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%%% -*-BibTeX-*-
%%% ====================================================================
%%%  BibTeX-file{
%%%     author          = "Nelson H. F. Beebe",
%%%     version         = "1.01",
%%%     date            = "14 October 2017",
%%%     time            = "10:27:20 MDT",
%%%     filename        = "tsas.bib",
%%%     address         = "University of Utah
%%%                        Department of Mathematics, 110 LCB
%%%                        155 S 1400 E RM 233
%%%                        Salt Lake City, UT 84112-0090
%%%                        USA",
%%%     telephone       = "+1 801 581 5254",
%%%     FAX             = "+1 801 581 4148",
%%%     URL             = "http://www.math.utah.edu/~beebe",
%%%     checksum        = "25951 1520 8726 80832",
%%%     email           = "beebe at math.utah.edu, beebe at acm.org,
%%%                        beebe at computer.org (Internet)",
%%%     codetable       = "ISO/ASCII",
%%%     keywords        = "ACM Transactions on Spatial Algorithms and
%%%                        Systems (TSAS); bibliography; BibTeX",
%%%     license         = "public domain",
%%%     supported       = "yes",
%%%     docstring       = "This is a COMPLETE BibTeX bibliography for
%%%                        ACM Transactions on Spatial Algorithms and
%%%                        Systems (TSAS) (CODEN ????, ISSN 2374-0353
%%%                        (print), 2374-0361 (electronic)).  The
%%%                        journal appears quarterly, and publication
%%%                        began with volume 1, number 1, in August
%%%                        2015.
%%%
%%%                        At version 1.01, the COMPLETE journal
%%%                        coverage looked like this:
%%%
%%%                             2015 (   8)    2016 (  16)    2017 (   3)
%%%
%%%                             Article:         27
%%%
%%%                             Total entries:   27
%%%
%%%                        The journal Web pages can be found at:
%%%
%%%                            http://tsas.acm.org/
%%%                            http://tsas.acm.org/archive-toc.cfm
%%%
%%%                        The journal table of contents page is at:
%%%
%%%                            http://dl.acm.org/pub.cfm?id=J1514
%%%
%%%                        Qualified subscribers can retrieve the full
%%%                        text of recent articles in PDF form.
%%%
%%%                        The initial draft was extracted from the ACM
%%%                        Web pages.
%%%
%%%                        ACM copyrights explicitly permit abstracting
%%%                        with credit, so article abstracts, keywords,
%%%                        and subject classifications have been
%%%                        included in this bibliography wherever
%%%                        available.  Article reviews have been
%%%                        omitted, until their copyright status has
%%%                        been clarified.
%%%
%%%                        bibsource keys in the bibliography entries
%%%                        below indicate the entry originally came
%%%                        from the computer science bibliography
%%%                        archive, even though it has likely since
%%%                        been corrected and updated.
%%%
%%%                        URL keys in the bibliography point to
%%%                        World Wide Web locations of additional
%%%                        information about the entry.
%%%
%%%                        BibTeX citation tags are uniformly chosen
%%%                        as name:year:abbrev, where name is the
%%%                        family name of the first author or editor,
%%%                        year is a 4-digit number, and abbrev is a
%%%                        3-letter condensation of important title
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%%%
%%%                        In this bibliography, entries are sorted in
%%%                        publication order, using ``bibsort -byvolume.''
