order measures and
provide theoretical performance guarantees.
Experimental results show that in a variety of
applications, the second-order measures can
dramatically improve the performance compared to their
first-order counterparts.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yang:2018:PPC,
author = "Bin Yang and Jian Dai and Chenjuan Guo and Christian
S. Jensen and Jilin Hu",
title = "{PACE}: a {PAth-CEntric} paradigm for stochastic path
finding",
journal = j-VLDB-J,
volume = "27",
number = "2",
pages = "153--178",
month = apr,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0491-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 24 08:39:19 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "With the growing volumes of vehicle trajectory data,
it becomes increasingly possible to capture
time-varying and uncertain travel costs, e.g., travel
time, in a road network. The current paradigm for doing
so is edge-centric: it represents a road network as a
weighted graph and splits trajectories into small
fragments that fit the underlying edges to assign
time-varying and uncertain weights to edges. It then
applies path finding algorithms to the resulting,
weighted graph. We propose a new PAth-CEntric paradigm,
PACE, that targets more accurate and more efficient
path cost estimation and path finding. By assigning
weights to paths, PACE avoids splitting trajectories
into small fragments. We solve two fundamental problems
to establish the PACE paradigm: (i) how to compute
accurately the travel cost distribution of a path and
(ii) how to conduct path finding for a
source---destination pair. To solve the first problem,
given a departure time and a query path, we show how to
select an optimal set of paths that cover the query
path and such that the weights of the paths enable the
most accurate joint cost distribution estimation for
the query path. The joint cost distribution models well
the travel cost dependencies among the edges in the
query path, which in turn enables accurate estimation
of the cost distribution of the query path. We solve
the second problem by showing that the resulting path
cost distribution estimation method satisfies an
incremental property that enables the method to be
integrated seamlessly into existing stochastic path
finding algorithms. Further, we propose a new
stochastic path finding algorithm that fully explores
the improved accuracy and efficiency provided by PACE.
Empirical studies with trajectory data from two
different cities offer insight into the design
properties of the PACE paradigm and offer evidence that
PACE is accurate, efficient, and effective in
real-world settings.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hu:2018:RAP,
author = "Jilin Hu and Bin Yang and Chenjuan Guo and Christian
S. Jensen",
title = "Risk-aware path selection with time-varying, uncertain
travel costs: a time series approach",
journal = j-VLDB-J,
volume = "27",
number = "2",
pages = "179--200",
month = apr,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0494-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 24 08:39:19 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We address the problem of choosing the best paths
among a set of candidate paths between the same
origin---destination pair. This functionality is used
extensively when constructing origin---destination
matrices in logistics and flex transportation. Because
the cost of a path, e.g., travel time, varies over time
and is uncertain, there is generally no single best
path. We partition time into intervals and represent
the cost of a path during an interval as a random
variable, resulting in an uncertain time series for
each path. When facing uncertainties, users generally
have different risk preferences, e.g., risk-loving or
risk-averse, and thus prefer different paths. We
develop techniques that, for each time interval, are
able to find paths with non-dominated lowest costs
while taking the users' risk preferences into account.
We represent risk by means of utility function
categories and show how the use of first-order and two
kinds of second-order stochastic dominance
relationships among random variables makes it possible
to find all paths with non-dominated lowest costs. We
report on empirical studies with large uncertain time
series collections derived from a 2-year GPS data set.
The study offers insight into the performance of the
proposed techniques, and it indicates that the best
techniques combine to offer an efficient and robust
solution.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Su:2018:PDP,
author = "Dong Su and Jianneng Cao and Ninghui Li and Min Lyu",
title = "{PrivPfC}: differentially private data publication for
classification",
journal = j-VLDB-J,
volume = "27",
number = "2",
pages = "201--223",
month = apr,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0492-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 24 08:39:19 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we tackle the problem of constructing a
differentially private synopsis for the classification
analysis. Several state-of-the-art methods follow the
structure of existing classification algorithms and are
all iterative, which is suboptimal due to the locally
optimal choices and division of the privacy budget
among many sequentially composed steps. We propose
PrivPfC, a new differentially private method for
releasing data for classification. The key idea
underlying PrivPfC is to privately select, in a single
step, a grid, which partitions the data domain into a
number of cells. This selection is done by using the
exponential mechanism with a novel quality function,
which maximizes the expected number of correctly
classified records by a histogram classifier. PrivPfC
supports both the binary classification and the
multiclass classification. Through extensive
experiments on real datasets, we demonstrate PrivPfC 's
superiority over the state-of-the-art methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2018:AKS,
author = "Dongxiang Zhang and Yuchen Li and Xin Cao and Jie Shao
and Heng Tao Shen",
title = "Augmented keyword search on spatial entity databases",
journal = j-VLDB-J,
volume = "27",
number = "2",
pages = "225--244",
month = apr,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0497-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 24 08:39:19 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/hash.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we propose a new type of query that
augments the spatial keyword search with an additional
boolean expression constraint. The query is issued
against a corpus of structured or semi-structured
spatial entities and is very useful in applications
like mobile search and targeted location-aware
advertising. We devise three types of indexing and
filtering strategies. First, we utilize the hybrid
IR$^2$-tree and propose a novel hashing scheme for
efficient pruning. Second, we propose an inverted
index-based solution, named BE-Inv, that is more cache
conscious and exhibits great pruning power for boolean
expression matching. Our third method, named SKB-Inv,
adopts a novel two-level partitioning scheme to
organize the spatial entities into inverted lists and
effectively facilitate the pruning in the spatial,
textual, and boolean expression dimensions. In
addition, we propose an adaptive query processing
strategy that takes into account the selectivity of
query keywords and predicates for early termination. We
conduct our experiments using two real datasets with
3.5 million Foursquare venues and 50 million Twitter
geo-profiles. The results show that the methods based
on inverted index are superior to the hybrid
{IR}$^2$-tree; and SKB-Inv achieves the best
performance.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Deutch:2018:EPT,
author = "Daniel Deutch and Amir Gilad and Yuval Moskovitch",
title = "Efficient provenance tracking for datalog using
top-$k$ queries",
journal = j-VLDB-J,
volume = "27",
number = "2",
pages = "245--269",
month = apr,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0496-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 24 08:39:19 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Highly expressive declarative languages, such as
datalog, are now commonly used to model the operational
logic of data-intensive applications. The typical
complexity of such datalog programs, and the large
volume of data that they process, call for result
explanation. Results may be explained through the
tracking and presentation of data provenance, defined
here as the set of derivation trees of a given fact.
While informative, the size of such full provenance
information is typically too large and complex (even
when compactly represented) to allow displaying it to
the user. To this end, we propose a novel top-k query
language for querying datalog provenance, supporting
selection criteria based on tree patterns and ranking
based on the rules and database facts used in
derivation. We propose an efficient novel algorithm
that computes in polynomial data complexity a compact
representation of the top-k trees which may be
explicitly constructed in linear time with respect to
their size. We further experimentally study the
algorithm performance, showing its scalability even for
complex datalog programs where full provenance tracking
is infeasible.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhou:2018:ARQ,
author = "Junfeng Zhou and Jeffrey Xu Yu and Na Li and Hao Wei
and Ziyang Chen and Xian Tang",
title = "Accelerating reachability query processing based on {$
\vec {\rm DAG} $} reduction",
journal = j-VLDB-J,
volume = "27",
number = "2",
pages = "271--296",
month = apr,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0495-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Mar 24 08:39:19 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Answering reachability queries is one of the
fundamental graph operations. The existing approaches
build indexes and answer reachability queries on a
directed acyclic graph (DAG) GG, which is constructed
by coalescing each strongly connected component of the
given directed graph $ \mathcal {G} $ into a node of
GG. Considering that GG can still be large to be
processed efficiently, there are studies to further
reduce GG to a smaller graph. However, these approaches
suffer from either inefficiency in answering
reachability queries, or cannot scale to large graphs.
In this paper, we study DAG reduction to accelerate
reachability query processing, which reduces the size
of GG by computing transitive reduction (TR) followed
by computing equivalence reduction (ER). For TR, we
propose a bottom-up algorithm, namely buTR, which
removes from GG all redundant edges to get the unique
smallest DAGG$^{tGt}$ satisfying that G$^{tGt}$ has the
same transitive closure as that of GG. For ER, we
propose a divide-and-conquer algorithm, namely
linear-ER. Given the result G$^{tGt}$ of TR, linear-ER
gets a smaller DAGG$^\varepsilon $G in linear time
based on equivalence relationship between nodes in GG.
Our DAG reduction approaches (TR and ER) significantly
improve the cost of time and space and can be scaled to
large graphs. Based on the result of DAG reduction, we
further propose a graph decomposition-based algorithm
to efficiently answer reachability queries. We confirm
the efficiency of our approaches by extensive
experimental studies for TR, ER, and reachability query
processing using 20 real datasets. The complete source
code is available for download at
https://pan.baidu.com/s/1skHBXXN.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Belesiotis:2018:STU,
author = "Alexandros Belesiotis and Dimitrios Skoutas and
Christodoulos Efstathiades and Vassilis Kaffes and
Dieter Pfoser",
title = "Spatio-textual user matching and clustering based on
set similarity joins",
journal = j-VLDB-J,
volume = "27",
number = "3",
pages = "297--320",
month = jun,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0498-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Jun 8 17:24:12 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper addresses the problem of matching and
clustering users based on their geolocated posts.
Individual posts are matched according to spatial
distance and textual similarity thresholds. Then, user
similarity is defined as the ratio of their posts that
match each other. Based on these criteria, we introduce
efficient algorithms for identifying pairs of matching
users in a large dataset, as well as for computing the
top-k matching pairs. We then proceed to identify
spatio-textual user clusters. For this purpose, we use
the Louvain method for community detection. Our
algorithms operate on a user graph where edge weights
represent spatio-textual user similarities. Since the
exact user similarity graph can be prohibitively
expensive to compute, we exploit our previous
algorithms to derive efficient methods that reduce
execution time both by avoiding to compute exact
similarity scores and by reducing the number of
similarity calculations performed. The presented
solution allows a trade-off between computation time
and quality of detected clusters. The proposed
algorithms are evaluated using three real-world
datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2018:GSG,
author = "Lei Li and Kai Zheng and Sibo Wang and Wen Hua and
Xiaofang Zhou",
title = "Go slow to go fast: minimal on-road time route
scheduling with parking facilities using historical
trajectory",
journal = j-VLDB-J,
volume = "27",
number = "3",
pages = "321--345",
month = jun,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0499-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Jun 8 17:24:12 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "For thousands of years, people have been innovating
new technologies to make their travel faster, the
latest of which is GPS technology that is used by
millions of drivers every day. The routes recommended
by a GPS device are computed by path planning
algorithms (e.g., fastest path algorithm), which aim to
minimize a certain objective function (e.g., travel
time) under the current traffic condition. When the
objective is to arrive the destination as early as
possible, waiting during travel is not an option as it
will only increase the total travel time due to the
First-In-First-Out property of most road networks.
However, some businesses such as logistics companies
are more interested in optimizing the actual on-road
time of their vehicles (i.e., while the engine is
running) since it is directly related to the
operational cost. At the same time, the drivers'
trajectories, which can reveal the traffic conditions
on the roads, are also collected by various service
providers. Compared to the existing speed profile
generation methods, which mainly rely on traffic
monitor systems, the trajectory-based method can cover
a much larger space and is much cheaper and flexible to
obtain. This paper proposes a system, which has an
online component and an offline component, to solve the
minimal on-road time problem using the trajectories.
The online query answering component studies how
parking facilities along the route can be leveraged to
avoid predicted traffic jam and eventually reduce the
drivers' on-road time, while the offline component
solves how to generate speed profiles of a road network
from historical trajectories. The challenging part of
the routing problem of the online component lies in the
computational complexity when determining if it is
beneficial to wait on specific parking places and the
time of waiting to maximize the benefit. To cope with
this challenging problem, we propose two efficient
algorithms using minimum on-road travel cost function
to answer the query. We further introduce several
approximation methods to speed up the query answering,
with an error bound guaranteed. The offline speed
profile generation component makes use of historical
trajectories to provide the traveling time for the
online component. Extensive experiments show that our
method is more efficient and accurate than baseline
approaches extended from the existing path planning
algorithms, and our speed profile is accurate and space
efficient.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yao:2018:SDT,
author = "Chang Yao and Meihui Zhang and Qian Lin and Beng Chin
Ooi and Jiatao Xu",
title = "Scaling distributed transaction processing and
recovery based on dependency logging",
journal = j-VLDB-J,
volume = "27",
number = "3",
pages = "347--368",
month = jun,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0500-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Jun 8 17:24:12 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Dependency graph-based concurrency control (DGCC)
protocol has been shown to achieve good performance on
multi-core in-memory system. DGCCseparates contention
resolution from the transaction execution and employs
dependency graphs to derive serializable transaction
schedules. However, distributed transactions complicate
the dependency resolution, and therefore, an effective
transaction partitioning strategy is essential to
reduce expensive multi-node distributed transactions.
During failure recovery, log must be examined from the
last checkpoint onward and the affected transactions
are re-executed based on the way they are partitioned
and executed. Existing approaches treat both
transaction management and recovery as two separate
problems, even though recovery is dependent on the
sequence in which transactions are executed. In this
paper, we propose to treat the transaction management
and recovery problems as one. We first propose an
efficient distributed dependency graph-based
concurrency control (DistDGCC) protocol for handling
transactions spanning multiple nodes and propose a new
novel and efficient logging protocol called dependency
logging that also makes use of dependency graphs for
efficient logging and recovery. DistDGCC optimizes the
average cost for each distributed transaction by
processing transactions in batches. Moreover, it also
reduces the effects of thread blocking caused by
distributed transactions and consequently improves the
runtime performance. Further, dependency logging
exploits the same data structure that is used by
DistDGCC to reduce the logging overhead, as well as the
logical dependency information to improve the recovery
parallelism. Extensive experiments are conducted to
evaluate the performance of our proposed technique
against state-of-the-art techniques. Experimental
results show that DistDGCC is efficient and scalable,
and dependency logging supports fast recovery with
marginal runtime overhead. Hence, the overall system
performance is significantly improved as a result.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chodpathumwan:2018:CEC,
author = "Yodsawalai Chodpathumwan and Ali Vakilian and Arash
Termehchy and Amir Nayyeri",
title = "Cost-effective conceptual design using taxonomies",
journal = j-VLDB-J,
volume = "27",
number = "3",
pages = "369--394",
month = jun,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0501-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Jun 8 17:24:12 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "It is known that annotating entities in unstructured
and semi-structured datasets by their concepts improves
the effectiveness of answering queries over these
datasets. Ideally, one would like to annotate entities
of all relevant concepts in a dataset. However, it
takes substantial time and computational resources to
annotate concepts in large datasets, and an
organization may have sufficient resources to annotate
only a subset of relevant concepts. Clearly, it would
like to annotate a subset of concepts that provides the
most effective answers to queries over the dataset. We
propose a formal framework that quantifies the amount
by which annotating entities of concepts from a
taxonomy in a dataset improves the effectiveness of
answering queries over the dataset. Because the problem
is \mathbf {NP}NP-hard, we propose efficient
approximation and pseudo-polynomial time algorithms for
several cases of the problem. Our extensive empirical
studies validate our framework and show accuracy and
efficiency of our algorithms.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Shang:2018:PTS,
author = "Shuo Shang and Lisi Chen and Zhewei Wei and Christian
S. Jensen and Kai Zheng and Panos Kalnis",
title = "Parallel trajectory similarity joins in spatial
networks",
journal = j-VLDB-J,
volume = "27",
number = "3",
pages = "395--420",
month = jun,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0502-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Jun 8 17:24:12 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The matching of similar pairs of objects, called
similarity join, is fundamental functionality in data
management. We consider two cases of trajectory
similarity joins (TS-Joins), including a
threshold-based join (Tb-TS-Join) and a top-k TS-Join
(k-TS-Join), where the objects are trajectories of
vehicles moving in road networks. Given two sets of
trajectories and a threshold \theta `?, the Tb-TS-Join
returns all pairs of trajectories from the two sets
with similarity above \theta `?. In contrast, the
k-TS-Join does not take a threshold as a parameter, and
it returns the top-k most similar trajectory pairs from
the two sets. The TS-Joins target diverse applications
such as trajectory near-duplicate detection, data
cleaning, ridesharing recommendation, and traffic
congestion prediction. With these applications in mind,
we provide purposeful definitions of similarity. To
enable efficient processing of the TS-Joins on large
sets of trajectories, we develop search space pruning
techniques and enable use of the parallel processing
capabilities of modern processors. Specifically, we
present a two-phase divide-and-conquer search framework
that lays the foundation for the algorithms for the
Tb-TS-Join and the k-TS-Join that rely on different
pruning techniques to achieve efficiency. For each
trajectory, the algorithms first find similar
trajectories. Then they merge the results to obtain the
final result. The algorithms for the two joins exploit
different upper and lower bounds on the spatiotemporal
trajectory similarity and different heuristic
scheduling strategies for search space pruning. Their
per-trajectory searches are independent of each other
and can be performed in parallel, and the mergings have
constant cost. An empirical study with real data offers
insight in the performance of the algorithms and
demonstrates that they are capable of outperforming
well-designed baseline algorithms by an order of
magnitude.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lee:2018:PRA,
author = "Juchang Lee and Wook-Shin Han and Hyoung Jun Na and
Chang Gyoo Park and Kyu Hwan Kim and Deok Hoe Kim and
Joo Yeon Lee and Sang Kyun Cha and Seunghyun Moon",
title = "Parallel replication across formats for scaling out
mixed {OLTP\slash OLAP} workloads in main-memory
databases",
journal = j-VLDB-J,
volume = "27",
number = "3",
pages = "421--444",
month = jun,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0503-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Jun 8 17:24:12 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Modern in-memory database systems are facing the need
of efficiently supporting mixed workloads of OLTP and
OLAP. A conventional approach to this requirement is to
rely on ETL-style, application-driven data replication
between two very different OLTP and OLAP systems,
sacrificing real-time reporting on operational data. An
alternative approach is to run OLTP and OLAP workloads
in a single machine, which eventually limits the
maximum scalability. In order to tackle this
challenging problem, we propose a novel database
replication architecture called HANA Asynchronous
Parallel Table Replication (ATR). ATR supports OLTP
workloads in one primary machine, while it supports
heavy OLAP workloads in replicas. Here, row store
formats can be used for OLTP transactions at the
primary, while column store formats are used for OLAP
analytical queries at the replicas. ATR is designed to
support elastic scalability of OLAP query performance,
while it minimizes the overhead for transaction
processing at the primary and minimizes CPU consumption
for replayed transactions at the replicas. ATR employs
a novel optimistic lock-free parallel log replay scheme
which exploits characteristics of multi-version
concurrency control (MVCC) to enable real-time
reporting by minimizing the propagation delay between
the primary and replicas. It supports adaptive query
routing depending on its predefined acceptable
staleness range. Through extensive experiments with a
concrete implementation available in a commercial
product, we demonstrate that ATR achieves sub-second
visibility delay even for update-intensive workloads,
providing scalable OLAP performance without notable
overhead to the primary. In addition, with extension of
ATR to eager parallel replication, we demonstrate how
the parallel log replay and its log-less replica
recovery mechanisms improve run-time transaction
performance under eager replication.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Choudhury:2018:FOL,
author = "Farhana Murtaza Choudhury and J. Shane Culpepper and
Zhifeng Bao and Timos Sellis",
title = "Finding the optimal location and keywords in
obstructed and unobstructed space",
journal = j-VLDB-J,
volume = "27",
number = "4",
pages = "445--470",
month = aug,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0504-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Sep 8 07:39:26 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The problem of optimal location selection based on
reverse k nearest neighbor (R kk NN) queries has been
extensively studied in spatial databases. In this work,
we present a related query, denoted as a Maximized
Bichromatic Reverse Spatial Textual k Nearest Neighbor
(MaxST) query, that finds an optimal location and a set
of keywords for an object so that the object is a kk NN
object for as many users as possible. Such a query has
many practical applications including advertisements,
where the query is to find the location and the text
contents to include in an advertisement so that it is
relevant to the maximum number of users. The visibility
of the advertisements also has an important role in the
users' interests. In this work, we address two
instances of the spatial relevance when ranking items:
(1) the Euclidean distance and (2) the visibility. We
carefully design a series of index structures and
approaches to answer the MaxST for both instances.
Specifically, we present (1) the Grp-topk approach that
requires the computation of the top-k objects for all
of the users first and then applies various pruning
techniques to find the optimal location and keywords;
(2) the Indiv-U approach, where we use similarity
estimations to avoid computing the top-k objects of the
users that cannot be a final result; and (3) the
Index-U approach where we propose a hierarchical index
structure over the users to improve pruning. We show
that the keyword selection component in MaxST queries
is NP-hard and present both approximate and exact
solutions for the problem.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yang:2018:ESC,
author = "Jianye Yang and Wenjie Zhang and Shiyu Yang and Ying
Zhang and Xuemin Lin and Long Yuan",
title = "Efficient set containment join",
journal = j-VLDB-J,
volume = "27",
number = "4",
pages = "471--495",
month = aug,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0505-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Sep 8 07:39:26 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "In this paper, we study the problem of set containment
join. Given two collections $ \mathcal {R} $ and $
\mathcal {S} $ of records, the set containment join $
\mathcal {R} \bowtie_\subseteq \mathcal {S} $ retrieves
all record pairs $ \{ (r, s) \} \in \mathcal {R} \times
\mathcal {S} $ such that $ r \subseteq s r \subseteq s
$. This problem has been extensively studied in the
literature and has many important applications in
commercial and scientific fields. Recent research
focuses on the in-memory set containment join
algorithms, and several techniques have been developed
following intersection-oriented or union-oriented
computing paradigms. Nevertheless, we observe that two
computing paradigms have their limits due to the nature
of the intersection and union operators. Particularly,
intersection-oriented method relies on the intersection
of the relevant inverted lists built on the elements of
$ \mathcal {S} $. A nice property of the
intersection-oriented method is that the join
computation is verification free. However, the number
of records explored during the join process may be
large because there are multiple replicas for each
record in $ \mathcal {S} $. On the other hand, the
union-oriented method generates a signature for each
record in $ \mathcal {R} $ and the candidate pairs are
obtained by the union of the inverted lists of the
relevant signatures. The candidate size of the
union-oriented method is usually small because each
record contributes only one replica in the index.