%%%
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%%% ====================================================================
@Preamble{"\input bibnames.sty" #
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%%% ====================================================================
%%% Acknowledgement abbreviations:
@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,
                    FAX: +1 801 581 4148,
                    e-mail: \path|beebe@math.utah.edu|,
                            \path|beebe@acm.org|,
                            \path|beebe@computer.org| (Internet),
                    URL: \path|http://www.math.utah.edu/~beebe/|"}

%%% ====================================================================
%%% Journal abbreviations:
@String{j-TSAS                  = "ACM Transactions on Spatial Algorithms and
                                  Systems (TSAS)"}

%%% ====================================================================
%%% Bibliography entries:
@Article{Gemsa:2015:MBL,
  author =       "Andreas Gemsa and Jan-Henrik Haunert and Martin
                 N{\"o}llenburg",
  title =        "Multirow Boundary-Labeling Algorithms for Panorama
                 Images",
  journal =      j-TSAS,
  volume =       "1",
  number =       "1",
  pages =        "1:1--1:30",
  month =        aug,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2794299",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:00 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2794299",
  abstract =     "Boundary labeling deals with placing annotations for
                 objects in an image on the boundary of that image. This
                 problem occurs frequently in situations in which
                 placing labels directly in the image is impossible or
                 produces too much visual clutter. Examples are
                 annotating maps, photos, or technical/medical
                 illustrations. Previous algorithmic results for
                 boundary labeling consider a single layer of labels
                 along some or all sides of a rectangular image. If,
                 however, the number of labels is large or the labels
                 are too long, multiple layers of labels are needed. In
                 this article, we study boundary labeling for panorama
                 images, where n points in a rectangle R are to be
                 annotated by disjoint unit-height rectangular labels
                 placed above R in K different rows (or layers). Each
                 point is connected to its label by a vertical leader
                 that does not intersect any other label. We present
                 polynomial time algorithms based on dynamic programming
                 that either minimize the number of rows to place all n
                 labels or maximize the number (or total weight) of
                 labels that can be placed in K rows for a given integer
                 K. For weighted labels, the problem is shown to be
                 (weakly) NP-hard; in this case, we give a
                 pseudo-polynomial algorithm to maximize the weight of
                 the selected labels. We have implemented our
                 algorithms; the experimental results show that
                 solutions for realistically sized instances are
                 computed instantaneously. We have also investigated
                 two-sided panorama labeling, for which the labels may
                 be placed above or below the panorama image. In this
                 model, all of the aforementioned problems are NP-hard.
                 For solving them, we propose mixed-integer linear
                 program formulations.",
  acknowledgement = ack-nhfb,
  articleno =    "1",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{To:2015:SAS,
  author =       "Hien To and Cyrus Shahabi and Leyla Kazemi",
  title =        "A Server-Assigned Spatial Crowdsourcing Framework",
  journal =      j-TSAS,
  volume =       "1",
  number =       "1",
  pages =        "2:1--2:28",
  month =        aug,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2729713",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:00 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2729713",
  abstract =     "With the popularity of mobile devices, spatial
                 crowdsourcing is rising as a new framework that enables
                 human workers to solve tasks in the physical world.
                 With spatial crowdsourcing, the goal is to crowdsource
                 a set of spatiotemporal tasks (i.e., tasks related to
                 time and location) to a set of workers, which requires
                 the workers to physically travel to those locations in
                 order to perform the tasks. In this article, we focus
                 on one class of spatial crowdsourcing, in which the
                 workers send their locations to the server and
                 thereafter the server assigns to every worker tasks in
                 proximity to the worker's location with the aim of
                 maximizing the overall number of assigned tasks. We
                 formally define this maximum task assignment (MTA)
                 problem in spatial crowdsourcing, and identify its
                 challenges. We propose alternative solutions to address
                 these challenges by exploiting the spatial properties
                 of the problem space, including the spatial
                 distribution and the travel cost of the workers. MTA is
                 based on the assumptions that all tasks are of the same
                 type and all workers are equally qualified in
                 performing the tasks. Meanwhile, different types of
                 tasks may require workers with various skill sets or
                 expertise. Subsequently, we extend MTA by taking the
                 expertise of the workers into consideration. We refer
                 to this problem as the maximum score assignment (MSA)
                 problem and show its practicality and generality.
                 Extensive experiments with various synthetic and two
                 real-world datasets show the applicability of our
                 proposed framework.",
  acknowledgement = ack-nhfb,
  articleno =    "2",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Ahmed:2015:PBD,
  author =       "Mahmuda Ahmed and Brittany Terese Fasy and Kyle S.
                 Hickmann and Carola Wenk",
  title =        "A Path-Based Distance for Street Map Comparison",
  journal =      j-TSAS,
  volume =       "1",
  number =       "1",
  pages =        "3:1--3:28",
  month =        aug,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2729977",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:00 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2729977",
  abstract =     "Comparing two geometric graphs embedded in space is
                 important in the field of transportation network
                 analysis. Given street maps of the same city collected
                 from different sources, researchers often need to know
                 how and where they differ. However, the majority of
                 current graph comparison algorithms are based on
                 structural properties of graphs, such as their degree
                 distribution or their local connectivity properties,
                 and do not consider their spatial embedding. This
                 ignores a key property of road networks since the
                 similarity of travel over two road networks is
                 intimately tied to the specific spatial embedding.
                 Likewise, many current algorithms specific to street
                 map comparison either do not provide quality guarantees
                 or focus on spatial embeddings only. Motivated by road
                 network comparison, we propose a new path-based
                 distance measure between two planar geometric graphs
                 that is based on comparing sets of travel paths
                 generated over the graphs. Surprisingly, we are able to
                 show that using paths of bounded link-length, we can
                 capture global structural and spatial differences
                 between the graphs. We show how to utilize our distance
                 measure as a local signature in order to identify and
                 visualize portions of high similarity in the maps.
                 Finally, we present an experimental evaluation of our
                 distance measure and its local signature on street map
                 data from Berlin, Germany and Athens, Greece.",
  acknowledgement = ack-nhfb,
  articleno =    "3",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Mckennney:2015:GMR,
  author =       "Mark Mckennney and Roger Frye",
  title =        "Generating Moving Regions from Snapshots of Complex
                 Regions",
  journal =      j-TSAS,
  volume =       "1",
  number =       "1",
  pages =        "4:1--4:30",
  month =        aug,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2774220",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:00 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2774220",
  abstract =     "Moving regions are a form of spatiotemporal data in
                 which a region changes in shape and/or position over
                 time. In many fields, moving regions representing
                 real-world phenomena are collected using sensors that
                 take temporally encoded snapshots of regions. We
                 provide a novel algorithm that creates a moving region
                 between any two complex regions. The proposed algorithm
                 has worst-case time bounds of O; ( n; 2 ), but can use
                 approximation techniques to achieve O ( n lg n ) in
                 practice, space bounds of O; ( n; ), and output size
                 bounded by O; ( n; ) (where n; is the number of line
                 segments that define the boundaries of the regions).",
  acknowledgement = ack-nhfb,
  articleno =    "4",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Tang:2015:EGF,
  author =       "Suhua Tang and Yi Yu and Roger Zimmermann and Sadao
                 Obana",
  title =        "Efficient Geo-Fencing via Hybrid Hashing: A
                 Combination of Bucket Selection and In-Bucket Binary
                 Search",
  journal =      j-TSAS,
  volume =       "1",
  number =       "2",
  pages =        "5:1--5:22",
  month =        nov,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2774219",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:01 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/hash.bib;
                 http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2774219",
  abstract =     "Geo-fencing, as a spatial join between points (moving
                 objects) and polygons (spatial range), is widely used
                 in emerging location-based services to trigger
                 context-aware events. It faces the challenge of
                 real-time processing a large number of time-variant
                 complex polygons, when points are constantly moving.