Unfortunately, union-oriented method needs to verify
the candidate pairs, which may be cost expensive
especially when the join result size is large. As a
matter of fact, the state-of-the-art union-oriented
solution is not competitive compared to the
intersection-oriented ones. In this paper, we propose a
new union-oriented method, namely TT-Join, which not
only enhances the advantage of the previous
union-oriented methods but also integrates the goodness
of intersection-oriented methods by imposing a variant
of prefix tree structure. We conduct extensive
experiments on 20 real-life datasets and synthetic
datasets by comparing our method with 7 existing
methods. The experiment results demonstrate that
TT-Join significantly outperforms the existing
algorithms on most of the datasets and can achieve up
to two orders of magnitude speedup. Furthermore, to
support large scale of datasets, we extend our
techniques to distributed systems on top of MapReduce
framework. With the help of careful designed load-aware
distribution mechanisms, our distributed join algorithm
can achieve up to an order of magnitude speedup than
the baselines methods.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hao:2018:DRU,
author = "Shuang Hao and Nan Tang and Guoliang Li and Jian Li
and Jianhua Feng",
title = "Distilling relations using knowledge bases",
journal = j-VLDB-J,
volume = "27",
number = "4",
pages = "497--519",
month = aug,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0506-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Sep 8 07:39:26 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Given a relational table, we study the problem of
detecting and repairing erroneous data, as well as
marking correct data, using well curated knowledge
bases (KBs). We propose detective rules (DRs), a new
type of data cleaning rules that can make actionable
decisions on relational data, by building connections
between a relation and a KB. The main invention is that
a DR simultaneously models two opposite semantics of an
attribute belonging to a relation using types and
relationships in a KB: The positive semantics explains
how its value should be linked to other attribute
values in a correct tuple, and the negative semantics
indicate how a wrong attribute value is connected to
other correct attribute values within the same tuple.
Naturally, a DR can mark correct values in a tuple if
it matches the positive semantics. Meanwhile, a DR can
detect/repair an error if it matches the negative
semantics. We study fundamental problems associated
with DRs, e.g., rule consistency and rule implication.
We present efficient algorithms to apply DRs to clean a
relation, based on rule order selection and inverted
indexes. Moreover, we discuss approaches on how to
generate DRs from examples. Extensive experiments,
using both real-world and synthetic datasets, verify
the effectiveness and efficiency of applying DRs in
practice.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Borovica-Gajic:2018:SSR,
author = "Renata Borovica-Gajic and Stratos Idreos and Anastasia
Ailamaki and Marcin Zukowski and Campbell Fraser",
title = "{Smooth Scan}: robust access path selection without
cardinality estimation",
journal = j-VLDB-J,
volume = "27",
number = "4",
pages = "521--545",
month = aug,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0507-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Sep 8 07:39:26 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Query optimizers depend heavily on statistics
representing column distributions to create good query
plans. In many cases, though, statistics are outdated
or nonexistent, and the process of refreshing
statistics is very expensive, especially for ad hoc
workloads on ever bigger data. This results in
suboptimal plans that severely hurt performance. The
core of the problem is the fixed decision on the type
of physical operators that comprise a query plan. This
paper makes a case for continuous adaptation and
morphing of physical operators throughout their
lifetime, by adjusting their behavior in accordance
with the observed statistical properties of the data at
run time. We demonstrate the benefits of the new
paradigm by designing and implementing an adaptive
access path operator called Smooth Scan, which morphs
continuously within the space of index access and full
table scan. Smooth Scan behaves similarly to an index
scan for low selectivity; if selectivity increases,
however, Smooth Scan progressively morphs its behavior
toward a sequential scan. As a result, a system with
Smooth Scan requires no optimization decisions on the
access paths up front. Additionally, by depending only
on the result distribution and eschewing statistics and
cardinality estimates altogether, Smooth Scan ensures
repeatable execution across multiple query invocations.
Smooth Scan implemented in PostgreSQL demonstrates
robust, near-optimal performance on micro-benchmarks
and real-life workloads, while being statistics
oblivious at the same time.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Herrmann:2018:MSV,
author = "Kai Herrmann and Hannes Voigt and Torben Bach Pedersen
and Wolfgang Lehner",
title = "Multi-schema-version data management: data
independence in the twenty-first century",
journal = j-VLDB-J,
volume = "27",
number = "4",
pages = "547--571",
month = aug,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0508-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Sep 8 07:39:26 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Agile software development allows us to continuously
evolve and run a software system. However, this is not
possible in databases, as established methods are very
expensive, error-prone, and far from agile. We present
InVerDa, a multi-schema-version database management
system (MSVDB) for agile database development. MSVDBs
realize co-existing schema versions within one
database, where each schema version behaves like a
regular single-schema database and write operations are
propagated between schema versions. Developers use a
relationally complete and bidirectional database
evolution language (BiDEL) to easily evolve existing
schema versions to new ones. BiDEL scripts are more
robust, orders of magnitude shorter, and cause only a
small performance overhead compared to handwritten SQL
scripts. We formally guarantee data independence: no
matter how the data of the co-existing schema versions
is physically materialized, each schema version is
guaranteed to behave like a regular database. Since,
the chosen physical materialization significantly
determines the overall performance, we equip database
administrators with an advisor that proposes an
optimized materialization for the current workload,
which can improve the performance by orders of
magnitude compared to na{\"\i}ve solutions. To our best
knowledge, we are the first to facilitate agile
evolution of production databases with full support of
co-existing schema versions and formally guaranteed
data independence.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Szlichta:2018:ECD,
author = "Jaroslaw Szlichta and Parke Godfrey and Lukasz Golab
and Mehdi Kargar and Divesh Srivastava",
title = "Effective and complete discovery of bidirectional
order dependencies via set-based axioms",
journal = j-VLDB-J,
volume = "27",
number = "4",
pages = "573--591",
month = aug,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0510-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Sep 8 07:39:26 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Integrity constraints (ICs) are useful for expressing
and enforcing application semantics. Formulating ICs
manually, however, requires domain expertise, is prone
to human error, and can be exceedingly time-consuming.
Thus, methods for automatic discovery have been
developed for some classes of ICs, such as functional
dependencies (FDs), and recently, order dependencies
(ODs). ODs properly subsume FDs and can express
business rules involving order; e.g., an employee who
pays higher taxes has a higher salary than another
employee. Bidirectional ODs further allow different
ordering directions, ascending and descending, as in
SQL's order-by; e.g., a student with an alphabetically
lower letter grade has a higher percentage grade than
another student. We address the limitations of prior
work on automatic OD discovery, which has factorial
complexity, is incomplete, and is not concise. We
present an efficient bidirectional OD discovery
algorithm enabled by a novel polynomial mapping to a
canonical form, and a sound and complete set of axioms
for canonical bidirectional ODs to prune the search
space. Our algorithm has exponential worst-case time
complexity in the number of attributes and linear
complexity in the number of tuples. We prove that it
produces a complete and minimal set of bidirectional
ODs, and we experimentally show orders of magnitude
performance improvements over the prior
state-of-the-art methodologies.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chaudhuri:2018:SIB,
author = "Surajit Chaudhuri and Jayant R. Haritsa",
title = "Special issue on best papers of {VLDB 2016}",
journal = j-VLDB-J,
volume = "27",
number = "5",
pages = "593--594",
month = oct,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0520-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Oct 4 06:40:44 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Interlandi:2018:ADP,
author = "Matteo Interlandi and Ari Ekmekji and Kshitij Shah and
Muhammad Ali Gulzar and Sai Deep Tetali and Miryung Kim
and Todd Millstein and Tyson Condie",
title = "Adding data provenance support to {Apache Spark}",
journal = j-VLDB-J,
volume = "27",
number = "5",
pages = "595--615",
month = oct,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0474-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Oct 4 06:40:44 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Debugging data processing logic in data-intensive
scalable computing (DISC) systems is a difficult and
time-consuming effort. Today's DISC systems offer very
little tooling for debugging programs, and as a result,
programmers spend countless hours collecting evidence
(e.g., from log files) and performing trial-and-error
debugging. To aid this effort, we built Titian, a
library that enables data provenance--tracking data
through transformations--in Apache Spark. Data
scientists using the Titian Spark extension will be
able to quickly identify the input data at the root
cause of a potential bug or outlier result. Titian is
built directly into the Spark platform and offers data
provenance support at interactive speeds--orders of
magnitude faster than alternative solutions--while
minimally impacting Spark job performance; observed
overheads for capturing data lineage rarely exceed 30\%
above the baseline job execution time.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Eich:2018:EGQ,
author = "Marius Eich and Pit Fender and Guido Moerkotte",
title = "Efficient generation of query plans containing
group-by, join, and groupjoin",
journal = j-VLDB-J,
volume = "27",
number = "5",
pages = "617--641",
month = oct,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0476-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Oct 4 06:40:44 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "It has been a recognized fact for many years that
query execution can benefit from pushing grouping
operators down in the operator tree and applying them
before a join. This so-called eager aggregation reduces
the size(s) of the join argument(s), making join
evaluation faster. Lately, the idea enjoyed a revival
when it was applied to outer joins for the first time
and incorporated in a state-of-the-art plan generator.
However, the recent approach is highly dependent on the
use of heuristics because of the exponential growth of
the search space that goes along with eager
aggregation. Finding an optimal solution for larger
queries calls for effective optimality-preserving
pruning mechanisms to reduce the search space size as
far as possible. By a more thorough investigation of
functional dependencies and keys, we provide a set of
new pruning criteria and extend the idea of eager
aggregation further by combining it with the
introduction of groupjoins. We evaluate the resulting
plan generator with respect to runtime and memory
consumption.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Leis:2018:QOT,
author = "Viktor Leis and Bernhard Radke and Andrey Gubichev and
Atanas Mirchev and Peter Boncz and Alfons Kemper and
Thomas Neumann",
title = "Query optimization through the looking glass, and what
we found running the {Join Order Benchmark}",
journal = j-VLDB-J,
volume = "27",
number = "5",
pages = "643--668",
month = oct,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0480-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Oct 4 06:40:44 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Finding a good join order is crucial for query
performance. In this paper, we introduce the Join Order
Benchmark that works on real-life data riddled with
correlations and introduces 113 complex join queries.
We experimentally revisit the main components in the
classic query optimizer architecture using a complex,
real-world data set and realistic multi-join queries.
For this purpose, we describe cardinality-estimate
injection and extraction techniques that allow us to
compare the cardinality estimators of multiple
industrial SQL implementations on equal footing, and to
characterize the value of having perfect cardinality
estimates. Our investigation shows that all
industrial-strength cardinality estimators routinely
produce large errors: though cardinality estimation
using table samples solves the problem for single-table
queries, there are still no techniques in industrial
systems that can deal accurately with join-crossing
correlated query predicates. We further show that while
estimates are essential for finding a good join order,
query performance is unsatisfactory if the query engine
relies too heavily on these estimates. Using another
set of experiments that measure the impact of the cost
model, we find that it has much less influence on query
performance than the cardinality estimates. We
investigate plan enumeration techniques comparing
exhaustive dynamic programming with heuristic
algorithms and find that exhaustive enumeration
improves performance despite the suboptimal cardinality
estimates. Finally, we extend our investigation from
main-memory only, to also include disk-based query
processing. Here, we find that though accurate
cardinality estimation should be the first priority,
other aspects such as modeling random versus sequential
I/O are also important to predict query runtime.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Makreshanski:2018:MQJ,
author = "Darko Makreshanski and Georgios Giannikis and Gustavo
Alonso and Donald Kossmann",
title = "Many-query join: efficient shared execution of
relational joins on modern hardware",
journal = j-VLDB-J,
volume = "27",
number = "5",
pages = "669--692",
month = oct,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0475-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Oct 4 06:40:44 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Database architectures typically process queries one
at a time, executing concurrent queries in independent
execution contexts. Often, such a design leads to
unpredictable performance and poor scalability. One
approach to circumvent the problem is to take advantage
of sharing opportunities across concurrently running
queries. In this paper, we propose many-query join
(MQJoin), a novel method for sharing the execution of a
join that can efficiently deal with hundreds of
concurrent queries. This is achieved by minimizing
redundant work and making efficient use of main-memory
bandwidth and multi-core architectures. Compared to
existing proposals, MQJoin is able to efficiently
handle larger workloads regardless of the schema by
exploiting more sharing opportunities. We also compared
MQJoin to two commercial main-memory column-store
databases. For a TPC-H-based workload, we show that
MQJoin provides 2---5 $ \times $ higher throughput with
significantly more stable response times.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Brucato:2018:PQE,
author = "Matteo Brucato and Azza Abouzied and Alexandra
Meliou",
title = "Package queries: efficient and scalable computation of
high-order constraints",
journal = j-VLDB-J,
volume = "27",
number = "5",
pages = "693--718",
month = oct,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0483-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Oct 4 06:40:44 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Traditional database queries follow a simple model:
they define constraints that each tuple in the result
must satisfy. This model is computationally efficient,
as the database system can evaluate the query
conditions on each tuple individually. However, many
practical, real-world problems require a collection of
result tuples to satisfy constraints collectively,
rather than individually. In this paper, we present
package queries, a new query model that extends
traditional database queries to handle complex
constraints and preferences over answer sets. We
develop a full-fledged package query system,
implemented on top of a traditional database engine.
Our work makes several contributions. (1) We design
PaQL, a SQL-based query language that supports the
declarative specification of package queries. We prove
that PaQL is at least as expressive as integer linear
programming, and therefore, evaluation of package
queries is NP-hard. (2) We present a fundamental
evaluation strategy that combines the capabilities of
databases and constraint optimization solvers to derive
solutions to package queries. The core of our approach
is a set of translation rules that transform a package
query to an integer linear program. (3) We introduce an
offline data partitioning strategy allowing query
evaluation to scale to large data sizes. (4) We
introduce SketchRefine, a scalable algorithm for
package evaluation, with strong approximation
guarantees [(1 \pm \varepsilon )(1 ?)-factor
approximation]. (5) We present a method for
parallelizing the Refine phase of SketchRefine. (6) We
present an empirical study of the efficiency gains of
providing integer solvers with starting solutions. (7)
We present extensive experiments over real-world and
benchmark data. The results demonstrate that our
methods are effective at deriving high-quality package
results and achieve runtime performance that is an
order of magnitude faster than directly using ILP
solvers over large datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Elgohary:2018:CLA,
author = "Ahmed Elgohary and Matthias Boehm and Peter J. Haas
and Frederick R. Reiss and Berthold Reinwald",
title = "Compressed linear algebra for large-scale machine
learning",
journal = j-VLDB-J,
volume = "27",
number = "5",
pages = "719--744",
month = oct,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0478-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Oct 4 06:40:44 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Large-scale machine learning algorithms are often
iterative, using repeated read-only data access and
I/O-bound matrix--vector multiplications to converge to
an optimal model. It is crucial for performance to fit
the data into single-node or distributed main memory
and enable fast matrix--vector operations on in-memory
data. General-purpose, heavy- and lightweight
compression techniques struggle to achieve both good
compression ratios and fast decompression speed to
enable block-wise uncompressed operations. Therefore,
we initiate work --- inspired by database compression
and sparse matrix formats --- on value-based compressed
linear algebra (CLA), in which heterogeneous,
lightweight database compression techniques are applied
to matrices, and then linear algebra operations such as
matrix--vector multiplication are executed directly on
the compressed representation. We contribute effective
column compression schemes, cache-conscious operations,
and an efficient sampling-based compression algorithm.
Our experiments show that CLA achieves in-memory
operations performance close to the uncompressed case
and good compression ratios, which enables fitting
substantially larger datasets into available memory. We
thereby obtain significant end-to-end performance
improvements up to $ 9.2 \times $.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chai:2018:POB,
author = "Chengliang Chai and Guoliang Li and Jian Li and Dong
Deng and Jianhua Feng",
title = "A partial-order-based framework for cost-effective
crowdsourced entity resolution",
journal = j-VLDB-J,
volume = "27",
number = "6",
pages = "745--770",
month = dec,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0509-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 5 08:07:20 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Crowdsourced entity resolution has recently attracted
significant attentions because it can harness the
wisdom of crowd to improve the quality of entity
resolution. However, existing techniques either cannot
achieve high quality or incur huge monetary costs. To
address these problems, we propose a cost-effective
crowdsourced entity resolution framework, which
significantly reduces the monetary cost while keeping
high quality. We first define a partial order on the
pairs of records. Then, we select a pair as a question
and ask the crowd to check whether the records in the
pair refer to the same entity. After getting the answer
of this pair, we infer the answers of other pairs based
on the partial order. Next, we iteratively select pairs
without answers to ask until we get the answers of all
pairs. We devise effective algorithms to judiciously
select the pairs to ask in order to minimize the number
of asked pairs. To further reduce the cost, we propose
a grouping technique to group the pairs and we only ask
one pair instead of all pairs in each group. We develop
error-tolerant techniques to tolerate the errors
introduced by the partial order and the crowd. We also
study the budget-aware entity resolution, which, given
a budget, finds the maximum number of matching pairs
within the budget, and propose effective optimization
techniques. Experimental results show that our method
reduces the cost to 1.25\% of existing approaches (or
existing approaches take 80\times 80$ \times $ monetary
cost of our method) while not sacrificing the
quality.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Roblot:2018:PCC,
author = "Tania Roblot and Miika Hannula and Sebastian Link",
title = "Probabilistic Cardinality Constraints",
journal = j-VLDB-J,
volume = "27",
number = "6",
pages = "771--795",
month = dec,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0511-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 5 08:07:20 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Probabilistic databases address the requirements of
applications that produce large collections of
uncertain data. They should provide declarative means
to control the integrity of data. Cardinality
constraints, in particular, control the occurrences of
data patterns by declaring in how many records a
combination of data values can occur. We propose
cardinality constraints on probabilistic data, which
stipulate lower bounds on the marginal probability by
which a cardinality constraint holds. We investigate
limits and opportunities for automating their use in
integrity control. This includes hardness results for
their validation, axiomatic and efficient algorithmic
characterisations of their implication problem, and an
algorithm that computes succinct semantic summaries for
any collection of these constraints. Experiments
complement our theoretical analysis on the time and
space complexity of computing semantic summaries,
suggesting that their computation provides the basis to
acquire meaningful constraints. We also establish
evidence that probabilistic functional and inclusion
dependencies cannot be managed as simply as
probabilistic cardinality constraints.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bress:2018:GCC,
author = "Sebastian Bre{\ss} and Bastian K{\"o}cher and Henning
Funke and Steffen Zeuch and Tilmann Rabl and Volker
Markl",
title = "Generating custom code for efficient query execution
on heterogeneous processors",
journal = j-VLDB-J,
volume = "27",
number = "6",
pages = "797--822",
month = dec,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0512-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 5 08:07:20 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Processor manufacturers build increasingly specialized
processors to mitigate the effects of the power wall in
order to deliver improved performance. Currently,
database engines have to be manually optimized for each
processor which is a costly and error- prone process.
In this paper, we propose concepts to adapt to and to
exploit the performance enhancements of modern
processors automatically. Our core idea is to create
processor-specific code variants and to learn a
well-performing code variant for each processor. These
code variants leverage various parallelization
strategies and apply both generic- and
processor-specific code transformations. Our
experimental results show that the performance of code
variants may diverge up to two orders of magnitude. In
order to achieve peak performance, we generate custom
code for each processor. We show that our approach
finds an efficient custom code variant for multi-core
CPUs, GPUs, and MICs.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zoumpatianos:2018:GDS,
author = "Kostas Zoumpatianos and Yin Lou and Ioana Ileana and
Themis Palpanas and Johannes Gehrke",
title = "Generating data series query workloads",
journal = j-VLDB-J,
volume = "27",
number = "6",
pages = "823--846",
month = dec,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0513-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 5 08:07:20 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Data series (including time series) has attracted lots
of interest in recent years. Most of the research has
focused on how to efficiently support similarity or
nearest neighbor queries over large data series
collections (an important data mining task), and
several data series summarization and indexing methods
have been proposed in order to solve this problem. Up
to this point, very little attention has been paid to
properly evaluating such index structures, with most
previous works relying solely on randomly selected data
series to use as queries. In this work, we show that
random workloads are inherently not suitable for the
task at hand and we argue that there is a need for
carefully generating query workloads. We define
measures that capture the characteristics of queries,
and we propose a method for generating workloads with
the desired properties, that is, effectively evaluating
and comparing data series summarizations and indexes.
In our experimental evaluation, with carefully
controlled query workloads, we shed light on key
factors affecting the performance of nearest neighbor
search in large data series collections. This is the
first paper that introduces a method for quantifying
hardness of data series queries, as well as the ability
to generate queries of predefined hardness.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{To:2018:SSM,
author = "Quoc-Cuong To and Juan Soto and Volker Markl",
title = "A survey of state management in big data processing
systems",
journal = j-VLDB-J,
volume = "27",
number = "6",
pages = "847--872",
month = dec,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0514-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 5 08:07:20 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The concept of state and its applications vary widely
across big data processing systems. This is evident in
both the research literature and existing systems, such
as Apache Flink, Apache Heron, Apache Samza, Apache
Spark, and Apache Storm. Given the pivotal role that
state management plays, particularly, for iterative
batch and stream processing, in this survey, we present
examples of state as an enabler, discuss the
alternative approaches used to handle and implement
state, capture the many facets of state management, and
highlight new research directions. Our aim is to
provide insight into disparate state management
techniques, motivate others to pursue research in this
area, and draw attention to open problems.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Liu:2018:ACE,
author = "Yuchen Liu and Hai Liu and Dongqing Xiao and Mohamed
Y. Eltabakh",
title = "Adaptive correlation exploitation in big data query
optimization",
journal = j-VLDB-J,
volume = "27",
number = "6",
pages = "873--898",
month = dec,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0515-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 5 08:07:20 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Correlations among the data attributes are abundant
and inherent in most application domains. These
correlations, if managed in systematic and efficient
ways, would enable various optimization opportunities.
Unfortunately, the state-of-art techniques are all
heavily tailored toward optimizing factors intrinsic to
relational databases, e.g., predicate selectivity,
random I/O accesses, and secondary indexes, which are
mostly not applicable to the modern big data
infrastructures, e.g., Hadoop and Spark. In this paper,
we propose the EXORD^++ system for exploiting the
data's correlations in big data query optimization.
EXORD^++ supports two types of correlations; hard
(which does not allow for exceptions) and soft (which
allows for exceptions). We introduce a three-phase
approach for managing soft correlations including: (1)
validating and judging the worthiness of soft
correlations, (2) selecting and preparing the soft
correlations for deployment, and (3) exploiting the
correlations in query optimization. EXORD^++ introduces
a novel cost-benefit model for adaptively selecting the
most beneficial soft correlations given a query
workload. We show the complexity of this problem
(NP-Hard) and propose a heuristic to efficiently solve
it in a polynomial time. Moreover, we present
incremental maintenance algorithms for efficiently
updating the system's state under data appends and
workload changes. EXORD^++ prototype is implemented as
an extension to the Hive engine on top of Hadoop. The
experimental evaluation shows the potential of EXORD^++
in achieving more than 10x speedup while introducing
minimal storage overheads.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2018:EEM,
author = "Tianzheng Wang and Ryan Johnson and Alan Fekete and
Ippokratis Pandis",
title = "Erratum to: {Efficiently making (almost) any
concurrency control mechanism serializable}",
journal = j-VLDB-J,
volume = "27",
number = "6",
pages = "899--900",
month = dec,
year = "2018",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-017-0471-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 5 08:07:20 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See \cite{Wang:2017:EMA}.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Rahman:2019:OGF,
author = "Habibur Rahman and Senjuti Basu Roy and Saravanan
Thirumuruganathan and Sihem Amer-Yahia and Gautam Das",
title = "Optimized group formation for solving collaborative
tasks",
journal = j-VLDB-J,
volume = "28",
number = "1",
pages = "1--23",
month = feb,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0516-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 5 08:07:20 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Many popular applications, such as collaborative
document editing, sentence translation, or citizen
science, resort to collaborative crowdsourcing, a
special form of human-based computing, where, crowd
workers with appropriate skills and expertise are
required to form groups to solve complex tasks. While
there has been extensive research on workers' task
assignment for traditional microtask-based
crowdsourcing, they often ignore the critical aspect of
collaboration. Central to any collaborative
crowdsourcing process is the aspect of solving
collaborative tasks that requires successful
collaboration among the workers. Our formalism
considers two main collaboration-related
factors--affinity and upper critical
mass--appropriately adapted from organizational science
and social theories. Our contributions are threefold.