                 Following the filter-and-refine policy, in our previous
                 work, we proposed to organize edges per polygon in hash
                 tables to improve the performance of the refining
                 stage. The number of edges, however, is uneven among
                 buckets. As a result, some points that happen to match
                 big buckets with many edges will have much longer
                 responses than usual. In this article, we solve this
                 problem from two aspects: (i) Constructing multiple
                 parallel hash tables and dynamically selecting the
                 bucket with fewest edges and (ii) sorting edges in a
                 bucket so as to realize the crossing number algorithm
                 by binary search. We further combine the two to suggest
                 a hybrid hashing scheme that takes a better tradeoff
                 between real-time pairing points with polygons and
                 system overhead of building hash tables. Extensive
                 analyses and evaluations on two real-world datasets
                 confirm that the proposed scheme can effectively reduce
                 the pairing time in terms of both the average and
                 distribution.",
  acknowledgement = ack-nhfb,
  articleno =    "5",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{That:2015:TGT,
  author =       "Dai Hai Ton That and Iulian Sandu Popa and Karine
                 Zeitouni",
  title =        "{TRIFL}: A Generic Trajectory Index for Flash
                 Storage",
  journal =      j-TSAS,
  volume =       "1",
  number =       "2",
  pages =        "6:1--6:44",
  month =        nov,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2786758",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:01 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2786758",
  abstract =     "Due to several important features, such as high
                 performance, low power consumption, and shock
                 resistance, NAND flash has become a very popular stable
                 storage medium for embedded mobile devices, personal
                 computers, and even enterprise servers. However, the
                 peculiar characteristics of flash memory require
                 redesigning the existing data storage and indexing
                 techniques that were devised for magnetic hard disks.
                 In this article, we propose TRIFL, an efficient and
                 generic TRajectory Index for FLash. TRIFL is designed
                 around the key requirements of trajectory indexing and
                 flash storage. TRIFL is generic in the sense that it is
                 efficient for both simple flash storage devices such as
                 SD cards and more powerful devices such as solid state
                 drives. In addition, TRIFL is supplied with an online
                 self-tuning algorithm that allows adapting the index
                 structure to the workload and the technical
                 specifications of the flash storage device to maximize
                 the index performance. Moreover, TRIFL achieves good
                 performance with relatively low memory requirements,
                 which makes the index appropriate for many application
                 scenarios. The experimental evaluation shows that TRIFL
                 outperforms the representative indexing methods on
                 magnetic disks and flash disks.",
  acknowledgement = ack-nhfb,
  articleno =    "6",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Guting:2015:ST,
  author =       "Ralf Hartmut G{\"u}ting and Fabio Vald{\'e}s and Maria
                 Luisa Damiani",
  title =        "Symbolic Trajectories",
  journal =      j-TSAS,
  volume =       "1",
  number =       "2",
  pages =        "7:1--7:51",
  month =        nov,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2786756",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:01 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2786756",
  abstract =     "Due to the proliferation of GPS-enabled devices in
                 vehicles or with people, large amounts of position data
                 are recorded every day and the management of such
                 mobility data, also called trajectories, is a very
                 active research field. A lot of effort has gone into
                 discovering ``semantics'' from the raw geometric
                 trajectories by relating them to the spatial
                 environment or finding patterns, for example, by data
                 mining techniques. A question is how the resulting
                 ``meaningful'' trajectories can be represented or
                 further queried. In this article, we propose a
                 systematic study of annotated trajectory databases. We
                 define a very simple generic model called symbolic
                 trajectory to capture a wide range of meanings derived
                 from a geometric trajectory. Essentially, a symbolic
                 trajectory is just a time-dependent label; variants
                 have sets of labels, places, or sets of places. They
                 are modeled as abstract data types and integrated into
                 a well-established framework of data types and
                 operations for moving objects. Symbolic trajectories
                 can represent, for example, the names of roads
                 traversed obtained by map matching, transportation
                 modes, speed profile, cells of a cellular network,
                 behaviors of animals, cinemas within 2km distance, and
                 so forth. Symbolic trajectories can be combined with
                 geometric trajectories to obtain annotated
                 trajectories. Besides the model, the main technical
                 contribution of the article is a language for pattern
                 matching and rewriting of symbolic trajectories. A
                 symbolic trajectory can be represented as a sequence of
                 pairs (called units) consisting of a time interval and
                 a label. A pattern consists of unit patterns
                 (specifications for time interval and/or label) and
                 wildcards, matching units and sequences of units,
                 respectively, and regular expressions over such
                 elements. It may further contain variables that can be
                 used in conditions and in rewriting. Conditions and
                 expressions in rewriting may use arbitrary operations
                 available for querying in the host DBMS environment,
                 which makes the language extensible and quite powerful.
                 We formally define the data model and syntax and
                 semantics of the pattern language. Query operations are
                 offered to integrate pattern matching, rewriting, and
                 classification of symbolic trajectories into a DBMS
                 querying environment. Implementation of the model using
                 finite state machines is described in detail. An
                 experimental evaluation demonstrates the efficiency of
                 the implementation. In particular, it shows dramatic
                 improvements in storage space and response time in a
                 comparison of symbolic and geometric trajectories for
                 some simple queries that can be executed on both
                 symbolic and raw trajectories.",
  acknowledgement = ack-nhfb,
  articleno =    "7",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Kovanen:2015:TAC,
  author =       "Janne Kovanen and Tapani Sarjakoski",
  title =        "Tilewise Accumulated Cost Surface Computation with
                 Graphics Processing Units",
  journal =      j-TSAS,
  volume =       "1",
  number =       "2",
  pages =        "8:1--8:27",
  month =        nov,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2803172",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:01 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pvm.bib;
                 http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2803172",
  abstract =     "Accumulated cost surfaces are used in a variety of
                 fields that employ spatial analysis. Several algorithms
                 have been suggested in the past for solving them
                 efficiently or with minimal errors. Meanwhile, a new
                 wave on the technological frontier has brought about
                 general-purpose computing on GPUs. In this article, we
                 describe how accumulated cost surfaces can be solved
                 with CUDA. To verify the performance of our solution,
                 we performed an experimental comparison against
                 implementations run on a CPU. Our results with
                 realistic cost models indicate that the move to GPUs
                 can engender a speed-up of an order of magnitude.",
  acknowledgement = ack-nhfb,
  articleno =    "8",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Belussi:2016:SRR,
  author =       "Alberto Belussi and Sara Migliorini and Mauro Negri
                 and Giuseppe Pelagatti",
  title =        "Snap Rounding with Restore: An Algorithm for Producing
                 Robust Geometric Datasets",
  journal =      j-TSAS,
  volume =       "2",
  number =       "1",
  pages =        "1:1--1:36",
  month =        apr,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2811256",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:01 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2811256",
  abstract =     "This article presents a new algorithm called Snap
                 Rounding with Restore (SRR), which aims to make
                 geometric datasets robust and to increase the quality
                 of geometric approximation and the preservation of
                 topological structure. It is based on the well-known
                 Snap Rounding algorithm but improves it by eliminating
                 from the snap rounded arrangement the configurations in
                 which the distance between a vertex and a nonincident
                 edge is smaller than half the width of a pixel of the
                 rounding grid. Therefore, the goal of SRR is exactly
                 the same as the goal of another algorithm, Iterated
                 Snap Rounding (ISR), and of its evolution, Iterated
                 Snap Rounding with Bounded Drift (ISRBD). However, SRR
                 produces an output with a quality of approximation that
                 is on average better than ISRBD, under the viewpoint
                 both of the distance from the original segments and of
                 the conservation of their topological structure. The
                 article also reports some cases where ISRBD,
                 notwithstanding the bounded drift, produces strong
                 topological modifications while SRR does not. A
                 statistical analysis on a large collection of input
                 datasets confirms these differences. It follows that
                 the proposed Snap Rounding with Restore algorithm is
                 suitable for applications that require robustness, a
                 guaranteed geometric approximation, and a good
                 topological approximation.",
  acknowledgement = ack-nhfb,
  articleno =    "1",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Buchin:2016:APS,
  author =       "Kevin Buchin and Wouter Meulemans and Andr{\'e} {Van
                 Renssen} and Bettina Speckmann",
  title =        "Area-Preserving Simplification and Schematization of
                 Polygonal Subdivisions",
  journal =      j-TSAS,
  volume =       "2",
  number =       "1",
  pages =        "2:1--2:36",
  month =        apr,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2818373",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:01 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2818373",
  abstract =     "In this article, we study automated simplification and
                 schematization of territorial outlines. We present a
                 quadratic-time simplification algorithm based on an
                 operation called edge-move. We prove that the number of
                 edges of any nonconvex simple polygon can be reduced
                 with this operation. Moreover, edge-moves preserve area
                 and topology and do not introduce new orientations. The
                 latter property in particular makes the algorithm
                 highly suitable for schematization in which all
                 resulting lines are required to be parallel to one of a
                 given set of lines (orientations). To obtain such a
                 result, we need only to preprocess the input to use
                 only lines that are parallel to one of the given set.