First, we formalize the notion of collaboration among
crowd workers and propose a comprehensive optimization
model for task assignment in a collaborative
crowdsourcing environment. Next, we study the hardness
of the task assignment optimization problem and propose
a series of efficient exact and approximation
algorithms with provable theoretical guarantees.
Finally, we present a detailed set of experimental
results stemming from two real-world collaborative
crowdsourcing application using Amazon Mechanical
Turk.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wu:2019:VFS,
author = "Zhiqiang Wu and Kenli Li",
title = "{VBTree}: forward secure conjunctive queries over
encrypted data for cloud computing",
journal = j-VLDB-J,
volume = "28",
number = "1",
pages = "25--46",
month = feb,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0517-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 5 08:07:20 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This paper concerns the fundamental problem of
processing conjunctive keyword queries over an
outsourced data table on untrusted public clouds in a
privacy-preserving manner. The data table can be
properly implemented with tree-based searchable
symmetric encryption schemes, such as the known Keyword
Red---Black tree and the Indistinguishable Bloom-filter
Tree in ICDE'17. However, as for these trees, there
still exist many limitations to support sub-linear time
updates. One of the reasons is that their tree branches
are directly exposed to the cloud. To achieve efficient
conjunctive queries while supporting dynamic updates,
we introduce a novel tree data structure called virtual
binary tree (VBTree). Our key design is to organize
indexing elements into the VBTree in a top-down
fashion, without storing any tree branches and tree
nodes. The tree only exists in a logical view, and all
of the elements are actually stored in a hash table. To
achieve forward privacy, which is discussed by Bost in
CCS'16, we also propose a storage mechanism called
version control repository (VCR), to record and control
versions of keywords and queries. VCR has a smaller
client-side storage compared to other forward-private
schemes. With our proposed approach, data elements can
be quickly searched while the index can be privately
updated. The security of the VBTree is formally proved
under the IND-CKA2 model. We test our scheme on a real
e-mail dataset and a user location dataset. The testing
results demonstrate its high efficiency and scalability
in both searching and updating processes.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lee:2019:PFP,
author = "Seokki Lee and Bertram Lud{\"a}scher and Boris
Glavic",
title = "{PUG}: a framework and practical implementation for
why and why-not provenance",
journal = j-VLDB-J,
volume = "28",
number = "1",
pages = "47--71",
month = feb,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0518-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 5 08:07:20 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Explaining why an answer is (or is not) returned by a
query is important for many applications including
auditing, debugging data and queries, and answering
hypothetical questions about data. In this work, we
present the first practical approach for answering such
questions for queries with negation (first-order
queries). Specifically, we introduce a graph-based
provenance model that, while syntactic in nature,
supports reverse reasoning and is proven to encode a
wide range of provenance models from the literature.
The implementation of this model in our PUG (Provenance
Unification through Graphs) system takes a provenance
question and Datalog query as an input and generates a
Datalog program that computes an explanation, i.e., the
part of the provenance that is relevant to answer the
question. Furthermore, we demonstrate how a desirable
factorization of provenance can be achieved by
rewriting an input query. We experimentally evaluate
our approach demonstrating its efficiency.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2019:SSS,
author = "Wenlu Wang and Ji Zhang and Min-Te Sun and Wei-Shinn
Ku",
title = "A scalable spatial skyline evaluation system utilizing
parallel independent region groups",
journal = j-VLDB-J,
volume = "28",
number = "1",
pages = "73--98",
month = feb,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0519-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 5 08:07:20 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/multithreading.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "This research presents two parallel solutions to
efficiently address spatial skyline queries. First, we
propose a novel concept called independent regions for
parallelizing the process of spatial skyline
evaluation. Spatial skyline candidates in an
independent region do not depend on any data point in
other independent regions. Then, we propose a GPU-based
solution. We use multi-level independent region
group-based parallel filter to support efficient
multi-threading spatial skyline non-candidate
elimination. Beyond that, we propose comparable region
to accelerate non-candidate elimination in each
independent region. Secondly, we propose a
MapReduce-based solution. We generate the convex hull
of query points in the first MapReduce phase. In the
second phase, we calculate independent regions based on
the input data points and the convex hull of the query
points. With the independent regions, spatial skylines
are evaluated in parallel in the third phase, in which
data points are partitioned by their associated
independent regions in map functions, and spatial
skyline candidates are calculated by reduce functions.
The results of the spatial skyline queries are the
union of outputs from the reduce functions. Our
experimental results show that GPU multi-threading
scheme is very efficient on small-scale input datasets.
On the contrary, MapReduce scheme performs very well on
large-scale input datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2019:APS,
author = "Yue Wang and Lei Chen and Yulin Che and Qiong Luo",
title = "Accelerating pairwise {SimRank} estimation over static
and dynamic graphs",
journal = j-VLDB-J,
volume = "28",
number = "1",
pages = "99--122",
month = feb,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0521-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 5 08:07:20 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Measuring similarities among different vertices is a
fundamental problem in graph analysis. Among different
similarity measurements, SimRank is one of the most
promising and popular. In reality, instead of computing
the whole similarity matrix, people often issue SimRank
queries in a pairwise manner, each of which needs to
estimate an approximate SimRank value within a
specified accuracy for a given pair of nodes. These
pairwise SimRank queries are often processed on
real-life graphs, which typically evolve over time,
requiring efficient algorithms that can query pairwise
SimRank under dynamic graph updates. However, current
single-pair SimRank solutions are either static or
inefficient in handling dynamic cases with good-quality
results. Observing that the sample size is the major
factor that determines the efficiency and the accuracy
in Monte Carlo methods to estimate pairwise SimRank, in
this paper, we propose three algorithms to query
pairwise SimRank over static and dynamic graphs
efficiently, by using different sample reduction
strategies. The accuracy of our algorithms is
guaranteed by the different invariants we propose for
pairwise SimRank. We show that our algorithms
outperform the state-of-the-art static and dynamic
solutions for pairwise SimRank estimation.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhao:2019:EMC,
author = "Kaiqi Zhao and Gao Cong and Jin-Yao Chin and Rong
Wen",
title = "Exploring market competition over topics in
spatio-temporal document collections",
journal = j-VLDB-J,
volume = "28",
number = "1",
pages = "123--145",
month = feb,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0522-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Feb 5 08:07:20 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "With the prominence of location-based services and
social networks in recent years, huge amounts of
spatio-temporal document collections (e.g., geo-tagged
tweets) have been generated. These data collections
often imply user's ideas on different products and thus
are helpful for business owners to explore hot topics
of their brands and the competition relation to other
brands in different spatial regions during different
periods. In this work, we aim to mine the topics and
the market competition of different brands over each
topic for a category of business (e.g., coffeehouses)
from spatio-temporal documents within a user-specified
region and time period. To support such spatio-temporal
search online in an exploratory manner, we propose a
novel framework equipped by (1) a generative model for
mining topics and market competition, (2) an
Octree-based off-line pre-training method for the model
and (3) an efficient algorithm for combining
pre-trained models to return the topics and market
competition on each topic within a user-specified pair
of region and time span. Extensive experiments show
that our framework is able to improve the runtime by up
to an order of magnitude compared with baselines while
achieving similar model quality in terms of training
log-likelihood.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Picado:2019:LSE,
author = "Jose Picado and Arash Termehchy and Alan Fern and
Parisa Ataei",
title = "Logical scalability and efficiency of relational
learning algorithms",
journal = j-VLDB-J,
volume = "28",
number = "2",
pages = "147--171",
month = apr,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0523-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon May 20 17:17:01 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Relational learning algorithms learn the definition of
a new relation in terms of existing relations in the
database. The same database may be represented under
different schemas for various reasons, such as
efficiency, data quality, and usability. Unfortunately,
the output of current relational learning algorithms
tends to vary quite substantially over the choice of
schema, both in terms of learning accuracy and
efficiency. We introduce the property of schema
independence of relational learning algorithms, and
study both the theoretical and empirical dependence of
existing algorithms on the common class of (de)
composition schema transformations. We show
theoretically and empirically that current relational
learning algorithms are generally not schema
independent. We propose Castor, a relational learning
algorithm that achieves schema independence.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Aluc:2019:BSC,
author = "G{\"u}nes Alu{\c{c}} and M. Tamer {\"O}zsu and
Khuzaima Daudjee",
title = "Building self-clustering {RDF} databases using
{Tunable-LSH}",
journal = j-VLDB-J,
volume = "28",
number = "2",
pages = "173--195",
month = apr,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0530-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon May 20 17:17:01 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/hash.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The Resource Description Framework (RDF) is a W3C
standard for representing graph-structured data, and
SPARQL is the standard query language for RDF. Recent
advances in information extraction, linked data
management and the Semantic Web have led to a rapid
increase in both the volume and the variety of RDF data
that are publicly available. As businesses start to
capitalize on RDF data, RDF data management systems are
being exposed to workloads that are far more diverse
and dynamic than what they were designed to handle.
Consequently, there is a growing need for developing
workload-adaptive and self-tuning RDF data management
systems. To realize this objective, we introduce a fast
and efficient method for dynamically clustering records
in an RDF data management system. Specifically, we
assume nothing about the workload upfront, but as
SPARQL queries are executed, we keep track of records
that are co-accessed by the queries in the workload and
physically cluster them. To decide dynamically and in
constant-time where a record needs to be placed in the
storage system, we develop a new locality-sensitive
hashing (LSH) scheme, Tunable-LSH. Using Tunable-LSH,
records that are co-accessed across similar sets of
queries can be hashed to the same or nearby physical
pages in the storage system. What sets Tunable-LSH
apart from existing LSH schemes is that it can
auto-tune to achieve the aforementioned clustering
objective with high accuracy even when the workloads
change. Experimental evaluation of Tunable-LSH in an
RDF data management system as well as in a standalone
hashtable shows end-to-end performance gains over
existing solutions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhou:2019:RTC,
author = "Xiangmin Zhou and Dong Qin and Lei Chen and Yanchun
Zhang",
title = "Real-time context-aware social media recommendation",
journal = j-VLDB-J,
volume = "28",
number = "2",
pages = "197--219",
month = apr,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0524-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon May 20 17:17:01 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Social media recommendation has attracted great
attention due to its wide applications in online
advertisement and entertainment, etc. Since contexts
highly affect social user preferences, great effort has
been put into context-aware recommendation in recent
years. However, existing techniques cannot capture the
optimal context information that is most discriminative
and compact from a large number of available features
flexibly for effective and efficient context-aware
social recommendation. To address this issue, we
propose a generic framework for context-aware
recommendation in shared communities, which exploits
the characteristics of media content and contexts.
Specifically, we first propose a novel approach based
on the correlation between a feature and a group of
other ones for selecting the optimal features used in
recommendation, which fully removes the redundancy.
Then, we propose a graph-based model called
content---context interaction graph, by analysing the
metadata content and social contexts, and the
interaction between attributes. Finally, we design
hash-based index over Apache Storm for organizing and
searching the media database in real time. Extensive
experiments have been conducted over large real media
collections to prove the high effectiveness and
efficiency of our proposed framework.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ntaflos:2019:UAB,
author = "Lefteris Ntaflos and George Trimponias and Dimitris
Papadias",
title = "A unified agent-based framework for constrained graph
partitioning",
journal = j-VLDB-J,
volume = "28",
number = "2",
pages = "221--241",
month = apr,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0526-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon May 20 17:17:01 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Social networks offer various services such as
recommendations of social events, or delivery of
targeted advertising material to certain users. In this
work, we focus on a specific type of services modeled
as constrained graph partitioning (CGP). CGP assigns
users of a social network to a set of classes with
bounded capacities so that the similarity and the
social costs are minimized. The similarity cost is
proportional to the dissimilarity between a user and
his class, whereas the social cost is measured in terms
of friends that are assigned to different classes. In
this work, we investigate two solutions for CGP. The
first utilizes a game-theoretic framework, where each
user constitutes a player that wishes to minimize his
own social and similarity cost. The second employs
local search, and aims at minimizing the global cost.
We show that the two approaches can be unified under a
common agent-based framework that allows for two types
of deviations. In a unilateral deviation, an agent
switches to a new class, whereas in a bilateral
deviation a pair of agents exchange their classes. We
develop a number of optimization techniques to improve
result quality and facilitate efficiency. Our
experimental evaluation on real datasets demonstrates
that the proposed methods always outperform the state
of the art in terms of solution quality, while they are
up to an order of magnitude faster.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Omidvar-Tehrani:2019:UGA,
author = "Behrooz Omidvar-Tehrani and Sihem Amer-Yahia and Ria
Mae Borromeo",
title = "User group analytics: hypothesis generation and
exploratory analysis of user data",
journal = j-VLDB-J,
volume = "28",
number = "2",
pages = "243--266",
month = apr,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0527-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon May 20 17:17:01 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "User data is becoming increasingly available in
multiple domains ranging from the social Web to retail
store receipts. User data is described by user
demographics (e.g., age, gender, occupation) and user
actions (e.g., rating a movie, publishing a paper,
following a medical treatment). The analysis of user
data is appealing to scientists who work on population
studies, online marketing, recommendations, and
large-scale data analytics. User data analytics usually
relies on identifying group-level behavior such as
``Asian women who publish regularly in databases.''
Group analytics addresses peculiarities of user data
such as noise and sparsity to enable insights. In this
paper, we introduce a framework for user group
analytics by developing several components which cover
the life cycle of user groups. We provide two different
analytical environments to support ``hypothesis
generation'' and ``exploratory analysis'' on user
groups. Experiments on datasets with different
characteristics show the usability and efficiency of
our group analytics framework.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2019:LSR,
author = "Xubo Wang and Lu Qin and Xuemin Lin and Ying Zhang and
Lijun Chang",
title = "Leveraging set relations in exact and dynamic set
similarity join",
journal = j-VLDB-J,
volume = "28",
number = "2",
pages = "267--292",
month = apr,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0529-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon May 20 17:17:01 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Set similarity join, which finds all the similar set
pairs from two collections of sets, is a fundamental
problem with a wide range of applications. Existing
works study both exact set similarity join and
approximate similarity join problems. In this paper, we
focus on the exact set similarity join problem. The
existing solutions for exact set similarity join follow
a filtering-verification framework, which generates a
list of candidate pairs through scanning indexes in the
filtering phase and reports those similar pairs in the
verification phase. Though much research has been
conducted on this problem, set relations have not been
well studied on improving the algorithm efficiency
through computational cost sharing. Therefore, in this
paper, we explore the set relations in different levels
to reduce the overall computational cost. First, it has
been shown that most of the computational time is spent
on the filtering phase, which can be quadratic to the
number of sets in the worst case for the existing
solutions. Thus, we explore index-level set relations
to reduce the filtering cost while keeping the same
filtering power. We achieve this by grouping related
sets into blocks in the index and skipping useless
index probes in joins. Second, we explore answer-level
set relations to further improve the algorithm based on
the intuition that if two sets are similar, their
answers may have a large overlap. We derive an
algorithm which incrementally generates the answer of
one set from an already computed answer of another
similar set rather than compute the answer from scratch
to reduce the computational cost. In addition,
considering that in real applications, the data are
usually updated dynamically, we extend our techniques
and design efficient algorithms to incrementally update
the join result when any element in the sets is
updated. Finally, we conduct extensive performance
studies using 21 real datasets with various data
properties from a wide range of domains. The
experimental results demonstrate that our algorithm
outperforms all the existing algorithms across all
datasets.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Amer-Yahia:2019:TID,
author = "Sihem Amer-Yahia and Lei Chen and Ren{\'e}e J.
Miller",
title = "Thematic issue on data management for graphs",
journal = j-VLDB-J,
volume = "28",
number = "3",
pages = "293--294",
month = jun,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00543-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 2 07:30:39 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Cebiric:2019:SSG,
author = "{\v{S}}ejla Cebiri{\'c} and Fran{\c{c}}ois
Goasdou{\'e} and Haridimos Kondylakis and Dimitris
Kotzinos and Ioana Manolescu and Georgia Troullinou and
Mussab Zneika",
title = "Summarizing semantic graphs: a survey",
journal = j-VLDB-J,
volume = "28",
number = "3",
pages = "295--327",
month = jun,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0528-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 2 07:30:39 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "The explosion in the amount of the available RDF data
has lead to the need to explore, query and understand
such data sources. Due to the complex structure of RDF
graphs and their heterogeneity, the exploration and
understanding tasks are significantly harder than in
relational databases, where the schema can serve as a
first step toward understanding the structure.
Summarization has been applied to RDF data to
facilitate these tasks. Its purpose is to extract
concise and meaningful information from RDF knowledge
bases, representing their content as faithfully as
possible. There is no single concept of RDF summary,
and not a single but many approaches to build such
summaries; each is better suited for some uses, and
each presents specific challenges with respect to its
construction. This survey is the first to provide a
comprehensive survey of summarization method for
semantic RDF graphs. We propose a taxonomy of existing
works in this area, including also some closely related
works developed prior to the adoption of RDF in the
data management community; we present the concepts at
the core of each approach and outline their main
technical aspects and implementation. We hope the
survey will help readers understand this scientifically
rich area and identify the most pertinent summarization
method for a variety of usage scenarios.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Demirci:2019:CAP,
author = "Gunduz Vehbi Demirci and Hakan Ferhatosmanoglu and
Cevdet Aykanat",
title = "Cascade-aware partitioning of large graph databases",
journal = j-VLDB-J,
volume = "28",
number = "3",
pages = "329--350",
month = jun,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0531-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 2 07:30:39 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Graph partitioning is an essential task for scalable
data management and analysis. The current partitioning
methods utilize the structure of the graph, and the
query log if available. Some queries performed on the
database may trigger further operations. For example,
the query workload of a social network application may
contain re-sharing operations in the form of cascades.
It is beneficial to include the potential cascades in
the graph partitioning objectives. In this paper, we
introduce the problem of cascade-aware graph
partitioning that aims to minimize the overall cost of
communication among parts/servers during cascade
processes. We develop a randomized solution that
estimates the underlying cascades, and use it as an
input for partitioning of large-scale graphs.
Experiments on 17 real social networks demonstrate the
effectiveness of the proposed solution in terms of the
partitioning objectives.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Das:2019:IMM,
author = "Apurba Das and Michael Svendsen and Srikanta
Tirthapura",
title = "Incremental maintenance of maximal cliques in a
dynamic graph",
journal = j-VLDB-J,
volume = "28",
number = "3",
pages = "351--375",
month = jun,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00540-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 2 07:30:39 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We consider the maintenance of the set of all maximal
cliques in a dynamic graph that is changing through the
addition or deletion of edges. We present nearly tight
bounds on the magnitude of change in the set of maximal
cliques when edges are added to the graph, as well as
the first change-sensitive algorithm for incremental
clique maintenance under edge additions, whose runtime
is proportional to the magnitude of the change in the
set of maximal cliques, when the number of edges added
is small. Our algorithm can also be applied to the
decremental case, when edges are deleted from the
graph. We present experimental results showing these
algorithms are efficient in practice and are faster
than prior work by two to three orders of magnitude.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wen:2019:ESG,
author = "Dong Wen and Lu Qin and Ying Zhang and Lijun Chang and
Xuemin Lin",
title = "Efficient structural graph clustering: an index-based
approach",
journal = j-VLDB-J,
volume = "28",
number = "3",
pages = "377--399",
month = jun,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00541-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 2 07:30:39 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Graph clustering is a fundamental problem widely
applied in many applications. The structural graph
clustering (\mathsf {SCAN}SCAN) method obtains not only
clusters but also hubs and outliers. However, the
clustering results heavily depend on two parameters,
\epsilon \in and \mu \mu , while the optimal parameter
setting depends on different graph properties and
various user requirements. In addition, all existing
\mathsf {SCAN}SCAN solutions need to scan at least the
whole graph, even if only a small number of vertices
belong to clusters. In this paper, we propose an
index-based method for \mathsf {SCAN}SCAN. Based on our
index, we cluster the graph for any \epsilon \in and
\mu \mu in O(\sum _{C\in \mathbb {C}}|E_C|)O(?C?C|EC|)
time, where \mathbb {C} C is the result set of all
clusters and |E_C||EC| is the number of edges in a
specific cluster CC. In other words, the time spent on
computing structural clustering depends only on the
result size, not on the size of the original graph. Our
index's space complexity is O(m), where m is the number
of edges in the graph. To handle dynamic graph updates,
we propose algorithms and several optimization
techniques for maintaining our index. We also design an
index for I/O efficient query processing. We conduct
extensive experiments to evaluate the performance of
all our proposed algorithms on 10 real-world networks,
with the largest one containing more than 1 billion
edges. The experimental results demonstrate that our
approaches significantly outperform existing
solutions.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yu:2019:SES,
author = "Weiren Yu and Xuemin Lin and Wenjie Zhang and Jian Pei
and Julie A. Mccann",
title = "{SimRank*}: effective and scalable pairwise similarity
search based on graph topology",
journal = j-VLDB-J,
volume = "28",
number = "3",
pages = "401--426",
month = jun,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0536-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Oct 2 07:30:39 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Given a graph, how can we quantify similarity between
two nodes in an effective and scalable way? SimRank is
an attractive measure of pairwise similarity based on
graph topologies. Its underpinning philosophy that
``two nodes are similar if they are pointed to (have
incoming edges) from similar nodes'' can be regarded as
an aggregation of similarities based on incoming paths.