                 We present an algorithm to enforce such orientation
                 restrictions, again without changing area or topology.
                 Experiments show that our algorithms obtain results of
                 high visual quality.",
  acknowledgement = ack-nhfb,
  articleno =    "2",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Ali:2016:SCQ,
  author =       "Mohammed Eunus Ali and Egemen Tanin and Peter
                 Scheuermann and Sarana Nutanong and Lars Kulik",
  title =        "Spatial Consensus Queries in a Collaborative
                 Environment",
  journal =      j-TSAS,
  volume =       "2",
  number =       "1",
  pages =        "3:1--3:37",
  month =        apr,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2829943",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:01 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2829943",
  abstract =     "We introduce a new type of query for a location-based
                 social network platform. Consider a scenario in which a
                 group of users is trying to find a common meeting
                 location, yet attempting to include all group members
                 is introducing a significant traveling cost to most of
                 them. In this article, we formulate a new query type
                 called the consensus query, which can be used to help
                 users explore these trade-off options to find a
                 solution upon which everyone can agree. Specifically,
                 we study the problem of evaluating consensus queries in
                 the context of nearest neighbor queries, where the
                 group is interested in finding a meeting place that
                 minimizes the travel distance for at least a specified
                 number of group members. To help the group in selecting
                 a suitable solution, the major challenge is to find
                 optimal subgroups of all allowable subgroup sizes,
                 i.e., greater or equal to the minimum specified
                 subgroup size, that minimize the travel distances. We
                 develop incremental algorithms to evaluate in one pass
                 the optimal query subgroups of different sizes along
                 with their corresponding nearest data points. These
                 subsets, which are evaluated by the location-based
                 service provider, constitute the answer set that is
                 returned to the group. The group then collaboratively
                 selects the final answer from the candidate answer set.
                 An extensive experimental study shows the efficiency
                 and effectiveness of our proposed techniques.",
  acknowledgement = ack-nhfb,
  articleno =    "3",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Goel:2016:PAD,
  author =       "Preeti Goel and Lars Kulik and Kotagiri
                 Ramamohanarao",
  title =        "Privacy-Aware Dynamic Ride Sharing",
  journal =      j-TSAS,
  volume =       "2",
  number =       "1",
  pages =        "4:1--4:41",
  month =        apr,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2845080",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:01 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2845080",
  abstract =     "Dynamic ride sharing is a service that enables shared
                 vehicle rides in real time and on short notice. It can
                 be an effective solution to counter the problem of
                 increasing traffic jams at peak hours in cities. The
                 growing use and popularity of smart phones and
                 GPS-enabled devices provides us with tools required to
                 efficiently implement ride sharing and significantly
                 enhance carpooling. However, privacy and safety
                 concerns are the main obstacles faced when encouraging
                 people to use such a service. In this work, we present
                 ``Match Maker,'' a negotiation-based model that hides
                 exact location information data for system participants
                 while implementing privacy preserving ride sharing. We
                 use the concept of imprecision (not being precise about
                 location of the user out of set of n locations) and
                 follow the idea of obfuscation, which equates a higher
                 degree of imprecision with a higher degree of privacy.