Despite its popularity in various applications (e.g.,
web search and social networks), SimRank has an
undesirable trait, i.e., ``zero-similarity'': it
accommodates only the paths of equal length from a
common ``center'' node, whereas a large portion of
other paths are fully ignored. In this paper, we
propose an effective and scalable similarity model,
SimRank*, to remedy this problem. (1) We first provide
a sufficient and necessary condition of the
``zero-similarity'' problem that exists in Jeh and
Widom's SimRank model, Li et al. 's SimRank model,
Random Walk with Restart (RWR), and ASCOS++. (2) We
next present our treatment, SimRank*, which can resolve
this issue while inheriting the merit of the simple
SimRank philosophy. (3) We reduce the series form of
SimRank* to a closed form, which looks simpler than
SimRank but which enriches semantics without suffering
from increased computational overhead. This leads to an
iterative form of SimRank*, which requires O(Knm) time
and O(n^2)O(n2) memory for computing all (n^2)(n2)
pairs of similarities on a graph of n nodes and m edges
for K iterations. (4) To improve the computational time
of SimRank* further, we leverage a novel clustering
strategy via edge concentration. Due to its
NP-hardness, we devise an efficient heuristic to speed
up all-pairs SimRank* computation to
O(Kn{\tilde{m}})O(Knm~) time, where {\tilde{m}}m~ is
generally much smaller than m. (5) To scale SimRank* on
billion-edge graphs, we propose two memory-efficient
single-source algorithms, i.e., ss-gSR* for geometric
SimRank*, and ss-eSR* for exponential SimRank*, which
can retrieve similarities between all n nodes and a
given query on an as-needed basis. This significantly
reduces the O(n^2)O(n2) memory of all-pairs search to
either O(Kn + {\tilde{m}})O(Kn+m~) for geometric
SimRank*, or O(n + {\tilde{m}})O(n+m~) for exponential
SimRank*, without any loss of accuracy, where
{\tilde{m}} \ll n^2m~?n2. (6) We also compare SimRank*
with another remedy of SimRank that adds self-loops on
each node and demonstrate that SimRank* is more
effective. (7) Using real and synthetic datasets, we
empirically verify the richer semantics of SimRank*,
and validate its high computational efficiency and
scalability on large graphs with billions of edges.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Valdes:2019:FEM,
author = "Fabio Vald{\'e}s and Ralf Hartmut G{\"u}ting",
title = "A framework for efficient multi-attribute movement
data analysis",
journal = j-VLDB-J,
volume = "28",
number = "4",
pages = "427--449",
month = aug,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0525-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-018-0525-6",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Psaropoulos:2019:ICS,
author = "Georgios Psaropoulos and Thomas Legler and Norman May
and Anastasia Ailamaki",
title = "Interleaving with coroutines: a systematic and
practical approach to hide memory latency in index
joins",
journal = j-VLDB-J,
volume = "28",
number = "4",
pages = "451--471",
month = aug,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0533-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-018-0533-6",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bronselaer:2019:CRT,
author = "Antoon Bronselaer and Christophe Billiet and Robin {De
Mol} and Joachim Nielandt and Guy {De Tr{\'e}}",
title = "Compact representations of temporal databases",
journal = j-VLDB-J,
volume = "28",
number = "4",
pages = "473--496",
month = aug,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0535-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-018-0535-4",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Baazizi:2019:PSI,
author = "Mohamed-Amine Baazizi and Dario Colazzo and Giorgio
Ghelli and Carlo Sartiani",
title = "Parametric schema inference for massive {JSON}
datasets",
journal = j-VLDB-J,
volume = "28",
number = "4",
pages = "497--521",
month = aug,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0532-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-018-0532-7",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2019:SCG,
author = "Yuan Li and Ahmed Eldawy and Jie Xue and Nadezda
Knorozova and Mohamed F. Mokbel and Ravi Janardan",
title = "Scalable computational geometry in {MapReduce}",
journal = j-VLDB-J,
volume = "28",
number = "4",
pages = "523--548",
month = aug,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-018-0534-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-018-0534-5",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Park:2019:FSM,
author = "Namyong Park and Sejoon Oh and U Kang",
title = "Fast and scalable method for distributed {Boolean}
tensor factorization",
journal = j-VLDB-J,
volume = "28",
number = "4",
pages = "549--574",
month = aug,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00538-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00538-z",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Frazzetto:2019:PAS,
author = "Davide Frazzetto and Thomas Dyhre Nielsen and Torben
Bach Pedersen and Laurynas {\v{S}}ik{\v{s}}nys",
title = "Prescriptive analytics: a survey of emerging trends
and technologies",
journal = j-VLDB-J,
volume = "28",
number = "4",
pages = "575--595",
month = aug,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00539-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00539-y",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhu:2019:FDC,
author = "Rong Zhu and Zhaonian Zou and Jianzhong Li",
title = "Fast diversified coherent core search on multi-layer
graphs",
journal = j-VLDB-J,
volume = "28",
number = "4",
pages = "597--622",
month = aug,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00542-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00542-3",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{AlHasanHaldar:2019:LPL,
author = "Nur {Al Hasan Haldar} and Jianxin Li and Mark Reynolds
and Timos Sellis and Jeffrey Xu Yu",
title = "Location prediction in large-scale social networks: an
in-depth benchmarking study",
journal = j-VLDB-J,
volume = "28",
number = "5",
pages = "623--648",
month = oct,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00553-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00553-0",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fang:2019:EPM,
author = "Yixiang Fang and Yun Li and Reynold Cheng and Nikos
Mamoulis and Gao Cong",
title = "Evaluating pattern matching queries for spatial
databases",
journal = j-VLDB-J,
volume = "28",
number = "5",
pages = "649--673",
month = oct,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00550-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00550-3",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kim:2019:ETD,
author = "Jinhyun Kim and Jun-Ki Min and Kyuseok Shim",
title = "Efficient two-dimensional {Haar$^+$} synopsis
construction for the maximum absolute error measure",
journal = j-VLDB-J,
volume = "28",
number = "5",
pages = "675--701",
month = oct,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00551-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00551-2",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Theocharidis:2019:SEM,
author = "Konstantinos Theocharidis and John Liagouris and Nikos
Mamoulis and Panagiotis Bouros and Manolis Terrovitis",
title = "{SRX}: efficient management of spatial {RDF} data",
journal = j-VLDB-J,
volume = "28",
number = "5",
pages = "703--733",
month = oct,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00554-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00554-z",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yang:2019:FAS,
author = "Tong Yang and Jie Jiang and Yang Zhou and Long He and
Jinyang Li and Bin Cui and Steve Uhlig and Xiaoming
Li",
title = "Fast and accurate stream processing by filtering the
cold",
journal = j-VLDB-J,
volume = "28",
number = "5",
pages = "735--763",
month = oct,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00560-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00560-1",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2019:ESW,
author = "Wentao Li and Miao Qiao and Lu Qin and Ying Zhang and
Lijun Chang and Xuemin Lin",
title = "Eccentricities on small-world networks",
journal = j-VLDB-J,
volume = "28",
number = "5",
pages = "765--792",
month = oct,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00566-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00566-9",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Affolter:2019:CSR,
author = "Katrin Affolter and Kurt Stockinger and Abraham
Bernstein",
title = "A comparative survey of recent natural language
interfaces for databases",
journal = j-VLDB-J,
volume = "28",
number = "5",
pages = "793--819",
month = oct,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00567-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00567-8;
http://link.springer.com/content/pdf/10.1007/s00778-019-00567-8.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Cheng:2019:PCD,
author = "Zhinan Cheng and Qun Huang and Patrick P. C. Lee",
title = "On the performance and convergence of distributed
stream processing via approximate fault tolerance",
journal = j-VLDB-J,
volume = "28",
number = "5",
pages = "821--846",
month = oct,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00565-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00565-w",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kondylakis:2019:CSS,
author = "Haridimos Kondylakis and Niv Dayan and Kostas
Zoumpatianos and Themis Palpanas",
title = "{Coconut}: sortable summarizations for scalable
indexes over static and streaming data series",
journal = j-VLDB-J,
volume = "28",
number = "6",
pages = "847--869",
month = dec,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00573-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00573-w",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2019:EDR,
author = "Tianming Zhang and Yunjun Gao and Lu Chen and Wei Guo
and Shiliang Pu and Baihua Zheng and Christian S.
Jensen",
title = "Efficient distributed reachability querying of massive
temporal graphs",
journal = j-VLDB-J,
volume = "28",
number = "6",
pages = "871--896",
month = dec,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00572-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00572-x",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lin:2019:OPT,
author = "Xuelian Lin and Jiahao Jiang and Shuai Ma and Yimeng
Zuo and Chunming Hu",
title = "One-pass trajectory simplification using the
synchronous {Euclidean} distance",
journal = j-VLDB-J,
volume = "28",
number = "6",
pages = "897--921",
month = dec,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00575-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00575-8",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2019:PAS,
author = "Runhui Wang and Sibo Wang and Xiaofang Zhou",
title = "Parallelizing approximate single-source personalized
{PageRank} queries on shared memory",
journal = j-VLDB-J,
volume = "28",
number = "6",
pages = "923--940",
month = dec,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00576-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00576-7",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Langdale:2019:PGJ,
author = "Geoff Langdale and Daniel Lemire",
title = "Parsing gigabytes of {JSON} per second",
journal = j-VLDB-J,
volume = "28",
number = "6",
pages = "941--960",
month = dec,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00578-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00578-5",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ren:2019:SQI,
author = "Weilong Ren and Xiang Lian and Kambiz Ghazinour",
title = "Skyline queries over incomplete data streams",
journal = j-VLDB-J,
volume = "28",
number = "6",
pages = "961--985",
month = dec,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00577-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00577-6",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2019:ECD,
author = "Fan Zhang and Xuemin Lin and Ying Zhang and Lu Qin and
Wenjie Zhang",
title = "Efficient community discovery with user engagement and
similarity",
journal = j-VLDB-J,
volume = "28",
number = "6",
pages = "987--1012",
month = dec,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00579-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00579-4",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2020:VSS,
author = "Lei Chen and Sihem Amer-Yahia",
title = "{VLDB SI} survey editorial",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "1--2",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00598-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00598-1;
http://link.springer.com/content/pdf/10.1007/s00778-019-00598-1.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Su:2020:STD,
author = "Han Su and Shuncheng Liu and Bolong Zheng and Xiaofang
Zhou and Kai Zheng",
title = "A survey of trajectory distance measures and
performance evaluation",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "3--32",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00574-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00574-9",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fang:2020:MDA,
author = "Jian Fang and Yvo T. B. Mulder and Jan Hidders and
Jinho Lee and H. Peter Hofstee",
title = "In-memory database acceleration on {FPGAs}: a survey",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "33--59",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00581-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00581-w;
http://link.springer.com/content/pdf/10.1007/s00778-019-00581-w.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Malliaros:2020:CDN,
author = "Fragkiskos D. Malliaros and Christos Giatsidis and
Apostolos N. Papadopoulos and Michalis Vazirgiannis",
title = "The core decomposition of networks: theory, algorithms
and applications",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "61--92",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00587-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00587-4",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Qin:2020:MDV,
author = "Xuedi Qin and Yuyu Luo and Nan Tang and Guoliang Li",
title = "Making data visualization more efficient and
effective: a survey",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "93--117",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00588-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00588-3",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Rahman:2020:EID,
author = "Protiva Rahman and Lilong Jiang and Arnab Nandi",
title = "Evaluating interactive data systems",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "119--146",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00589-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00589-2",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xie:2020:ESR,
author = "Min Xie and Raymond Chi-Wing Wong and Ashwin Lall",
title = "An experimental survey of regret minimization query
and variants: bridging the best worlds between top-$k$
query and skyline query",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "147--175",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00570-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00570-z",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Magdy:2020:MDM,
author = "Amr Magdy and Laila Abdelhafeez and Yunfan Kang and
Eric Ong and Mohamed F. Mokbel",
title = "Microblogs data management: a survey",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "177--216",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00569-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00569-6",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Tong:2020:SCS,
author = "Yongxin Tong and Zimu Zhou and Yuxiang Zeng and Lei
Chen and Cyrus Shahabi",
title = "Spatial crowdsourcing: a survey",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "217--250",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00568-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00568-7",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chapman:2020:DSS,
author = "Adriane Chapman and Elena Simperl and Laura Koesten
and George Konstantinidis and Luis-Daniel
Ib{\'a}{\~n}ez and Emilia Kacprzak and Paul Groth",
title = "Dataset search: a survey",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "251--272",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00564-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00564-x;
http://link.springer.com/content/pdf/10.1007/s00778-019-00564-x.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fevgas:2020:IFS,
author = "Athanasios Fevgas and Leonidas Akritidis and
Panayiotis Bozanis and Yannis Manolopoulos",
title = "Indexing in flash storage devices: a survey on
challenges, current approaches, and future trends",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "273--311",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00559-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00559-8",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Giatrakos:2020:CER,
author = "Nikos Giatrakos and Elias Alevizos and Alexander
Artikis and Antonios Deligiannakis and Minos
Garofalakis",
title = "Complex event recognition in the {Big Data} era: a
survey",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "313--352",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00557-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00557-w",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fang:2020:SCS,
author = "Yixiang Fang and Xin Huang and Lu Qin and Ying Zhang
and Wenjie Zhang and Reynold Cheng and Xuemin Lin",
title = "A survey of community search over big graphs",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "353--392",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00556-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See correction \cite{Fang:2020:CSC}.",
URL = "http://link.springer.com/article/10.1007/s00778-019-00556-x",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Luo:2020:LBS,
author = "Chen Luo and Michael J. Carey",
title = "{LSM}-based storage techniques: a survey",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "393--418",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00555-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00555-y",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Blumenthal:2020:CHG,
author = "David B. Blumenthal and Nicolas Boria and Johann
Gamper and S{\'e}bastien Bougleux and Luc Brun",
title = "Comparing heuristics for graph edit distance
computation",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "419--458",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00544-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00544-1",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2020:EMM,
author = "Xinhong Chen and Qing Li",
title = "Event modeling and mining: a long journey toward
explainable events",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "459--482",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00545-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00545-0",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Boncz:2020:SIB,
author = "Peter Boncz and Kenneth Salem",
title = "Special issue on best papers of {VLDB 2017}",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "483--484",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00600-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00600-w;
http://link.springer.com/content/pdf/10.1007/s00778-019-00600-w.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Deutch:2020:ENL,
author = "Daniel Deutch and Nave Frost and Amir Gilad",
title = "Explaining Natural Language query results",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "485--508",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00584-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00584-7",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Huang:2020:VOD,
author = "Silu Huang and Liqi Xu and Jialin Liu and Aaron J.
Elmore and Aditya Parameswaran",
title = "{$ \varvec {{\sc Orpheus}} $DB}: bolt-on versioning
for relational databases (extended version)",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "509--538",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00594-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00594-5",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Orr:2020:EPA,
author = "Laurel Orr and Magdalena Balazinska and Dan Suciu",
title = "{EntropyDB}: a probabilistic approach to approximate
query processing",
journal = j-VLDB-J,
volume = "29",
number = "1",
pages = "539--567",
month = jan,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00582-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00582-9",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Amer-Yahia:2020:VSE,
author = "Sihem Amer-Yahia and Jian Pei",
title = "{VLDB SI 2018} editorial",
journal = j-VLDB-J,
volume = "29",
number = "2--3",
pages = "593--594",
month = may,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00599-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00599-0;
http://link.springer.com/content/pdf/10.1007/s00778-019-00599-0.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sahu:2020:ULG,
author = "Siddhartha Sahu and Amine Mhedhbi and Semih Salihoglu
and Jimmy Lin and M. Tamer {\"O}zsu",
title = "The ubiquity of large graphs and surprising challenges
of graph processing: extended survey",
journal = j-VLDB-J,
volume = "29",
number = "2--3",
pages = "595--618",
month = may,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00548-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00548-x",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Idris:2020:GDY,
author = "Muhammad Idris and Mart{\'{\i}}n Ugarte and Stijn
Vansummeren and Hannes Voigt and Wolfgang Lehner",
title = "General dynamic {Yannakakis}: conjunctive queries with
theta joins under updates",
journal = j-VLDB-J,
volume = "29",
number = "2--3",
pages = "619--653",
month = may,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00590-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00590-9",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bonifati:2020:ASL,
author = "Angela Bonifati and Wim Martens and Thomas Timm",
title = "An analytical study of large {SPARQL} query logs",
journal = j-VLDB-J,
volume = "29",
number = "2--3",
pages = "655--679",
month = may,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00558-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00558-9",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Asudeh:2020:SAS,
author = "Abolfazl Asudeh and Jees Augustine and Azade Nazi and
Saravanan Thirumuruganathan and Nan Zhang and Gautam
Das and Divesh Srivastava",
title = "Scalable algorithms for signal reconstruction by
leveraging similarity joins",
journal = j-VLDB-J,
volume = "29",
number = "2--3",
pages = "681--707",
month = may,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00562-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00562-z",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ratner:2020:SRT,
author = "Alexander Ratner and Stephen H. Bach and Henry
Ehrenberg and Jason Fries and Sen Wu and Christopher
R{\'e}",
title = "{Snorkel}: rapid training data creation with weak
supervision",
journal = j-VLDB-J,
volume = "29",
number = "2--3",
pages = "709--730",
month = may,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00552-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00552-1;
http://link.springer.com/content/pdf/10.1007/s00778-019-00552-1.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Breslow:2020:MFF,
author = "Alex D. Breslow and Nuwan S. Jayasena",
title = "{Morton} filters: fast, compressed sparse cuckoo
filters",
journal = j-VLDB-J,
volume = "29",
number = "2--3",
pages = "731--754",
month = may,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00561-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00561-0",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Salem:2020:SIB,
author = "Kenneth Salem",
title = "Special issue on best papers of {DaMoN 2018}",
journal = j-VLDB-J,
volume = "29",
number = "2--3",
pages = "755--755",
month = may,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00597-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00597-2;
http://link.springer.com/content/pdf/10.1007/s00778-019-00597-2.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lang:2020:MMY,
author = "Harald Lang and Linnea Passing and Andreas Kipf and
Peter Boncz and Thomas Neumann and Alfons Kemper",
title = "Make the most out of your {SIMD} investments: counter
control flow divergence in compiled query pipelines",
journal = j-VLDB-J,
volume = "29",
number = "2--3",
pages = "757--774",
month = may,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00547-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00547-y;
http://link.springer.com/content/pdf/10.1007/s00778-019-00547-y.pdf",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zarubin:2020:ECN,
author = "Mikhail Zarubin and Thomas Kissinger and Dirk Habich
and Thomas Willhalm and Wolfgang Lehner",
title = "Efficient compute node-local replication mechanisms
for {NVRAM}-centric data structures",
journal = j-VLDB-J,
volume = "29",
number = "2--3",
pages = "775--795",
month = may,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00549-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00549-w",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Pohl:2020:JHB,
author = "Constantin Pohl and Kai-Uwe Sattler and Goetz Graefe",
title = "Joins on high-bandwidth memory: a new level in the
memory hierarchy",
journal = j-VLDB-J,
volume = "29",
number = "2--3",
pages = "797--817",
month = may,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00546-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:22 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00546-z",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Pedersen:2020:FSR,
author = "Simon Aagaard Pedersen and Bin Yang and Christian S.
Jensen",
title = "Fast stochastic routing under time-varying
uncertainty",
journal = j-VLDB-J,
volume = "29",
number = "4",
pages = "819--839",
month = jul,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00585-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:39 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-019-00585-6",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 31 October 2019 Pages: 819 - 839",
}
@Article{Xu:2020:EPM,
author = "Hongfei Xu and Yu Gu and Rui Zhang",
title = "Efficient processing of moving collective spatial
keyword queries",
journal = j-VLDB-J,
volume = "29",
number = "4",
pages = "841--865",
month = jul,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00583-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:39 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-019-00583-8",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 01 November 2019 Pages: 841 - 865",
}
@Article{Geerts:2020:CDL,
author = "Floris Geerts and Giansalvatore Mecca and Donatello
Santoro",
title = "Cleaning data with {Llunatic}",
journal = j-VLDB-J,
volume = "29",
number = "4",
pages = "867--892",
month = jul,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00586-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:39 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-019-00586-5",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 08 November 2019 Pages: 867 - 892",
}
@Article{Wu:2020:TRS,
author = "Dingming Wu and Hao Zhou and Nikos Mamoulis",
title = "Top-$k$ relevant semantic place retrieval on
spatiotemporal {RDF} data",
journal = j-VLDB-J,
volume = "29",
number = "4",
pages = "893--917",
month = jul,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00591-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:39 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-019-00591-8",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 19 November 2019 Pages: 893 - 917",
}
@Article{Qin:2020:EQA,
author = "Jianbin Qin and Chuan Xiao and Kunihiko Sadakane",
title = "Efficient query autocompletion with edit
distance-based error tolerance",
journal = j-VLDB-J,
volume = "29",
number = "4",
pages = "919--943",
month = jul,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00595-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:39 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-019-00595-4",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 14 December 2019 Pages: 919 - 943",
}
@Article{Jiang:2020:SCS,
author = "Jiawei Jiang and Fangcheng Fu and Bin Cui",
title = "{SKCompress}: compressing sparse and nonuniform
gradient in distributed machine learning",
journal = j-VLDB-J,
volume = "29",
number = "5",
pages = "945--972",
month = sep,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00596-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-019-00596-3",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 01 January 2020 Pages: 945 - 972",
}
@Article{Wang:2020:FEF,
author = "Chaohui Wang and Miao Xie and Shuigeng Zhou",
title = "{FERRARI}: an efficient framework for visual
exploratory subgraph search in graph databases",
journal = j-VLDB-J,
volume = "29",
number = "5",
pages = "973--998",
month = sep,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00601-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00601-0",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 30 January 2020 Pages: 973 - 998",
}
@Article{Chang:2020:EMC,
author = "Lijun Chang",
title = "Efficient maximum clique computation and enumeration
over large sparse graphs",
journal = j-VLDB-J,
volume = "29",
number = "5",
pages = "999--1022",
month = sep,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00602-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00602-z",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 15 February 2020 Pages: 999 - 1022",
}
@Article{Chondrogiannis:2020:FSP,
author = "Theodoros Chondrogiannis and Panagiotis Bouros and
David B. Blumenthal",
title = "Finding $k$-shortest paths with limited overlap",
journal = j-VLDB-J,
volume = "29",
number = "5",
pages = "1023--1047",
month = sep,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00604-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00604-x",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 21 February 2020 Pages: 1023 - 1047",
}
@Article{Zou:2020:ADS,
author = "Jia Zou and Arun Iyengar and Chris Jermaine",
title = "Architecture of a distributed storage that combines
file system, memory and computation in a single layer",
journal = j-VLDB-J,
volume = "29",
number = "5",
pages = "1049--1073",
month = sep,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00605-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00605-w",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 26 February 2020 Pages: 1049 - 1073",
}
@Article{Liu:2020:ECC,
author = "Boge Liu and Long Yuan and Jingren Zhou",
title = "Efficient $ (\alpha, \beta)$-core computation in
bipartite graphs",
journal = j-VLDB-J,
volume = "29",
number = "5",
pages = "1075--1099",
month = sep,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00606-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00606-9",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 04 March 2020 Pages: 1075 - 1099",
}
@Article{Chen:2020:TTP,
author = "Lisi Chen and Shuo Shang and Ling Shao",
title = "Top-$k$ term publish/subscribe for geo-textual data
streams",
journal = j-VLDB-J,
volume = "29",
number = "5",
pages = "1101--1128",
month = sep,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00607-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00607-8",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 09 March 2020 Pages: 1101 - 1128",
}
@Article{Yang:2020:TFA,
author = "Fan Yang and Faisal M. Almutairi and Vladimir
Zadorozhny",
title = "{TurboLift}: fast accuracy lifting for historical data
recovery",
journal = j-VLDB-J,
volume = "29",
number = "5",
pages = "1129--1148",
month = sep,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00609-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See correction \cite{Yang:2024:CTF}.",
URL = "https://link.springer.com/article/10.1007/s00778-020-00609-6",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 09 March 2020 Pages: 1129 - 1148",
}
@Article{Guo:2020:CAP,
author = "Chenjuan Guo and Bin Yang and Lu Chen",
title = "Context-aware, preference-based vehicle routing",
journal = j-VLDB-J,
volume = "29",
number = "5",
pages = "1149--1170",
month = sep,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00608-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00608-7",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 11 March 2020 Pages: 1149 - 1170",
}
@Article{Cai:2020:DSK,
author = "Zhi Cai and Georgios Kalamatianos and Dimitris
Papadias",
title = "Diversified spatial keyword search on {RDF} data",
journal = j-VLDB-J,
volume = "29",
number = "5",
pages = "1171--1189",
month = sep,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00610-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00610-z",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 12 March 2020 Pages: 1171 - 1189",
}
@Article{Goasdoue:2020:RGS,
author = "Fran{\c{c}}ois Goasdou{\'e} and Pawe{\l} Guzewicz and
Ioana Manolescu",
title = "{RDF} graph summarization for first-sight structure
discovery",
journal = j-VLDB-J,
volume = "29",
number = "5",
pages = "1191--1218",
month = sep,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00611-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00611-y",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 30 April 2020 Pages: 1191 - 1218",
}
@Article{Fang:2020:CSC,
author = "Yixiang Fang and Xin Huang and Xuemin Lin",
title = "Correction: {A survey of community search over big
graphs}",
journal = j-VLDB-J,
volume = "29",
number = "5",
pages = "1219--1219",
month = sep,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00592-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See \cite{Fang:2020:SCS}.",
URL = "https://link.springer.com/article/10.1007/s00778-019-00592-7",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 11 November 2019 Pages: 1219 - 1219",
}
@Article{Aboulnaga:2020:SIB,
author = "Ashraf Aboulnaga",
title = "Special issue on the best papers of {DaMoN 2019}",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1221--1221",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00629-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00629-2",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 05 September 2020 Pages: 1221 - 1221",
}
@Article{vanRenen:2020:BBP,
author = "Alexander van Renen and Lukas Vogel and Alfons
Kemper",
title = "Building blocks for persistent memory",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1223--1241",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00622-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00622-9",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 23 September 2020 Pages: 1223 - 1241",
}
@Article{Polychroniou:2020:VSV,
author = "Orestis Polychroniou and Kenneth A. Ross",
title = "{VIP}: A {SIMD} vectorized analytical query engine",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1243--1261",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00621-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00621-w",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 13 July 2020 Pages: 1243 - 1261",
}
@Article{Lasch:2020:FSS,
author = "Robert Lasch and Ismail Oukid and Kai-Uwe Sattler",
title = "Faster \& strong: string dictionary compression using
sampling and fast vectorized decompression",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1263--1285",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00620-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00620-x",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 20 July 2020 Pages: 1263 - 1285",
}
@Article{Kruse:2020:RDJ,
author = "Sebastian Kruse and Zoi Kaoudi and Jorge-Arnulfo
Quian{\'e}-Ruiz",
title = "{RHEEMix} in the data jungle: a cost-based optimizer
for cross-platform systems",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1287--1310",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00612-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00612-x",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 18 May 2020 Pages: 1287 - 1310",
}
@Article{Yang:2020:GBF,
author = "Jingru Yang and Ju Fan and Xiaoyong Du",
title = "A game-based framework for crowdsourced data
labeling",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1311--1336",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00613-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00613-w",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 19 May 2020 Pages: 1311 - 1336",
}
@Article{Jacobs:2020:BBB,
author = "Steven Jacobs and Xikui Wang and Md Yusuf Sarwar
Uddin",
title = "{BAD} to the bone: {Big Active Data} at its core",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1337--1364",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00616-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00616-7",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 23 May 2020 Pages: 1337 - 1364",
}
@Article{Sun:2020:TSI,
author = "Tao Sun and Hongbo Liu and Xindong Wu",
title = "Time series indexing by dynamic covering with
cross-range constraints",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1365--1384",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00614-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00614-9",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 28 May 2020 Pages: 1365 - 1384",
}
@Article{Huang:2020:EAA,
author = "Keke Huang and Jing Tang and Andrew Lim",
title = "Efficient approximation algorithms for adaptive
influence maximization",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1385--1406",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00615-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00615-8",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 01 June 2020 Pages: 1385 - 1406",
}
@Article{Li:2020:FSC,
author = "Rong-Hua Li and Lu Qin and Zibin Zheng",
title = "Finding skyline communities in multi-valued networks",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1407--1432",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00618-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00618-5",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 08 June 2020 Pages: 1407 - 1432",
}
@Article{Ahmad:2020:AWM,
author = "Hiba Abu Ahmad and Hongzhi Wang",
title = "Automatic weighted matching rectifying rule discovery
for data repairing",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1433--1447",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00617-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00617-6",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 09 June 2020 Pages: 1433 - 1447",
}
@Article{Linardi:2020:SDS,
author = "Michele Linardi and Themis Palpanas",
title = "Scalable data series subsequence matching with
{ULISSE}",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1449--1474",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00619-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00619-4",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 04 July 2020 Pages: 1449 - 1474",
}
@Article{Song:2020:IPA,
author = "Liangjun Song and Junhao Gan and Timos Sellis",
title = "Incremental preference adjustment: a graph-theoretical
approach",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1475--1500",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00623-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00623-8",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 03 August 2020 Pages: 1475 - 1500",
}
@Article{Lee:2020:TLA,
author = "Dongjin Lee and Kijung Shin and Christos Faloutsos",
title = "Temporal locality-aware sampling for accurate triangle
counting in real graph streams",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1501--1525",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00624-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00624-7",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 12 August 2020 Pages: 1501 - 1525",
}
@Article{Omidvar-Tehrani:2020:CAE,
author = "Behrooz Omidvar-Tehrani and Sihem Amer-Yahia and Laks
V. S. Lakshmanan",
title = "Cohort analytics: efficiency and applicability",
journal = j-VLDB-J,
volume = "29",
number = "6",
pages = "1527--1550",
month = nov,
year = "2020",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00625-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:40 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00625-6",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 27 August 2020 Pages: 1527 - 1550",
}
@Article{Ozcan:2021:GES,
author = "Fatma {\"O}zcan and Lei Chen",
title = "Guest Editorial: Special issue on {VLDB 2019}",
journal = j-VLDB-J,
volume = "30",
number = "1",
pages = "1--2",
month = jan,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00630-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00630-9",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 11 September 2020 Pages: 1 - 2",
}
@Article{Ruan:2021:LFG,
author = "Pingcheng Ruan and Tien Tuan Anh Dinh and Beng Chin
Ooi",
title = "{LineageChain}: a fine-grained, secure and efficient
data provenance system for blockchains",
journal = j-VLDB-J,
volume = "30",
number = "1",
pages = "3--24",
month = jan,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00646-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/bitcoin.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00646-1",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 01 January 2021 Pages: 3 - 24",
}
@Article{Wu:2021:ATC,
author = "Chenggang Wu and Vikram Sreekanti and Joseph M.
Hellerstein",
title = "Autoscaling tiered cloud storage in {Anna}",
journal = j-VLDB-J,
volume = "30",
number = "1",
pages = "25--43",
month = jan,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00632-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00632-7",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 09 September 2020 Pages: 25 - 43",
}
@Article{Abuzaid:2021:DRI,
author = "Firas Abuzaid and Peter Kraft and Matei Zaharia",
title = "{DIFF}: a relational interface for large-scale data
explanation",
journal = j-VLDB-J,
volume = "30",
number = "1",
pages = "45--70",
month = jan,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00633-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00633-6",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 30 September 2020 Pages: 45 - 70",
}
@Article{Whittaker:2021:ICC,
author = "Michael Whittaker and Joseph M. Hellerstein",
title = "Interactive checks for coordination avoidance",
journal = j-VLDB-J,
volume = "30",
number = "1",
pages = "71--92",
month = jan,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00628-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00628-3",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 05 September 2020 Pages: 71 - 92",
}
@Article{Fan:2021:GBV,
author = "Hua Fan and Wojciech Golab",
title = "Gossip-based visibility control for high-performance
geo-distributed transactions",
journal = j-VLDB-J,
volume = "30",
number = "1",
pages = "93--114",
month = jan,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00626-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00626-5",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 21 September 2020 Pages: 93 - 114",
}
@Article{Li:2021:QSD,
author = "Yuliang Li and Aaron Feng and Wang-Chiew Tan",
title = "Querying subjective data",
journal = j-VLDB-J,
volume = "30",
number = "1",
pages = "115--140",
month = jan,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00634-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00634-5",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 08 September 2020 Pages: 115 - 140",
}
@Article{Dong:2021:CTS,
author = "Yuyang Dong and Chuan Xiao and Hiroyuki Kitagawa",
title = "Continuous top-$k$ spatial-keyword search on dynamic
objects",
journal = j-VLDB-J,
volume = "30",
number = "2",
pages = "141--161",
month = mar,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00627-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00627-4",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 05 September 2020 Pages: 141 - 161",
}
@Article{Zhang:2021:TTA,
author = "Feng Zhang and Jidong Zhai and Xiaoyong Du",
title = "{TADOC}: Text analytics directly on compression",
journal = j-VLDB-J,
volume = "30",
number = "2",
pages = "163--188",
month = mar,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00636-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00636-3",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 19 September 2020 Pages: 163 - 188",
}
@Article{Li:2021:CTQ,
author = "Yan Li and Hao Wang and Zhiguo Gong",
title = "Crowdsourced top-$k$ queries by pairwise preference
judgments with confidence and budget control",
journal = j-VLDB-J,
volume = "30",
number = "2",
pages = "189--213",
month = mar,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00631-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00631-8",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 21 September 2020 Pages: 189 - 213",
}
@Article{Liu:2021:LET,
author = "Wanqi Liu and Hanchen Wang and Xuemin Lin",
title = "{EI-LSH}: An early-termination driven {I/O} efficient
incremental c -approximate nearest neighbor search",
journal = j-VLDB-J,
volume = "30",
number = "2",
pages = "215--235",
month = mar,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00635-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00635-4",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 30 September 2020 Pages: 215 - 235",
}
@Article{Yu:2021:GCC,
author = "Jia Yu and Mohamed Sarwat",
title = "{GeoSparkViz}: a cluster computing system for
visualizing massive-scale geospatial data",
journal = j-VLDB-J,
volume = "30",
number = "2",
pages = "237--258",
month = mar,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00645-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00645-2",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 07 January 2021 Pages: 237 - 258",
}
@Article{Zhang:2021:SAN,
author = "Yongqi Zhang and Quanming Yao and Lei Chen",
title = "Simple and automated negative sampling for knowledge
graph embedding",
journal = j-VLDB-J,
volume = "30",
number = "2",
pages = "259--285",
month = mar,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00640-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00640-7",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 28 January 2021 Pages: 259 - 285",
}
@Article{Fang:2021:DHE,
author = "Ziquan Fang and Lu Chen and Christian S. Jensen",
title = "{Dragoon}: a hybrid and efficient big trajectory
management system for offline and online analytics",
journal = j-VLDB-J,
volume = "30",
number = "2",
pages = "287--310",
month = mar,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00652-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu May 13 17:41:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00652-x",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
online-date = "Published: 03 February 2021 Pages: 287 - 310",
}
@Article{Paul:2021:SER,
author = "Debjyoti Paul and Feifei Li and Jeff M. Phillips",
title = "Semantic embedding for regions of interest",
journal = j-VLDB-J,
volume = "30",
number = "3",
pages = "311--331",
month = may,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00647-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 9 10:33:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00647-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Romanous:2021:ELL,
author = "Bashar Romanous and Skyler Windh and Vassilis
Tsotras",
title = "Efficient local locking for massively multithreaded
in-memory hash-based operators",
journal = j-VLDB-J,
volume = "30",
number = "3",
pages = "333--359",
month = may,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00642-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 9 10:33:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/hash.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00642-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mao:2021:CES,
author = "Qizhong Mao and Steven Jacobs and Neal E. Young",
title = "Comparison and evaluation of state-of-the-art {LSM}
merge policies",
journal = j-VLDB-J,
volume = "30",
number = "3",
pages = "361--378",
month = may,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00638-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 9 10:33:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00638-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Piatov:2021:CES,
author = "Danila Piatov and Sven Helmer and Fabio Persia",
title = "Cache-efficient sweeping-based interval joins for
extended {Allen} relation predicates",
journal = j-VLDB-J,
volume = "30",
number = "3",
pages = "379--402",
month = may,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00650-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 9 10:33:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00650-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Do:2021:BDC,
author = "Jaeyoung Do and Ivan Luiz Picoli and Philippe Bonnet",
title = "Better database cost\slash performance via batched
{I/O} on programmable {SSD}",
journal = j-VLDB-J,
volume = "30",
number = "3",
pages = "403--424",
month = may,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00648-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 9 10:33:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00648-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Song:2021:CTT,
author = "Shaoxu Song and Ruihong Huang and Jianmin Wang",
title = "Cleaning timestamps with temporal constraints",
journal = j-VLDB-J,
volume = "30",
number = "3",
pages = "425--446",
month = may,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00641-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 9 10:33:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00641-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yang:2021:IEM,
author = "Chengcheng Yang and Dong Deng and Ling Shao",
title = "Internal and external memory set containment join",
journal = j-VLDB-J,
volume = "30",
number = "3",
pages = "447--470",
month = may,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00644-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 9 10:33:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See correction \cite{Yang:2021:CIE}.",
URL = "https://link.springer.com/article/10.1007/s00778-020-00644-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2021:ESN,
author = "Xiaoshuang Chen and Longbin Lai and Xuemin Lin",
title = "Efficient structural node similarity computation on
billion-scale graphs",
journal = j-VLDB-J,
volume = "30",
number = "3",
pages = "471--493",
month = may,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00654-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 9 10:33:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00654-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yu:2021:VAR,
author = "Wenhui Yu and Xiangnan He and Zheng Qin",
title = "Visually aware recommendation with aesthetic
features",
journal = j-VLDB-J,
volume = "30",
number = "4",
pages = "495--513",
month = jul,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00651-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00651-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hao:2021:MCE,
author = "Shuang Hao and Nan Tang and Ning Wang",
title = "Mis-categorized entities detection",
journal = j-VLDB-J,
volume = "30",
number = "4",
pages = "515--536",
month = jul,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00653-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00653-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Galhotra:2021:EEP,
author = "Sainyam Galhotra and Donatella Firmani and Divesh
Srivastava",
title = "Efficient and effective {ER} with progressive
blocking",
journal = j-VLDB-J,
volume = "30",
number = "4",
pages = "537--557",
month = jul,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00656-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00656-7",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hewasinghage:2021:CMR,
author = "Moditha Hewasinghage and Alberto Abell{\'o} and
Esteban Zim{\'a}nyi",
title = "A cost model for random access queries in document
stores",
journal = j-VLDB-J,
volume = "30",
number = "4",
pages = "559--578",
month = jul,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00660-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00660-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Schneider:2021:DDS,
author = "Johannes Schneider and Phillip Wenig and Thorsten
Papenbrock",
title = "Distributed detection of sequential anomalies in
univariate time series",
journal = j-VLDB-J,
volume = "30",
number = "4",
pages = "579--602",
month = jul,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00657-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00657-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2021:LKB,
author = "Zhida Chen and Lisi Chen and Christian S. Jensen",
title = "Location- and keyword-based querying of geo-textual
data: a survey",
journal = j-VLDB-J,
volume = "30",
number = "4",
pages = "603--640",
month = jul,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00661-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00661-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sirin:2021:MAA,
author = "Utku Sirin and Pinar T{\"o}z{\"u}n and Anastasia
Ailamaki",
title = "Micro-architectural analysis of in-memory {OLTP}:
Revisited",
journal = j-VLDB-J,
volume = "30",
number = "4",
pages = "641--665",
month = jul,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00663-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00663-8",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bouros:2021:MIJ,
author = "Panagiotis Bouros and Nikos Mamoulis and Manolis
Terrovitis",
title = "In-Memory Interval Joins",
journal = j-VLDB-J,
volume = "30",
number = "4",
pages = "667--691",
month = jul,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00639-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00639-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Guo:2021:MAD,
author = "Yunyan Guo and Zhipeng Zhang and Jianzhong Li",
title = "Model averaging in distributed machine learning: a
case study with {Apache Spark}",
journal = j-VLDB-J,
volume = "30",
number = "4",
pages = "693--712",
month = jul,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00664-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00664-7",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Jiang:2021:EEK,
author = "Yuli Jiang and Xin Huang and Hong Cheng",
title = "{I/O} efficient $k$-truss community search in massive
graphs",
journal = j-VLDB-J,
volume = "30",
number = "5",
pages = "713--738",
month = sep,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00649-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00649-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Balayn:2021:MBU,
author = "Agathe Balayn and Christoph Lofi and Geert-Jan
Houben",
title = "Managing bias and unfairness in data for decision
support: a survey of machine learning and data
engineering approaches to identify and mitigate bias
and unfairness within data management and analytics
systems",
journal = j-VLDB-J,
volume = "30",
number = "5",
pages = "739--768",
month = sep,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00671-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00671-8",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Shao:2021:MAF,
author = "Yingxia Shao and Shiyue Huang and Lei Chen",
title = "Memory-aware framework for fast and scalable
second-order random walk over billion-edge natural
graphs",
journal = j-VLDB-J,
volume = "30",
number = "5",
pages = "769--797",
month = sep,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00669-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00669-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Peng:2021:EHC,
author = "You Peng and Xuemin Lin and Jingren Zhou",
title = "Efficient Hop-constrained $s$--$t$ Simple Path
Enumeration",
journal = j-VLDB-J,
volume = "30",
number = "5",
pages = "799--823",
month = sep,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00674-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00674-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Debrouvier:2021:MQL,
author = "Ariel Debrouvier and Eliseo Parodi and Alejandro
Vaisman",
title = "A model and query language for temporal graph
databases",
journal = j-VLDB-J,
volume = "30",
number = "5",
pages = "825--858",
month = sep,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00675-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00675-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2021:FSH,
author = "Jin Wang and Jiacheng Wu and Carlo Zaniolo",
title = "Formal semantics and high performance in declarative
machine learning using {Datalog}",
journal = j-VLDB-J,
volume = "30",
number = "5",
pages = "859--881",
month = sep,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00665-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00665-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kersten:2021:TTF,
author = "Timo Kersten and Viktor Leis and Thomas Neumann",
title = "Tidy Tuples and Flying Start: fast compilation and
fast execution of relational queries in {Umbra}",
journal = j-VLDB-J,
volume = "30",
number = "5",
pages = "883--905",
month = sep,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-020-00643-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-020-00643-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yang:2021:CIE,
author = "Chengcheng Yang and Dong Deng and Ling Shao",
title = "Correction to: {Internal} and external memory set
containment join",
journal = j-VLDB-J,
volume = "30",
number = "5",
pages = "907--907",
month = sep,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00662-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See \cite{Yang:2021:IEM}.",
URL = "https://link.springer.com/article/10.1007/s00778-021-00662-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Boniol:2021:USS,
author = "Paul Boniol and Michele Linardi and Emmanuel Remy",
title = "Unsupervised and scalable subsequence anomaly
detection in large data series",
journal = j-VLDB-J,
volume = "30",
number = "6",
pages = "909--931",
month = nov,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00655-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See correction \cite{Boniol:2023:CUS}.",
URL = "https://link.springer.com/article/10.1007/s00778-021-00655-8",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Tangwongsan:2021:OSW,
author = "Kanat Tangwongsan and Martin Hirzel and Scott
Schneider",
title = "In-order sliding-window aggregation in worst-case
constant time",
journal = j-VLDB-J,
volume = "30",
number = "6",
pages = "933--957",
month = nov,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00668-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00668-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2021:HCE,
author = "Ji Zhang and Ke Zhou and Jiashu Xing",
title = "{CDBTune}$^+$: an efficient deep reinforcement
learning-based automatic cloud database tuning system",
journal = j-VLDB-J,
volume = "30",
number = "6",
pages = "959--987",
month = nov,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00670-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00670-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2021:EBS,
author = "Hanzhi Wang and Zhewei Wei and Ji-Rong Wen",
title = "{ExactSim}: benchmarking single-source {SimRank}
algorithms with high-precision ground truths",
journal = j-VLDB-J,
volume = "30",
number = "6",
pages = "989--1015",
month = nov,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00672-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00672-7",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Forresi:2021:DBF,
author = "Chiara Forresi and Enrico Gallinucci and Hamdi {Ben
Hamadou}",
title = "A dataspace-based framework for {OLAP} analyses in a
high-variety multistore",
journal = j-VLDB-J,
volume = "30",
number = "6",
pages = "1017--1040",
month = nov,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00682-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00682-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Peng:2021:FDS,
author = "Botao Peng and Panagiota Fatourou and Themis
Palpanas",
title = "Fast data series indexing for in-memory data",
journal = j-VLDB-J,
volume = "30",
number = "6",
pages = "1041--1067",
month = nov,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00677-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00677-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wei:2021:ADE,
author = "Ziheng Wei and Sven Hartmann and Sebastian Link",
title = "Algorithms for the discovery of embedded functional
dependencies",
journal = j-VLDB-J,
volume = "30",
number = "6",
pages = "1069--1093",
month = nov,
year = "2021",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00684-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Apr 14 14:19:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00684-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kossmann:2022:DDQ,
author = "Jan Kossmann and Thorsten Papenbrock and Felix
Naumann",
title = "Data dependencies for query optimization: a survey",
journal = j-VLDB-J,
volume = "31",
number = "1",
pages = "1--22",
month = jan,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00676-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 15 07:02:55 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See correction \cite{Kossmann:2023:CDD}.",
URL = "https://link.springer.com/article/10.1007/s00778-021-00676-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhu:2022:PSA,
author = "Yifan Zhu and Lu Chen and Christian S. Jensen",
title = "Pivot selection algorithms in metric spaces: a survey
and experimental study",
journal = j-VLDB-J,
volume = "31",
number = "1",
pages = "23--47",
month = jan,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00691-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 15 07:02:55 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00691-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Schmidl:2022:EDD,
author = "Sebastian Schmidl and Thorsten Papenbrock",
title = "Efficient distributed discovery of bidirectional order
dependencies",
journal = j-VLDB-J,
volume = "31",
number = "1",
pages = "49--74",
month = jan,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00683-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 15 07:02:55 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00683-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Dignos:2022:LRJ,
author = "Anton Dign{\"o}s and Michael H. B{\"o}hlen and Peter
Moser",
title = "Leveraging range joins for the computation of overlap
joins",
journal = j-VLDB-J,
volume = "31",
number = "1",
pages = "75--99",
month = jan,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00692-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 15 07:02:55 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00692-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Peng:2022:ARE,
author = "You Peng and Xuemin Lin and Lu Qin",
title = "Answering reachability and {$K$}-reach queries on
large graphs with label constraints",
journal = j-VLDB-J,
volume = "31",
number = "1",
pages = "101--127",
month = jan,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00695-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 15 07:02:55 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00695-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2022:DLP,
author = "Wentao Li and Miao Qiao and Xuemin Lin",
title = "Distance labeling: on parallelism, compression, and
ordering",
journal = j-VLDB-J,
volume = "31",
number = "1",
pages = "129--155",
month = jan,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00694-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 15 07:02:55 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00694-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Alevizos:2022:CEF,
author = "Elias Alevizos and Alexander Artikis and Georgios
Paliouras",
title = "Complex event forecasting with prediction suffix
trees",
journal = j-VLDB-J,
volume = "31",
number = "1",
pages = "157--180",
month = jan,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00698-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 15 07:02:55 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00698-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Snodgrass:2022:QOH,
author = "Richard T. Snodgrass and Sabah Currim and Young-Kyoon
Suh",
title = "Have query optimizers hit the wall?",
journal = j-VLDB-J,
volume = "31",
number = "1",
pages = "181--200",
month = jan,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00689-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 15 07:02:55 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00689-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bonifati:2022:SIB,
author = "Angela Bonifati and Hannes Voigt",
title = "Special issue on big graph data management and
processing",
journal = j-VLDB-J,
volume = "31",
number = "2",
pages = "201--202",
month = mar,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00732-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 16 07:47:28 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00732-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2022:TES,
author = "Kai Wang and Xuemin Lin and Ying Zhang",
title = "Towards efficient solutions of bitruss decomposition
for large-scale bipartite graphs",
journal = j-VLDB-J,
volume = "31",
number = "2",
pages = "203--226",
month = mar,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00658-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 16 07:47:28 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00658-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Linghu:2022:ACE,
author = "Qingyuan Linghu and Fan Zhang and Ying Zhang",
title = "Anchored coreness: efficient reinforcement of social
networks",
journal = j-VLDB-J,
volume = "31",
number = "2",
pages = "227--252",
month = mar,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00673-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 16 07:47:28 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00673-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yan:2022:PPF,
author = "Da Yan and Wenwen Qu and Yang Zhou",
title = "{PrefixFPM}: a parallel framework for general-purpose
mining of frequent and closed patterns",
journal = j-VLDB-J,
volume = "31",
number = "2",
pages = "253--286",
month = mar,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00687-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 16 07:47:28 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00687-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yan:2022:GTG,
author = "Da Yan and Guimu Guo and John C. S. Lui",
title = "{G-thinker}: a general distributed framework for
finding qualified subgraphs in a big graph with load
balancing",
journal = j-VLDB-J,
volume = "31",
number = "2",
pages = "287--320",
month = mar,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00688-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 16 07:47:28 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00688-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mohamed:2022:RKG,
author = "Aisha Mohamed and Ghadeer Abuoda and Ashraf
Aboulnaga",
title = "{RDFFrames}: knowledge graph access for machine
learning tools",
journal = j-VLDB-J,
volume = "31",
number = "2",
pages = "321--346",
month = mar,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00690-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 16 07:47:28 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00690-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sagi:2022:DSR,
author = "Tomer Sagi and Matteo Lissandrini and Katja Hose",
title = "A design space for {RDF} data representations",
journal = j-VLDB-J,
volume = "31",
number = "2",
pages = "347--373",
month = mar,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00725-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 16 07:47:28 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00725-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Rost:2022:DTG,
author = "Christopher Rost and Kevin Gomez and Erhard Rahm",
title = "Distributed temporal graph analytics with {GRADOOP}",
journal = j-VLDB-J,
volume = "31",
number = "2",
pages = "375--401",
month = mar,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00667-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 16 07:47:28 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00667-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bevilacqua:2022:FME,
author = "Glenn S. Bevilacqua and Laks V. S. Lakshmanan",
title = "A fractional memory-efficient approach for online
continuous-time influence maximization",
journal = j-VLDB-J,
volume = "31",
number = "2",
pages = "403--429",
month = mar,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00679-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Apr 16 07:47:28 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00679-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ali:2022:SRS,
author = "Waqas Ali and Muhammad Saleem and Axel-Cyrille Ngonga
Ngomo",
title = "A survey of {RDF} stores \& {SPARQL} engines for
querying knowledge graphs",
journal = j-VLDB-J,
volume = "31",
number = "3",
pages = "1--26",
month = may,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00711-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 6 07:32:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00711-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Pitoura:2022:FRR,
author = "Evaggelia Pitoura and Kostas Stefanidis and Georgia
Koutrika",
title = "Fairness in rankings and recommendations: an
overview",
journal = j-VLDB-J,
volume = "31",
number = "3",
pages = "431--458",
month = may,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00697-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 6 07:32:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00697-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hidayat:2022:CMM,
author = "Arif Hidayat and Muhammad Aamir Cheema and Ying
Zhang",
title = "Continuous monitoring of moving skyline and top-$k$
queries",
journal = j-VLDB-J,
volume = "31",
number = "3",
pages = "459--482",
month = may,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00702-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 6 07:32:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00702-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Farhan:2022:FFD,
author = "Muhammad Farhan and Qing Wang and Brendan McKay",
title = "Fast fully dynamic labelling for distance queries",
journal = j-VLDB-J,
volume = "31",
number = "3",
pages = "483--506",
month = may,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00707-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 6 07:32:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00707-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhao:2022:RCS,
author = "Tianyu Zhao and Shuai Huang and Guoliang Li",
title = "{RNE}: computing shortest paths using road network
embedding",
journal = j-VLDB-J,
volume = "31",
number = "3",
pages = "507--528",
month = may,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00705-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 6 07:32:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00705-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lai:2022:AMW,
author = "Zhuohang Lai and Xibo Sun and Xiaolong Xie",
title = "Accelerating multi-way joins on the {GPU}",
journal = j-VLDB-J,
volume = "31",
number = "3",
pages = "529--553",
month = may,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00708-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 6 07:32:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00708-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{diVimercati:2022:AMQ,
author = "Sabrina {De Capitani di Vimercati} and Sara Foresti
and Pierangela Samarati",
title = "An authorization model for query execution in the
cloud",
journal = j-VLDB-J,
volume = "31",
number = "3",
pages = "555--579",
month = may,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00709-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 6 07:32:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00709-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Huang:2022:PEG,
author = "Kai Huang and Haibo Hu and Xiaofang Zhou",
title = "Privacy and efficiency guaranteed social subgraph
matching",
journal = j-VLDB-J,
volume = "31",
number = "3",
pages = "581--602",
month = may,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00706-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri May 6 07:32:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00706-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wen:2022:SRQ,
author = "Dong Wen and Bohua Yang and Wenjie Zhang",
title = "Span-reachability querying in large temporal graphs",
journal = j-VLDB-J,
volume = "31",
number = "4",
pages = "629--647",
month = jul,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00715-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 25 16:46:59 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00715-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Khalil:2022:PML,
author = "Jalal Khalil and Da Yan and Lyuheng Yuan",
title = "Parallel mining of large maximal quasi-cliques",
journal = j-VLDB-J,
volume = "31",
number = "4",
pages = "649--674",
month = jul,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00712-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 25 16:46:59 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00712-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kellou-Menouer:2022:SSS,
author = "Kenza Kellou-Menouer and Nikolaos Kardoulakis and
Haridimos Kondylakis",
title = "A survey on semantic schema discovery",
journal = j-VLDB-J,
volume = "31",
number = "4",
pages = "675--710",
month = jul,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00717-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 25 16:46:59 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00717-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fritz:2022:EEC,
author = "Manuel Fritz and Michael Behringer and Holger
Schwarz",
title = "Efficient exploratory clustering analyses in
large-scale exploration processes",
journal = j-VLDB-J,
volume = "31",
number = "4",
pages = "711--732",
month = jul,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00716-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 25 16:46:59 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00716-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zheng:2022:PPW,
author = "Libin Zheng and Lei Chen and Peng Cheng",
title = "Privacy-preserving worker allocation in
crowdsourcing",
journal = j-VLDB-J,
volume = "31",
number = "4",
pages = "733--751",
month = jul,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00713-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 25 16:46:59 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00713-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Qin:2022:IDR,
author = "Xuedi Qin and Chengliang Chai and Mourad Ouzzani",
title = "Interactively discovering and ranking desired tuples
by data exploration",
journal = j-VLDB-J,
volume = "31",
number = "4",
pages = "753--777",
month = jul,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00714-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 25 16:46:59 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00714-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhu:2022:OPP,
author = "Yuqing Zhu and Jing Tang and Xueyan Tang",
title = "Optimal price profile for influential nodes in online
social networks",
journal = j-VLDB-J,
volume = "31",
number = "4",
pages = "779--795",
month = jul,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00727-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 25 16:46:59 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00727-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Amagata:2022:FEP,
author = "Daichi Amagata and Makoto Onizuka and Takahiro Hara",
title = "Fast, exact, and parallel-friendly outlier detection
algorithms with proximity graph in metric spaces",
journal = j-VLDB-J,
volume = "31",
number = "4",
pages = "797--821",
month = jul,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00729-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jun 25 16:46:59 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00729-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Huang:2022:SIR,
author = "Zi Huang and Yanyan Shen and Divesh Srivastava",
title = "Special issue on responsible data management and data
science",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "823--823",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00761-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00761-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2022:AOD,
author = "Pei Li and Jaroslaw Szlichta and Divesh Srivastava",
title = "{ABC} of order dependencies",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "825--849",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00696-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00696-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Principe:2022:AHS,
author = "Renzo Arturo Alva Principe and Andrea Maurino and
Blerina Spahiu",
title = "{ABSTAT-HD}: a scalable tool for profiling very large
knowledge graphs",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "851--876",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00704-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00704-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2022:FAP,
author = "Qinyong Wang and Hongzhi Yin and Xiangliang Zhang",
title = "Fast-adapting and privacy-preserving federated
recommender system",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "877--896",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00700-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00700-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xiang:2022:GGG,
author = "Sheng Xiang and Dong Wen and Xuemin Lin",
title = "General graph generators: experiments, analyses, and
improvements",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "897--925",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00701-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00701-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Liu:2022:PGA,
author = "Zifan Liu and Zhechun Zhou and Theodoros Rekatsinas",
title = "Picket: guarding against corrupted data in tabular
data during learning and inference",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "927--955",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00699-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00699-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ge:2022:MMD,
author = "Yong-Feng Ge and Maria Orlowska and Yanchun Zhang",
title = "{MDDE}: multitasking distributed differential
evolution for privacy-preserving database
fragmentation",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "957--975",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00718-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00718-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Panjei:2022:SOE,
author = "Egawati Panjei and Le Gruenwald and Shejuti Silvia",
title = "A survey on outlier explanations",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "977--1008",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00721-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00721-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zeng:2022:EAS,
author = "Weixin Zeng and Xiang Zhao and Wei Wang",
title = "On entity alignment at scale",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "1009--1033",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00703-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00703-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xu:2022:PBP,
author = "Qingyu Xu and Feng Zhang and Xiaoyong Du",
title = "Payment behavior prediction on shared parking lots
with {TR-GCN}",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "1035--1058",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00722-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00722-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Sadiq:2022:IRN,
author = "Shazia Sadiq and Amir Aryani and Xiaofang Zhou",
title = "Information Resilience: the nexus of responsible and
agile approaches to information use",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "1059--1084",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00720-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00720-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Liu:2022:ECR,
author = "Fanzhen Liu and Zhao Li and Quan Z. Sheng",
title = "{eRiskCom}: an e-commerce risky community detection
platform",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "1085--1101",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00723-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00723-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Grafberger:2022:DDD,
author = "Stefan Grafberger and Paul Groth and Sebastian
Schelter",
title = "Data distribution debugging in machine learning
pipelines",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "1103--1126",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00726-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00726-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2022:DTA,
author = "Qian Li and Zhichao Wang and Guandong Xu",
title = "Deep treatment-adaptive network for causal inference",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "1127--1142",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00724-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00724-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2022:BCS,
author = "Rui Zhang and Bayu Distiawan Trisedya and Jianzhong
Qi",
title = "A benchmark and comprehensive survey on knowledge
graph entity alignment via representation learning",
journal = j-VLDB-J,
volume = "31",
number = "5",
pages = "1143--1168",
month = sep,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00747-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 29 11:34:10 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00747-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Porobic:2022:SIB,
author = "Danica Porobic",
title = "Special issue on the best papers of {DaMoN 2020}",
journal = j-VLDB-J,
volume = "31",
number = "6",
pages = "1169--1169",
month = nov,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00766-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 22 11:01:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00766-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Funke:2022:LLQ,
author = "Henning Funke and Jan M{\"u}hlig and Jens Teubner",
title = "Low-latency query compilation",
journal = j-VLDB-J,
volume = "31",
number = "6",
pages = "1171--1184",
month = nov,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00741-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 22 11:01:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00741-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bang:2022:FSC,
author = "Tiemo Bang and Norman May and Ilia Petrov and Carsten
Binnig",
title = "The full story of 1000 cores",
journal = j-VLDB-J,
volume = "31",
number = "6",
pages = "1185--1213",
month = nov,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00742-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 22 11:01:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00742-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Pietrzyk:2022:SSV,
author = "Johannes Pietrzyk and Alexander Krause and Dirk Habich
and Wolfgang Lehner",
title = "To share or not to share vector registers?",
journal = j-VLDB-J,
volume = "31",
number = "6",
pages = "1215--1236",
month = nov,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00744-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 22 11:01:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00744-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Balazinska:2022:EV,
author = "Magdalena Balazinska and Xiaofang Zhou",
title = "Editorial for {S.I.}: {VLDB 2020}",
journal = j-VLDB-J,
volume = "31",
number = "6",
pages = "1237--1238",
month = nov,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00734-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 22 11:01:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00734-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Huang:2022:OOC,
author = "Yihe Huang and William Qian and Eddie Kohler and
Barbara Liskov and Liuba Shrira",
title = "Opportunities for optimism in contended main-memory
multicore transactions",
journal = j-VLDB-J,
volume = "31",
number = "6",
pages = "1239--1261",
month = nov,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00719-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 22 11:01:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00719-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kandula:2022:DIP,
author = "Srikanth Kandula and Laurel Orr and Surajit
Chaudhuri",
title = "Data-induced predicates for sideways information
passing in query optimizers",
journal = j-VLDB-J,
volume = "31",
number = "6",
pages = "1263--1290",
month = nov,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00693-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 22 11:01:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00693-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Herlihy:2022:CCD,
author = "Maurice Herlihy and Barbara Liskov and Liuba Shrira",
title = "Cross-chain deals and adversarial commerce",
journal = j-VLDB-J,
volume = "31",
number = "6",
pages = "1291--1309",
month = nov,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00686-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 22 11:01:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00686-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2022:AAC,
author = "Yuanbing Li and Xian Wu and Yifei Jin and Jian Li and
Guoliang Li and Jianhua Feng",
title = "Adapative algorithms for crowd-aided categorization",
journal = j-VLDB-J,
volume = "31",
number = "6",
pages = "1311--1337",
month = nov,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00685-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 22 11:01:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00685-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zheng:2022:PLF,
author = "Bolong Zheng and Xi Zhao and Lianggui Weng and Quoc
Viet Hung Nguyen and Hang Liu and Christian S. Jensen",
title = "{PM-LSH}: a fast and accurate in-memory framework for
high-dimensional approximate {NN} and closest pair
search",
journal = j-VLDB-J,
volume = "31",
number = "6",
pages = "1339--1363",
month = nov,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00680-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 22 11:01:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00680-7",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lyu:2022:MTE,
author = "Bingqing Lyu and Lu Qin and Xuemin Lin and Ying Zhang
and Zhengping Qian and Jingren Zhou",
title = "Maximum and top-$k$ diversified biclique search at
scale",
journal = j-VLDB-J,
volume = "31",
number = "6",
pages = "1365--1389",
month = nov,
year = "2022",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00681-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 22 11:01:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-021-00681-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2023:HHF,
author = "Wei Chen and Weiqing Wang and Hongzhi Yin and Lei Zhao
and Xiaofang Zhou",
title = "{HFUL}: a hybrid framework for user account linkage
across location-aware social networks",
journal = j-VLDB-J,
volume = "32",
number = "1",
pages = "1--22",
month = jan,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00730-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00730-8",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ding:2023:FGE,
author = "Zeyu Ding and Yuxin Wang and Yingtai Xiao and Guanhong
Wang and Danfeng Zhang and Daniel Kifer",
title = "Free gap estimates from the exponential mechanism,
sparse vector, noisy max and related algorithms",
journal = j-VLDB-J,
volume = "32",
number = "1",
pages = "23--48",
month = jan,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00728-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00728-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fan:2023:MGC,
author = "Wenfei Fan and Yuanhao Li and Muyang Liu and Can Lu",
title = "Making graphs compact by lossless contraction",
journal = j-VLDB-J,
volume = "32",
number = "1",
pages = "49--73",
month = jan,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00731-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00731-7",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lourenco:2023:BID,
author = "Raoni Louren{\c{c}}o and Juliana Freire and Eric Simon
and Gabriel Weber and Dennis Shasha",
title = "{BugDoc}: Iterative debugging and explanation of
pipeline",
journal = j-VLDB-J,
volume = "32",
number = "1",
pages = "75--101",
month = jan,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00733-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See correction \cite{Lourenco:2023:CBI}.",
URL = "https://link.springer.com/article/10.1007/s00778-022-00733-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Pankowski:2023:ODF,
author = "Tadeusz Pankowski",
title = "Ontological databases with faceted queries",
journal = j-VLDB-J,
volume = "32",
number = "1",
pages = "103--121",
month = jan,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00735-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00735-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Liu:2023:ZRN,
author = "Gang Liu and Leying Chen and Shimin Chen",
title = "{Zen+}: a robust {NUMA}-aware {OLTP} engine optimized
for non-volatile main memory",
journal = j-VLDB-J,
volume = "32",
number = "1",
pages = "123--148",
month = jan,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00737-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00737-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fan:2023:ADG,
author = "Wenfei Fan and Ruiqi Xu and Qiang Yin and Wenyuan Yu
and Jingren Zhou",
title = "Application-driven graph partitioning",
journal = j-VLDB-J,
volume = "32",
number = "1",
pages = "149--172",
month = jan,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00736-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00736-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Miao:2023:AIR,
author = "Dongjing Miao and Pengfei Zhang and Jianzhong Li and
Ye Wang and Zhipeng Cai",
title = "Approximation and inapproximability results on
computing optimal repairs",
journal = j-VLDB-J,
volume = "32",
number = "1",
pages = "173--197",
month = jan,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00738-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00738-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Maroulis:2023:RAA,
author = "Stavros Maroulis and Nikos Bikakis and George
Papastefanatos and Panos Vassiliadis and Yannis
Vassiliou",
title = "Resource-aware adaptive indexing for in situ visual
exploration and analytics",
journal = j-VLDB-J,
volume = "32",
number = "1",
pages = "199--227",
month = jan,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00739-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00739-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Huang:2023:DEM,
author = "Jiacheng Huang and Wei Hu and Zhifeng Bao and Qijin
Chen and Yuzhong Qu",
title = "Deep entity matching with adversarial active
learning",
journal = j-VLDB-J,
volume = "32",
number = "1",
pages = "229--255",
month = jan,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00745-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00745-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2023:ABC,
author = "Kai Wang and Xuemin Lin and Lu Qin and Wenjie Zhang
and Ying Zhang",
title = "Accelerated butterfly counting with vertex priority on
bipartite graphs",
journal = j-VLDB-J,
volume = "32",
number = "2",
pages = "257--281",
month = mar,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00746-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00746-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Nikookar:2023:DRS,
author = "Sepideh Nikookar and Mohammadreza Esfandiari and Ria
Mae Borromeo and Paras Sakharkar and Sihem Amer-Yahia
and Senjuti Basu Roy",
title = "Diversifying recommendations on sequences of sets",
journal = j-VLDB-J,
volume = "32",
number = "2",
pages = "283--304",
month = mar,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00740-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00740-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Piai:2023:FGS,
author = "Federico Piai and Paolo Atzeni and Paolo Merialdo and
Divesh Srivastava",
title = "Fine-grained semantic type discovery for heterogeneous
sources using clustering",
journal = j-VLDB-J,
volume = "32",
number = "2",
pages = "305--324",
month = mar,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00743-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00743-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Liu:2023:URR,
author = "Hao Liu and Jindong Han and Yanjie Fu and Yanyan Li
and Kai Chen and Hui Xiong",
title = "Unified route representation learning for multi-modal
transportation recommendation with spatiotemporal
pre-training",
journal = j-VLDB-J,
volume = "32",
number = "2",
pages = "325--342",
month = mar,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00748-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00748-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kim:2023:FSQ,
author = "Hyunjoon Kim and Yunyoung Choi and Kunsoo Park and
Xuemin Lin and Seok-Hee Hong and Wook-Shin Han",
title = "Fast subgraph query processing and subgraph matching
via static and dynamic equivalences",
journal = j-VLDB-J,
volume = "32",
number = "2",
pages = "343--368",
month = mar,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00749-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00749-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Nguyen:2023:DRL,
author = "Thanh Tam Nguyen and Thanh Trung Huynh and Hongzhi Yin
and Matthias Weidlich and Thanh Thi Nguyen and Thai Son
Mai and Quoc Viet Hung Nguyen",
title = "Detecting rumours with latency guarantees using
massive streaming data",
journal = j-VLDB-J,
volume = "32",
number = "2",
pages = "369--387",
month = mar,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00750-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00750-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2023:VSE,
author = "Yang Li and Yu Shen and Wentao Zhang and Ce Zhang and
Bin Cui",
title = "{VolcanoML}: speeding up end-to-end {AutoML} via
scalable search space decomposition",
journal = j-VLDB-J,
volume = "32",
number = "2",
pages = "389--413",
month = mar,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00752-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00752-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Bouganim:2023:HDP,
author = "Luc Bouganim and Julien Loudet and Iulian Sandu Popa",
title = "Highly distributed and privacy-preserving queries on
personal data management systems",
journal = j-VLDB-J,
volume = "32",
number = "2",
pages = "415--445",
month = mar,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00753-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00753-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2023:IGL,
author = "Jiazun Chen and Jun Gao and Bin Cui",
title = "{ICS-GNN$^+$}: lightweight interactive community
search via graph neural network",
journal = j-VLDB-J,
volume = "32",
number = "2",
pages = "447--467",
month = mar,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00754-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00754-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Boniol:2023:CUS,
author = "Paul Boniol and Michele Linardi and Federico Roncallo
and Themis Palpanas and Mohammed Meftah and Emmanuel
Remy",
title = "Correction to: {Unsupervised} and scalable subsequence
anomaly detection in large data series",
journal = j-VLDB-J,
volume = "32",
number = "2",
pages = "469--469",
month = mar,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00678-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See \cite{Boniol:2021:USS}.",
URL = "https://link.springer.com/article/10.1007/s00778-021-00678-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kossmann:2023:CDD,
author = "Jan Kossmann and Thorsten Papenbrock and Felix
Naumann",
title = "Correction to: Data dependencies for query
optimization: a survey",
journal = j-VLDB-J,
volume = "32",
number = "2",
pages = "471--471",
month = mar,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-021-00710-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See \cite{Kossmann:2022:DDQ}.",
URL = "https://link.springer.com/article/10.1007/s00778-021-00710-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lourenco:2023:CBI,
author = "Raoni Louren{\c{c}}o and Juliana Freire and Eric Simon
and Gabriel Weber and Dennis Shasha",
title = "Correction to: {BugDoc} Iterative debugging and
explanation of pipeline executions",
journal = j-VLDB-J,
volume = "32",
number = "2",
pages = "473--473",
month = mar,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00751-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Feb 25 08:12:25 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See \cite{Lourenco:2023:BID}.",
URL = "https://link.springer.com/article/10.1007/s00778-022-00751-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Azzalini:2023:EDA,
author = "Fabio Azzalini and Davide Piantella and Emanuele
Rabosio and Letizia Tanca",
title = "Enhancing domain-aware multi-truth data fusion using
copy-based source authority and value similarity",
journal = j-VLDB-J,
volume = "32",
number = "3",
pages = "475--500",
month = may,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00757-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 21 10:46:50 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00757-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ahmed:2023:RST,
author = "Pritom Ahmed and Ahmed Eldawy and Vagelis Hristidis
and Vassilis J. Tsotras",
title = "Reverse spatial top-$k$ keyword queries",
journal = j-VLDB-J,
volume = "32",
number = "3",
pages = "501--524",
month = may,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00759-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 21 10:46:50 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00759-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2023:POB,
author = "Meng Li and Rongbiao Xie and Deyi Chen and Haipeng Dai
and Rong Gu and He Huang and Wanchun Dou and Guihai
Chen",
title = "A {Pareto} optimal {Bloom} filter family with hash
adaptivity",
journal = j-VLDB-J,
volume = "32",
number = "3",
pages = "525--548",
month = may,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00755-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 21 10:46:50 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00755-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lei:2023:HDP,
author = "Chuan Lei and Abdul Quamar and Vasilis Efthymiou and
Fatma {\"O}zcan and Rana Alotaibi",
title = "{HERMES}: data placement and schema optimization for
enterprise knowledge bases",
journal = j-VLDB-J,
volume = "32",
number = "3",
pages = "549--574",
month = may,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00756-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 21 10:46:50 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00756-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2023:ENQ,
author = "Jiajia Li and Cancan Ni and Dan He and Lei Li and
Xiufeng Xia and Xiaofang Zhou",
title = "Efficient $k$ {NN} query for moving objects on
time-dependent road networks",
journal = j-VLDB-J,
volume = "32",
number = "3",
pages = "575--594",
month = may,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00758-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 21 10:46:50 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00758-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Liu:2023:MCS,
author = "Ziyi Liu and Lei Li and Mengxuan Zhang and Wen Hua and
Xiaofang Zhou",
title = "Multi-constraint shortest path using forest hop
labeling",
journal = j-VLDB-J,
volume = "32",
number = "3",
pages = "595--621",
month = may,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00760-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 21 10:46:50 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00760-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2023:LBQ,
author = "Pengcheng Zhang and Bin Yao and Chao Gao and Bin Wu
and Xiao He and Feifei Li and Yuanfei Lu and Chaoqun
Zhan and Feilong Tang",
title = "Learning-based query optimization for multi-probe
approximate nearest neighbor search",
journal = j-VLDB-J,
volume = "32",
number = "3",
pages = "623--645",
month = may,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00762-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 21 10:46:50 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00762-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Luo:2023:TMH,
author = "Qi Luo and Dongxiao Yu and Zhipeng Cai and Xuemin Lin
and Guanghui Wang and Xiuzhen Cheng",
title = "Toward maintenance of hypercores in large-scale
dynamic hypergraphs",
journal = j-VLDB-J,
volume = "32",
number = "3",
pages = "647--664",
month = may,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00763-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 21 10:46:50 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00763-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2023:PPB,
author = "Liang Zhang and Noura Alghamdi and Huayi Zhang and
Mohamed Y. Eltabakh and Elke A. Rundensteiner",
title = "{PARROT}: pattern-based correlation exploitation in
big partitioned data series",
journal = j-VLDB-J,
volume = "32",
number = "3",
pages = "665--688",
month = may,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00767-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Apr 21 10:46:50 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00767-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wellenzohn:2023:RSC,
author = "Kevin Wellenzohn and Michael H. B{\"o}hlen and Sven
Helmer and Antoine Pietri and Stefano Zacchiroli",
title = "Robust and scalable content-and-structure indexing",
journal = j-VLDB-J,
volume = "32",
number = "4",
pages = "689--715",
month = jul,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00764-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jun 1 08:33:00 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00764-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Miao:2023:PPP,
author = "Xupeng Miao and Wentao Zhang and Yuezihan Jiang and
Fangcheng Fu and Yingxia Shao and Lei Chen and Yangyu
Tao and Gang Cao and Bin Cui",
title = "{P$^2$CG}: a privacy preserving collaborative graph
neural network training framework",
journal = j-VLDB-J,
volume = "32",
number = "4",
pages = "717--736",
month = jul,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00768-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jun 1 08:33:00 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00768-8",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Islam:2023:GFE,
author = "Md Mouinul Islam and Mahsa Asadi and Sihem Amer-Yahia
and Senjuti Basu Roy",
title = "A generic framework for efficient computation of
top-$k$ diverse results",
journal = j-VLDB-J,
volume = "32",
number = "4",
pages = "737--761",
month = jul,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00770-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jun 1 08:33:00 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00770-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Echihabi:2023:PDS,
author = "Karima Echihabi and Theophanis Tsandilas and Anna
Gogolou and Anastasia Bezerianos and Themis Palpanas",
title = "{ProS}: data series progressive $k$-{NN} similarity
search and classification with probabilistic quality
guarantees",
journal = j-VLDB-J,
volume = "32",
number = "4",
pages = "763--789",
month = jul,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00771-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jun 1 08:33:00 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00771-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Whang:2023:DCQ,
author = "Steven Euijong Whang and Yuji Roh and Hwanjun Song and
Jae-Gil Lee",
title = "Data collection and quality challenges in deep
learning: a data-centric {AI} perspective",
journal = j-VLDB-J,
volume = "32",
number = "4",
pages = "791--813",
month = jul,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00775-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jun 1 08:33:00 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00775-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lou:2023:TTA,
author = "Yunkai Lou and Chaokun Wang and Tiankai Gu and Hao
Feng and Jun Chen and Jeffrey Xu Yu",
title = "Time-topology analysis on temporal graphs",
journal = j-VLDB-J,
volume = "32",
number = "4",
pages = "815--843",
month = jul,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00772-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jun 1 08:33:00 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00772-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ntroumpogiannis:2023:MLA,
author = "Antonios Ntroumpogiannis and Michail Giannoulis and
Nikolaos Myrtakis and Vassilis Christophides and Eric
Simon and Ioannis Tsamardinos",
title = "A meta-level analysis of online anomaly detectors",
journal = j-VLDB-J,
volume = "32",
number = "4",
pages = "845--886",
month = jul,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00773-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jun 1 08:33:00 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00773-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2023:SSQ,
author = "Dongxiang Zhang and Zhihao Chang and Dingyu Yang and
Dongsheng Li and Kian-Lee Tan and Ke Chen and Gang
Chen",
title = "{SQUID}: subtrajectory query in trillion-scale {GPS}
database",
journal = j-VLDB-J,
volume = "32",
number = "4",
pages = "887--904",
month = jul,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00777-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jun 1 08:33:00 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00777-7",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Katsogiannis-Meimarakis:2023:SDL,
author = "George Katsogiannis-Meimarakis and Georgia Koutrika",
title = "A survey on deep learning approaches for
text-to-{SQL}",
journal = j-VLDB-J,
volume = "32",
number = "4",
pages = "905--936",
month = jul,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00776-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Jun 1 08:33:00 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00776-8",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhao:2023:LSS,
author = "Kangfei Zhao and Jeffrey Xu Yu and Qiyan Li and Hao
Zhang and Yu Rong",
title = "Learned sketch for subgraph counting: a holistic
approach",
journal = j-VLDB-J,
volume = "32",
number = "5",
pages = "937--962",
month = sep,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00781-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Aug 18 07:36:55 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00781-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yamada:2023:ALT,
author = "Masaya Yamada and Hiroyuki Kitagawa and Toshiyuki
Amagasa and Akiyoshi Matono",
title = "Augmented lineage: traceability of data analysis
including complex {UDF} processing",
journal = j-VLDB-J,
volume = "32",
number = "5",
pages = "963--983",
month = sep,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00769-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Aug 18 07:36:55 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00769-7",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Verwiebe:2023:SWT,
author = "Juliane Verwiebe and Philipp M. Grulich and Jonas
Traub and Volker Markl",
title = "Survey of window types for aggregation in stream
processing systems",
journal = j-VLDB-J,
volume = "32",
number = "5",
pages = "985--1011",
month = sep,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00778-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Aug 18 07:36:55 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See \cite{Verwiebe:2024:CSW}.",
URL = "https://link.springer.com/article/10.1007/s00778-022-00778-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhou:2023:BCB,
author = "Alexander Zhou and Yue Wang and Lei Chen",
title = "Butterfly counting and bitruss decomposition on
uncertain bipartite graphs",
journal = j-VLDB-J,
volume = "32",
number = "5",
pages = "1013--1036",
month = sep,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00782-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Aug 18 07:36:55 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00782-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hirsch:2023:EDK,
author = "Vitali Hirsch and Peter Reimann and Dennis
Treder-Tschechlov and Holger Schwarz and Bernhard
Mitschang",
title = "Exploiting domain knowledge to address class imbalance
and a heterogeneous feature space in multi-class
classification",
journal = j-VLDB-J,
volume = "32",
number = "5",
pages = "1037--1064",
month = sep,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00780-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Aug 18 07:36:55 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00780-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xu:2023:LUI,
author = "Jia Xu and Zulong Chen and Wanjie Tao and Ziyi Wang
and Detao Lv and Yao Yu and Chuanfei Xu",
title = "Leveraging user itinerary to improve personalized deep
matching at {Fliggy}",
journal = j-VLDB-J,
volume = "32",
number = "5",
pages = "1065--1086",
month = sep,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00787-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Aug 18 07:36:55 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00787-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gou:2023:SWB,
author = "Xiangyang Gou and Lei Zou",
title = "Sliding window-based approximate triangle counting
with bounded memory usage",
journal = j-VLDB-J,
volume = "32",
number = "5",
pages = "1087--1110",
month = sep,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00783-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Aug 18 07:36:55 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00783-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Schiavio:2023:DDQ,
author = "Filippo Schiavio and Daniele Bonetta and Walter
Binder",
title = "{DynQ}: a dynamic query engine with query-reuse
capabilities embedded in a polyglot runtime",
journal = j-VLDB-J,
volume = "32",
number = "5",
pages = "1111--1135",
month = sep,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00784-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Aug 18 07:36:55 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00784-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yang:2023:BCE,
author = "Jianye Yang and Yun Peng and Dian Ouyang and Wenjie
Zhang and Xuemin Lin and Xiang Zhao",
title = "$ (p, q)$-biclique counting and enumeration for large
sparse bipartite graphs",
journal = j-VLDB-J,
volume = "32",
number = "5",
pages = "1137--1161",
month = sep,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00786-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Aug 18 07:36:55 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00786-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Naumann:2023:ESI,
author = "Felix Naumann and Xin Luna Dong",
title = "Editorial: Special Issue for Selected Papers of {VLDB
2021}",
journal = j-VLDB-J,
volume = "32",
number = "6",
pages = "1163--1163",
month = nov,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00792-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 21 08:56:16 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00792-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fent:2023:PPE,
author = "Philipp Fent and Altan Birler and Thomas Neumann",
title = "Practical planning and execution of groupjoin and
nested aggregates",
journal = j-VLDB-J,
volume = "32",
number = "6",
pages = "1165--1190",
month = nov,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00765-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 21 08:56:16 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00765-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Farias:2023:LDD,
author = "Victor A. E. Farias and Felipe T. Brito and Cheryl
Flynn and Javam C. Machado and Subhabrata Majumdar and
Divesh Srivastava",
title = "Local dampening: differential privacy for non-numeric
queries via local sensitivity",
journal = j-VLDB-J,
volume = "32",
number = "6",
pages = "1191--1214",
month = nov,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-022-00774-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 21 08:56:16 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-022-00774-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Li:2023:EEM,
author = "Yuliang Li and Jinfeng Li and Yoshi Suhara and AnHai
Doan and Wang-Chiew Tan",
title = "Effective entity matching with transformers",
journal = j-VLDB-J,
volume = "32",
number = "6",
pages = "1215--1235",
month = nov,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00779-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 21 08:56:16 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00779-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yang:2023:PSE,
author = "Renchi Yang and Jieming Shi and Xiaokui Xiao and Yin
Yang and Sourav S. Bhowmick and Juncheng Liu",
title = "{PANE}: scalable and effective attributed network
embedding",
journal = j-VLDB-J,
volume = "32",
number = "6",
pages = "1237--1262",
month = nov,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00790-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 21 08:56:16 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00790-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ouyang:2023:WHM,
author = "Dian Ouyang and Dong Wen and Lu Qin and Lijun Chang
and Xuemin Lin and Ying Zhang",
title = "When hierarchy meets 2-hop-labeling: efficient
shortest distance and path queries on road networks",
journal = j-VLDB-J,
volume = "32",
number = "6",
pages = "1263--1287",
month = nov,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00789-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 21 08:56:16 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00789-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Qian:2023:IDD,
author = "Chaoqin Qian and Menglu Li and Zijing Tan and Ai Ran
and Shuai Ma",
title = "Incremental discovery of denial constraints",
journal = j-VLDB-J,
volume = "32",
number = "6",
pages = "1289--1313",
month = nov,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00788-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 21 08:56:16 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00788-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2023:TGC,
author = "Zuozhi Wang and Kai Zeng and Botong Huang and Wei Chen
and Xiaozong Cui and Bo Wang and Ji Liu and Liya Fan
and Dachuan Qu and Zhenyu Hou and Tao Guan and Chen Li
and Jingren Zhou",
title = "{Tempura}: a general cost-based optimizer framework
for incremental data processing (Journal Version)",
journal = j-VLDB-J,
volume = "32",
number = "6",
pages = "1315--1342",
month = nov,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00785-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 21 08:56:16 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00785-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Hellings:2023:BSB,
author = "Jelle Hellings and Mohammad Sadoghi",
title = "{ByShard}: sharding in a {Byzantine} environment",
journal = j-VLDB-J,
volume = "32",
number = "6",
pages = "1343--1367",
month = nov,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00794-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 21 08:56:16 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00794-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Papadakis:2023:AOO,
author = "George Papadakis and Vasilis Efthymiou and Emmanouil
Thanos and Oktie Hassanzadeh and Peter Christen",
title = "An analysis of one-to-one matching algorithms for
entity resolution",
journal = j-VLDB-J,
volume = "32",
number = "6",
pages = "1369--1400",
month = nov,
year = "2023",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00791-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Oct 21 08:56:16 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00791-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2024:SDC,
author = "Haoyu Wang and Aoqian Zhang and Shaoxu Song and
Jianmin Wang",
title = "Streaming data cleaning based on speed change",
journal = j-VLDB-J,
volume = "33",
number = "1",
pages = "1--24",
month = jan,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00796-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00796-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Aghasadeghi:2024:TGP,
author = "Amir Aghasadeghi and Jan {Van den Bussche} and Julia
Stoyanovich",
title = "Temporal graph patterns by timed automata",
journal = j-VLDB-J,
volume = "33",
number = "1",
pages = "25--47",
month = jan,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00795-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00795-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chang:2024:NOA,
author = "Lijun Chang and Zhiyi Wang",
title = "A near-optimal approach to edge connectivity-based
hierarchical graph decomposition",
journal = j-VLDB-J,
volume = "33",
number = "1",
pages = "49--71",
month = jan,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00797-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00797-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Christodoulou:2024:HHI,
author = "George Christodoulou and Panagiotis Bouros and Nikos
Mamoulis",
title = "{HINT}: a hierarchical interval index for {Allen}
relationships",
journal = j-VLDB-J,
volume = "33",
number = "1",
pages = "73--100",
month = jan,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00798-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00798-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Farhan:2024:BBD,
author = "Muhammad Farhan and Henning Koehler and Qing Wang",
title = "{BatchHL$^+$}: batch dynamic labelling for distance
queries on large-scale networks",
journal = j-VLDB-J,
volume = "33",
number = "1",
pages = "101--129",
month = jan,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00799-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00799-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Meilicke:2024:ABR,
author = "Christian Meilicke and Melisachew Wudage Chekol and
Patrick Betz and Manuel Fink and Heiner Stuckeschmidt",
title = "Anytime bottom-up rule learning for large-scale
knowledge graph completion",
journal = j-VLDB-J,
volume = "33",
number = "1",
pages = "131--161",
month = jan,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00800-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00800-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhao:2024:CBT,
author = "Yan Zhao and Kai Zheng and Ziwei Wang and Liwei Deng
and Bin Yang and Torben Bach Pedersen and Christian S.
Jensen and Xiaofang Zhou",
title = "Coalition-based task assignment with priority-aware
fairness in spatial crowdsourcing",
journal = j-VLDB-J,
volume = "33",
number = "1",
pages = "163--184",
month = jan,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00802-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00802-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Shaham:2024:SSD,
author = "Sina Shaham and Gabriel Ghinita and Cyrus Shahabi",
title = "Supporting secure dynamic alert zones using searchable
encryption and graph embedding",
journal = j-VLDB-J,
volume = "33",
number = "1",
pages = "185--206",
month = jan,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00803-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00803-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ma:2024:ADD,
author = "Chenhao Ma and Yixiang Fang and Reynold Cheng and Laks
V. S. Lakshmanan and Xiaolin Han and Xiaodong Li",
title = "Accelerating directed densest subgraph queries with
software and hardware approaches",
journal = j-VLDB-J,
volume = "33",
number = "1",
pages = "207--230",
month = jan,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00805-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00805-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Mouratidis:2024:QCD,
author = "Kyriakos Mouratidis and Keming Li and Bo Tang",
title = "Quantifying the competitiveness of a dataset in
relation to general preferences",
journal = j-VLDB-J,
volume = "33",
number = "1",
pages = "231--250",
month = jan,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00804-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00804-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Verwiebe:2024:CSW,
author = "Juliane Verwiebe and Philipp M. Grulich and Jonas
Traub and Volker Markl",
title = "Correction to: {Survey} of window types for
aggregation in stream processing systems",
journal = j-VLDB-J,
volume = "33",
number = "1",
pages = "251--251",
month = jan,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00793-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See \cite{Verwiebe:2023:SWT}.",
URL = "https://link.springer.com/article/10.1007/s00778-023-00793-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yang:2024:CTF,
author = "Fan Yang and Faisal M. Almutairi and Hyun Ah Song and
Christos Faloutsos and Nicholas D. Sidiropoulos and
Vladimir Zadorozhny",
title = "Correction to: {TurboLift}: fast accuracy lifting for
historical data recovery",
journal = j-VLDB-J,
volume = "33",
number = "1",
pages = "253--253",
month = jan,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00801-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See \cite{Yang:2020:TFA}.",
URL = "https://link.springer.com/article/10.1007/s00778-023-00801-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Liu:2024:TDS,
author = "Tongyu Liu and Ju Fan and Guoliang Li and Nan Tang and
Xiaoyong Du",
title = "Tabular data synthesis with generative adversarial
networks: design space and optimizations",
journal = j-VLDB-J,
volume = "33",
number = "2",
pages = "255--280",
month = mar,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00807-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00807-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Karpov:2024:MFA,
author = "Nikolai Karpov and Haoyu Zhang and Qin Zhang",
title = "{MinJoin++}: a fast algorithm for string similarity
joins under edit distance",
journal = j-VLDB-J,
volume = "33",
number = "2",
pages = "281--299",
month = mar,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00806-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00806-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Usta:2024:XEN,
author = "Arif Usta and Akifhan Karakayali and {\"O}zg{\"u}r
Ulusoy",
title = "{xDBTagger}: explainable natural language interface to
databases using keyword mappings and schema graph",
journal = j-VLDB-J,
volume = "33",
number = "2",
pages = "301--321",
month = mar,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00809-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00809-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2024:CEU,
author = "Jiayi Wang and Chengliang Chai and Jiabin Liu and
Guoliang Li",
title = "Cardinality estimation using normalizing flow",
journal = j-VLDB-J,
volume = "33",
number = "2",
pages = "323--348",
month = mar,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00808-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00808-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Arroyuelo:2024:ORC,
author = "Diego Arroyuelo and Adri{\'a}n G{\'o}mez-Brand{\'o}n
and Aidan Hogan and Gonzalo Navarro and Javiel
Rojas-Ledesma",
title = "Optimizing {RPQs} over a compact graph
representation",
journal = j-VLDB-J,
volume = "33",
number = "2",
pages = "349--374",
month = mar,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00811-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00811-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2024:QEP,
author = "Zhiwen Chen and Daokun Hu and Wenkui Che and Jianhua
Sun and Hao Chen",
title = "A quantitative evaluation of persistent memory hash
indexes",
journal = j-VLDB-J,
volume = "33",
number = "2",
pages = "375--397",
month = mar,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00812-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/hash.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00812-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Abello:2024:EEM,
author = "Alberto Abell{\'o} and James Cheney",
title = "{Eris}: efficiently measuring discord in
multidimensional sources",
journal = j-VLDB-J,
volume = "33",
number = "2",
pages = "399--423",
month = mar,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00810-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00810-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Jiang:2024:SEM,
author = "Jiawei Jiang and Shaoduo Gan and Bo Du and Gustavo
Alonso and Ana Klimovic and Ankit Singla and Wentao Wu
and Sheng Wang and Ce Zhang",
title = "A systematic evaluation of machine learning on
serverless infrastructure",
journal = j-VLDB-J,
volume = "33",
number = "2",
pages = "425--449",
month = mar,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00813-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00813-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2024:STS,
author = "Shuhao Zhang and Juan Soto and Volker Markl",
title = "A survey on transactional stream processing",
journal = j-VLDB-J,
volume = "33",
number = "2",
pages = "451--479",
month = mar,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00814-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00814-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{dHondt:2024:EDM,
author = "Jens E. d'Hondt and Koen Minartz and Odysseas
Papapetrou",
title = "Efficient detection of multivariate correlations with
different correlation measures",
journal = j-VLDB-J,
volume = "33",
number = "2",
pages = "481--505",
month = mar,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00815-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00815-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fragkoulis:2024:SES,
author = "Marios Fragkoulis and Paris Carbone and Vasiliki
Kalavri and Asterios Katsifodimos",
title = "A survey on the evolution of stream processing
systems",
journal = j-VLDB-J,
volume = "33",
number = "2",
pages = "507--541",
month = mar,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00819-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00819-8",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhao:2024:RRE,
author = "Hongyao Zhao and Jingyao Li and Wei Lu and Qian Zhang
and Wanqing Yang and Jiajia Zhong and Meihui Zhang and
Haixiang Li and Xiaoyong Du and Anqun Pan",
title = "{RCBench}: an {RDMA}-enabled transaction framework for
analyzing concurrency control algorithms",
journal = j-VLDB-J,
volume = "33",
number = "2",
pages = "543--567",
month = mar,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00821-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Mar 19 08:11:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00821-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2024:LCS,
author = "Junhua Zhang and Long Yuan and Wentao Li and Lu Qin
and Ying Zhang and Wenjie Zhang",
title = "Label-constrained shortest path query processing on
road networks",
journal = j-VLDB-J,
volume = "33",
number = "3",
pages = "569--593",
month = may,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00825-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 24 13:05:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00825-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Fang:2024:NWC,
author = "James Fang and Dmitry Lychagin and Michael J. Carey
and Vassilis J. Tsotras",
title = "A new window clause for {SQL++}",
journal = j-VLDB-J,
volume = "33",
number = "3",
pages = "595--623",
month = may,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00830-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 24 13:05:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00830-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lee:2024:HMT,
author = "Geon Lee and Seokbum Yoon and Jihoon Ko and Hyunju Kim
and Kijung Shin",
title = "Hypergraph motifs and their extensions beyond binary",
journal = j-VLDB-J,
volume = "33",
number = "3",
pages = "625--665",
month = may,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00827-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 24 13:05:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00827-8",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Liao:2024:SDG,
author = "Ningyi Liao and Dingheng Mo and Siqiang Luo and Xiang
Li and Pengcheng Yin",
title = "Scalable decoupling graph neural network with
feature-oriented optimization",
journal = j-VLDB-J,
volume = "33",
number = "3",
pages = "667--683",
month = may,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00829-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 24 13:05:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00829-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Liang:2024:STC,
author = "Anqi Liang and Bin Yao and Bo Wang and Yinpei Liu and
Zhida Chen and Jiong Xie and Feifei Li",
title = "Sub-trajectory clustering with deep reinforcement
learning",
journal = j-VLDB-J,
volume = "33",
number = "3",
pages = "685--702",
month = may,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00833-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 24 13:05:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00833-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yao:2024:ISB,
author = "Kai Yao and Lijun Chang and Jeffrey Xu Yu",
title = "Identifying similar-bicliques in bipartite graphs",
journal = j-VLDB-J,
volume = "33",
number = "3",
pages = "703--726",
month = may,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00834-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 24 13:05:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00834-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xia:2024:TSD,
author = "Tianrui Xia and Jinzhao Xiao and Yuxiang Huang and
Changyu Hu and Shaoxu Song and Xiangdong Huang and
Jianmin Wang",
title = "Time series data encoding in {Apache IoTDB}:
comparative analysis and recommendation",
journal = j-VLDB-J,
volume = "33",
number = "3",
pages = "727--752",
month = may,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00840-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 24 13:05:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00840-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Ting:2024:NDT,
author = "Kai Ming Ting and Zongyou Liu and Lei Gong and Hang
Zhang and Ye Zhu",
title = "A new distributional treatment for time series anomaly
detection",
journal = j-VLDB-J,
volume = "33",
number = "3",
pages = "753--780",
month = may,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00832-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 24 13:05:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00832-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Gong:2024:IAI,
author = "Shufeng Gong and Chao Tian and Qiang Yin and Zhengdong
Wang and Song Yu and Yanfeng Zhang and Wenyuan Yu and
Liang Geng and Chong Fu and Ge Yu and Jingren Zhou",
title = "{Ingress}: an automated incremental graph processing
system",
journal = j-VLDB-J,
volume = "33",
number = "3",
pages = "781--806",
month = may,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00838-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 24 13:05:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00838-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lin:2024:RRE,
author = "Hong Lin and Ke Chen and Dawei Jiang and Lidan Shou
and Gang Chen",
title = "{Refiner}: a reliable and efficient incentive-driven
federated learning system powered by blockchain",
journal = j-VLDB-J,
volume = "33",
number = "3",
pages = "807--831",
month = may,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00839-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 24 13:05:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/bitcoin.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See correction \cite{Lin:2024:CRR}.",
URL = "https://link.springer.com/article/10.1007/s00778-024-00839-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Jiang:2024:HGM,
author = "Jiawei Jiang and Yi Wei and Yu Liu and Wentao Wu and
Chuang Hu and Zhigao Zheng and Ziyi Zhang and Yingxia
Shao and Ce Zhang",
title = "How good are machine learning clouds? {Benchmarking}
two snapshots over 5 years",
journal = j-VLDB-J,
volume = "33",
number = "3",
pages = "833--857",
month = may,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00842-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 24 13:05:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00842-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Magalhaes:2024:MDM,
author = "Arlino Magalhaes and Angelo Brayner and Jose Maria
Monteiro",
title = "{MM-DIRECT}: Main memory database instant recovery
with tuple consistent checkpoint",
journal = j-VLDB-J,
volume = "33",
number = "3",
pages = "859--882",
month = may,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00846-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 24 13:05:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00846-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Jia:2024:HNA,
author = "Tong Jia and Ying Li and Yong Yang and Gang Huang",
title = "{Hilogx}: noise-aware log-based anomaly detection with
human feedback",
journal = j-VLDB-J,
volume = "33",
number = "3",
pages = "883--900",
month = may,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00843-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Wed Apr 24 13:05:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00843-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Boehm:2024:SIM,
author = "Matthias Boehm and Nesime Tatbul",
title = "Special issue on ``{Machine} learning and
databases''",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "901--901",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00848-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00848-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kara:2024:FIA,
author = "Ahmet Kara and Milos Nikolic and Dan Olteanu and
Haozhe Zhang",
title = "{F-IVM}: analytics over relational databases under
updates",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "903--929",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00817-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00817-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Huang:2024:ERA,
author = "Enhui Huang and Yanlei Diao and Anna Liu and Liping
Peng and Luciano {Di Palma}",
title = "Efficient and robust active learning methods for
interactive database exploration",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "931--956",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00816-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00816-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Neutatz:2024:AHC,
author = "Felix Neutatz and Marius Lindauer and Ziawasch
Abedjan",
title = "{AutoML} in heavily constrained applications",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "957--979",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00820-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00820-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Meduri:2024:AAL,
author = "Venkata Vamsikrishna Meduri and Abdul Quamar and Chuan
Lei and Xiao Qin and Berthold Reinwald",
title = "{Alfa}: active learning for graph neural network-based
semantic schema alignment",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "981--1011",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00822-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00822-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Olteanu:2024:GRD,
author = "Dan Olteanu and Nils Vortmeier and {\Dbar}or{\dbar}e
{\v{Z}}ivanovi{\'c}",
title = "{Givens} rotations for {$ Q R $} decomposition, {SVD}
and {PCA} over database joins",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "1013--1037",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00818-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00818-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
keywords = "FIGARO (algorithm for computing the upper-triangular
matrix in the $Q R$ decomposition of the matrix)",
}
@Article{Paganelli:2024:MFA,
author = "Matteo Paganelli and Donato Tiano and Francesco
Guerra",
title = "A multi-facet analysis of {BERT}-based entity matching
models",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "1039--1064",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00824-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00824-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Luo:2024:MPM,
author = "Yongping Luo and Peiquan Jin and Zhaole Chu and
Xiaoliang Wang and Yigui Yuan and Zhou Zhang and Yun
Luo and Xufei Wu and Peng Zou",
title = "{Morphtree}: a polymorphic main-memory learned index
for dynamic workloads",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "1065--1084",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00823-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00823-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Trummer:2024:DBM,
author = "Immanuel Trummer",
title = "{DB-BERT}: making database tuning tools ``read'' the
manual",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "1085--1104",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00831-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00831-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Huynh:2024:TFR,
author = "Andy Huynh and Harshal A. Chaudhari and Evimaria Terzi
and Manos Athanassoulis",
title = "Towards flexibility and robustness of {LSM} trees",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "1105--1128",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00826-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00826-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Redyuk:2024:ADD,
author = "Sergey Redyuk and Zoi Kaoudi and Sebastian Schelter
and Volker Markl",
title = "Assisted design of data science pipelines",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "1129--1153",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00835-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00835-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Vu:2024:LBF,
author = "Tin Vu and Alberto Belussi and Sara Migliorini and
Ahmed Eldawy",
title = "A learning-based framework for spatial join
processing: estimation, optimization and tuning",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "1155--1177",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00836-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00836-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Song:2024:SST,
author = "Yuanfeng Song and Raymond Chi-Wing Wong and Xuefang
Zhao",
title = "{Speech-to-SQL}: toward speech-driven {SQL} query
generation from natural language question",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "1179--1201",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00837-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00837-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Shahbazi:2024:REI,
author = "Nima Shahbazi and Abolfazl Asudeh",
title = "Reliability evaluation of individual predictions: a
data-centric approach",
journal = j-VLDB-J,
volume = "33",
number = "4",
pages = "1203--1230",
month = jul,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00857-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Mon Aug 5 15:56:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00857-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xu:2024:SGD,
author = "Lijie Xu and Shuang Qiu and Binhang Yuan and Jiawei
Jiang and Cedric Renggli and Shaoduo Gan and Kaan Kara
and Guoliang Li and Ji Liu and Wentao Wu and Jieping Ye
and Ce Zhang",
title = "Stochastic gradient descent without full data shuffle:
with applications to in-database machine learning and
deep learning systems",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1231--1255",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00845-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00845-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Karegar:2024:DAI,
author = "Reza Karegar and Melicaalsadat Mirsafian and Parke
Godfrey and Lukasz Golab and Mehdi Kargar and Divesh
Srivastava and Jaroslaw Szlichta",
title = "Discovering approximate implicit domain orders through
order dependencies",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1257--1282",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00847-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00847-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chang:2024:DDT,
author = "Jiwon Chang and Bohan Cui and Fatemeh Nargesian and
Abolfazl Asudeh and H. V. Jagadish",
title = "Data distribution tailoring revisited: cost-efficient
integration of representative data",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1283--1306",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00849-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00849-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Chen:2024:LAL,
author = "Xingguang Chen and Rong Zhu and Bolin Ding and Sibo
Wang and Jingren Zhou",
title = "{Lero}: applying learning-to-rank in query optimizer",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1307--1331",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00850-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00850-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Preti:2024:HDO,
author = "Giulia Preti and Gianmarco {De Francisci Morales} and
Francesco Bonchi",
title = "Hyper-distance oracles in hypergraphs",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1333--1356",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00851-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00851-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Shi:2024:ECE,
author = "Gongyu Shi and Geng Wang and Shi-Feng Sun and Dawu
Gu",
title = "Efficient cryptanalysis of an encrypted database
supporting data interoperability",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1357--1375",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00852-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00852-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2024:SDT,
author = "Chen Jason Zhang and Yunrui Liu and Pengcheng Zeng and
Ting Wu and Lei Chen and Pan Hui and Fei Hao",
title = "Similarity-driven and task-driven models for diversity
of opinion in crowdsourcing markets",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1377--1398",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00853-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00853-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2024:EAR,
author = "Kai Wang and Minghao Cai and Xiaoshuang Chen and
Xuemin Lin and Wenjie Zhang and Lu Qin and Ying Zhang",
title = "Efficient algorithms for reachability and path queries
on temporal bipartite graphs",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1399--1426",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00854-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00854-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xu:2024:EEA,
author = "Yichen Xu and Chenhao Ma and Yixiang Fang and Zhifeng
Bao",
title = "Efficient and effective algorithms for densest
subgraph discovery and maintenance",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1427--1452",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00855-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00855-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wang:2024:PBC,
author = "Zhibin Wang and Longbin Lai and Yixue Liu and Bing
Shui and Chen Tian and Sheng Zhong",
title = "Parallelization of butterfly counting on hierarchical
memory",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1453--1484",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00856-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00856-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Song:2024:SHT,
author = "Haoze Song and Wenchao Zhou and Heming Cui and Xiang
Peng and Feifei Li",
title = "A survey on hybrid transactional and analytical
processing",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1485--1515",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00858-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00858-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Peng:2024:MMC,
author = "Peng Peng and Shengyi Ji and M. Tamer {\"O}zsu and Lei
Zou",
title = "Minimum motif-cut: a workload-aware {RDF} graph
partitioning strategy",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1517--1542",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00860-1",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00860-1",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Xia:2024:GBB,
author = "Yifei Xia and Feng Zhang and Qingyu Xu and Mingde
Zhang and Zhiming Yao and Lv Lu and Xiaoyong Du and
Dong Deng and Bingsheng He and Siqi Ma",
title = "{GPU}-based butterfly counting",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1543--1567",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00861-0",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00861-0",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Kitsios:2024:FGL,
author = "Xenophon Kitsios and Panagiotis Liakos and Katia
Papakonstantinopoulou and Yannis Kotidis",
title = "Flexible grouping of linear segments for highly
accurate lossy compression of time series data",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1569--1589",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00862-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00862-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Pan:2024:SVD,
author = "James Jie Pan and Jianguo Wang and Guoliang Li",
title = "Survey of vector database management systems",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1591--1615",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00864-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00864-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Liang:2024:FSF,
author = "Zhiyu Liang and Hongzhi Wang",
title = "{FedST}: secure federated shapelet transformation for
time series classification",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1617--1641",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00865-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00865-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Yang:2024:FRC,
author = "Yifei Yang and Xiangyao Yu and Marco Serafini and
Ashraf Aboulnaga and Michael Stonebraker",
title = "{FlexpushdownDB}: rethinking computation pushdown for
cloud {OLAP DBMSs}",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1643--1670",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00867-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00867-8",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Neuhof:2024:OBF,
author = "Franziska Neuhof and Marco Fisichella and George
Papadakis and Konstantinos Nikoletos and Nikolaus
Augsten and Wolfgang Nejdl and Manolis Koubarakis",
title = "Open benchmark for filtering techniques in entity
resolution",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1671--1696",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00868-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00868-7",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Liu:2024:WUG,
author = "Zirui Liu and Fenghao Dong and Chengwu Liu and
Xiangwei Deng and Tong Yang and Yikai Zhao and Jizhou
Li and Bin Cui and Gong Zhang",
title = "{WavingSketch}: an unbiased and generic sketch for
finding top-$k$ items in data streams",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1697--1722",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00869-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00869-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Zhang:2024:FES,
author = "Xingyi Zhang and Jinchao Huang and Fangyuan Zhang and
Sibo Wang",
title = "{FICOM}: an effective and scalable active learning
framework for {GNNs} on semi-supervised node
classification",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1723--1742",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00870-z",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00870-z",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Wu:2024:AZS,
author = "Xinle Wu and Xingjian Wu and Bin Yang and Lekui Zhou
and Chenjuan Guo and Xiangfei Qiu and Jilin Hu and
Zhenli Sheng and Christian S. Jensen",
title = "{AutoCTS++}: zero-shot joint neural architecture and
hyperparameter search for correlated time series
forecasting",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1743--1770",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00872-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00872-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{Lin:2024:CRR,
author = "Hong Lin and Ke Chen and Dawei Jiang and Lidan Shou
and Gang Chen",
title = "Correction to: {``Refiner: a reliable and efficient
incentive-driven federated learning system powered by
blockchain''}",
journal = j-VLDB-J,
volume = "33",
number = "5",
pages = "1771--1771",
month = sep,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00866-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sun Aug 18 07:21:17 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/bitcoin.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
note = "See \cite{Lin:2024:RRE}.",
URL = "https://link.springer.com/article/10.1007/s00778-024-00866-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "https://dl.acm.org/loi/vldb;
https://link.springer.com/journal/778",
}
@Article{May:2024:SIM,
author = "Norman May and Spyros Blanas and Danica Porobic",
title = "Special issue: modern hardware",
journal = j-VLDB-J,
volume = "33",
number = "6",
pages = "1773--1774",
month = nov,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00841-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Oct 22 11:15:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00841-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Nicholson:2024:HME,
author = "Hamish Nicholson and Periklis Chrysogelos and
Anastasia Ailamaki",
title = "{HPCache}: memory-efficient {OLAP} through
proportional caching revisited",
journal = j-VLDB-J,
volume = "33",
number = "6",
pages = "1775--1791",
month = nov,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-023-00828-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Oct 22 11:15:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-023-00828-7",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Li:2024:PAL,
author = "Tianyu Li and Badrish Chandramouli and Samuel Madden",
title = "Performant almost-latch-free data structures using
epoch protection in more depth",
journal = j-VLDB-J,
volume = "33",
number = "6",
pages = "1793--1812",
month = nov,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00859-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Oct 22 11:15:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00859-8",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Wang:2024:OLB,
author = "Ruihong Wang and Chuqing Gao and Jianguo Wang and
Prishita Kadam and M. Tamer{\"O}zsu and Walid G. Aref",
title = "Optimizing {LSM}-based indexes for disaggregated
memory",
journal = j-VLDB-J,
volume = "33",
number = "6",
pages = "1813--1836",
month = nov,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00863-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Oct 22 11:15:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00863-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Fan:2024:EST,
author = "Zhuochen Fan and Bowen Ye and Ziwei Wang and Zheng
Zhong and Jiarui Guo and Yuhan Wu and Haoyu Li and Tong
Yang and Yaofeng Tu and Zirui Liu and Bin Cui",
title = "Enabling space-time efficient range queries with
{REncoder}",
journal = j-VLDB-J,
volume = "33",
number = "6",
pages = "1837--1859",
month = nov,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00873-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Oct 22 11:15:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00873-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{He:2024:DCV,
author = "Yizhang He and Kai Wang and Wenjie Zhang and Xuemin
Lin and Ying Zhang",
title = "Discovering critical vertices for reinforcement of
large-scale bipartite networks",
journal = j-VLDB-J,
volume = "33",
number = "6",
pages = "1861--1886",
month = nov,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00871-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Oct 22 11:15:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00871-y",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Wang:2024:DDA,
author = "Zeyu Wang and Qitong Wang and Peng Wang and Themis
Palpanas and Wei Wang",
title = "{DumpyOS}: A data-adaptive multi-ary index for
scalable data series similarity search",
journal = j-VLDB-J,
volume = "33",
number = "6",
pages = "1887--1911",
month = nov,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00874-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Oct 22 11:15:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00874-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Li:2024:VFA,
author = "Yiran Li and Gongyao Guo and Jieming Shi and Renchi
Yang and Shiqi Shen and Qing Li and Jun Luo",
title = "A versatile framework for attributed network
clustering via {$K$}-nearest neighbor augmentation",
journal = j-VLDB-J,
volume = "33",
number = "6",
pages = "1913--1943",
month = nov,
year = "2024",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00875-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Oct 22 11:15:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00875-8",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Ahmed:2025:RTA,
author = "Waqas Ahmed and Leticia G{\'o}mez and Alejandro
Vaisman and Esteban Zim{\'a}nyi",
title = "Reconciling tuple and attribute timestamping for
temporal data warehouses",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00889-2",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00889-2",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "11",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Arroyuelo:2025:ERP,
author = "Diego Arroyuelo and Adri{\'a}n G{\'o}mez-Brand{\'o}n
and Gonzalo Navarro",
title = "Evaluating regular path queries on compressed
adjacency matrices",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00885-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00885-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "2",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Georgiadis:2025:RIO,
author = "Thanasis Georgiadis and Eleni Tzirita Zacharatou and
Nikos Mamoulis",
title = "Raster interval object approximations for spatial
intersection joins",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00887-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00887-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "8",
fjournal = "VLDB Journal: Very Large Data Bases",
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}
@Article{Guo:2025:DQO,
author = "Yunyan Guo and Guoliang Li and Ruilin Hu and Yong
Wang",
title = "In-database query optimization on {SQL} with {ML}
predicates",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00888-3",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00888-3",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "12",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Hu:2025:GPM,
author = "Lin Hu and Yinnian Lin and Lei Zou and M. Tamer
{\"O}zsu",
title = "A graph pattern mining framework for large graphs on
{GPU}",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00883-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00883-8",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "6",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Hu:2025:PRF,
author = "Zheng Hu and Cong Xu and Weiguo Zheng",
title = "A powerful reducing framework for accelerating set
intersections over graphs",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00881-w",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00881-w",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "3",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Liu:2025:EOR,
author = "Hao Liu and Raymond Chi-Wing Wong",
title = "On efficient {3D} object retrieval",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00884-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00884-7",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "4",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Mann:2025:STK,
author = "Willi Mann and Nikolaus Augsten and Christian S.
Jensen and Mateusz Pawlik",
title = "{SWOOP}: top-$k$ similarity joins over set streams",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00880-x",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00880-x",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "13",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Messaoud:2025:TBK,
author = "Aghiles Ait Messaoud and Sonia {Ben Mokhtar} and
Anthony Simonet-Boulogne",
title = "Tee-based key-value stores: a survey",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00877-6",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00877-6",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "10",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Mondal:2025:SME,
author = "Manuel Mondal and Mourad Khayati and H{\^o}ng-{\^A}n
Sandlin and Philippe Cudr{\'e}-Mauroux",
title = "A survey of multimodal event detection based on data
fusion",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00878-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00878-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "9",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Pan:2025:AMB,
author = "Dong Pan and Xu Zhou and Wensheng Luo and Zhibang Yang
and Qing Liu and Yunjun Gao and Kenli Li",
title = "Accelerating maximum biplex search over large
bipartite graphs",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00882-9",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00882-9",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "1",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Shin:2025:UIL,
author = "Jaewoo Shin and Libin Zhou and Jianguo Wang and Walid
G. Aref",
title = "An update-intensive {LSM}-based {R}-tree index",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00876-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00876-7",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "7",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Song:2025:ETS,
author = "Yitong Song and Bin Yao and Zhida Chen and Xin Yang
and Jiong Xie and Feifei Li and Mengshi Chen",
title = "Efficient top-$k$ spatial-range-constrained
approximate nearest neighbor search on geo-tagged
high-dimensional vectors",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00894-5",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00894-5",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "14",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Zeakis:2025:DAP,
author = "Alexandros Zeakis and George Papadakis and Dimitrios
Skoutas and Manolis Koubarakis",
title = "An in-depth analysis of pre-trained embeddings for
entity resolution",
journal = j-VLDB-J,
volume = "34",
number = "1",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-024-00879-4",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat Jan 4 08:20:23 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "https://link.springer.com/article/10.1007/s00778-024-00879-4",
acknowledgement = ack-nhfb,
ajournal = "VLDB J.",
articleno = "5",
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