                 We identify two attack types that could circumvent
                 privacy preserving ride sharing. We compare the Match
                 Maker model with the standard central trusted server
                 model collecting precise location data, which we term
                 eBay model. We provide the first comprehensive approach
                 that integrates privacy, safety and trust in a single
                 model. We present a recursive ellipse-based algorithm
                 to compute an optimal driver path as well as three
                 negotiation strategies for drivers and passengers. We
                 conduct extensive experiments on real road networks and
                 compare the strategies for privacy and effectiveness of
                 ride sharing in terms of traffic load and vehicle km
                 reduction. We show that ride sharing saves between 9\%
                 and 21\% (on average 12\%) of vehicle km if drivers are
                 only prepared to accept slight detours of their usual
                 trips. In the city of Melbourne, with 11.6 million
                 trips a weekday and an average trip length of 10.2 km,
                 this would save 14.2 million km per weekday.",
  acknowledgement = ack-nhfb,
  articleno =    "4",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Skoumas:2016:LEU,
  author =       "Georgios Skoumas and Dieter Pfoser and Anastasios
                 Kyrillidis and Timos Sellis",
  title =        "Location Estimation Using Crowdsourced Spatial
                 Relations",
  journal =      j-TSAS,
  volume =       "2",
  number =       "2",
  pages =        "5:1--5:23",
  month =        jul,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2894745",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 15:01:39 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2894745",
  abstract =     "The ``crowd'' has become a very important geospatial
                 data provider. Specifically, nonexpert users have been
                 providing a wealth of quantitative geospatial data
                 (e.g., geotagged tweets or photos, online). With
                 spatial reasoning being a basic form of human
                 cognition, textual narratives expressing user travel
                 experiences (e.g., travel blogs) would provide an even
                 bigger source of geospatial data. Narratives typically
                 contain qualitative geospatial data in the form of
                 objects and spatial relations (e.g., ``St. John's
                 church is to the North of the Acropolis museum.''). The
                 scope of this work is (i) to extract these spatial
                 relations from textual narratives, (ii) to quantify
                 (model) them, and (iii) to reason about object
                 locations based only on the quantified spatial
                 relations. We use information extraction methods to
                 identify toponyms and spatial relations, and we
                 formulate a quantitative approach based on distance and
                 orientation features to represent the latter.
                 Probability density functions (PDFs) for spatial
                 relations are determined by means of a greedy
                 expectation maximization (EM)-based algorithm. These
                 PDFs are then used to estimate unknown object
                 locations. Experiments using a text corpus harvested
                 from travel blog sites establish the considerable
                 location estimation accuracy of the proposed approach
                 on synthetic and real-world scenarios.",
  acknowledgement = ack-nhfb,
  articleno =    "5",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Ferreira:2016:EEM,
  author =       "Chaulio R. Ferreira and Marcus V. A. Andrade and
                 Salles V. G. Magalh{\~a}es and W. Randolph Franklin",
  title =        "An Efficient External Memory Algorithm for Terrain
                 Viewshed Computation",
  journal =      j-TSAS,
  volume =       "2",
  number =       "2",
  pages =        "6:1--6:17",
  month =        jul,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2903206",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 15:01:39 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2903206",
  abstract =     "This article presents TiledVS, a fast external
                 algorithm and implementation for computing viewsheds.
                 TiledVS is intended for terrains that are too large for
                 internal memory, even more than 100,000 $ \times $
                 100,000 points. It subdivides the terrain into tiles
                 that are stored compressed on disk and then paged into
                 memory with a custom cache data structure and least
                 recently used algorithm. If there is sufficient
                 available memory to store a whole row of tiles, which
                 is easy, then this specialized data management is
                 faster than relying on the operating system's virtual
                 memory management. Applications of viewshed computation
                 include siting radio transmitters, surveillance, and
                 visual environmental impact measurement. TiledVS runs a
                 rotating line of sight from the observer to points on
                 the region boundary. For each boundary point, it
                 computes the visibility of all terrain points close to
                 the line of sight. The running time is linear in the
                 number of points. No terrain tile is read more than
                 twice. TiledVS is very fast, for instance, processing a
                 104,000 $ \times $ 104,000 terrain on a modest computer
                 with only 512MB of RAM took only $ 1.5 $ hours. On
                 large datasets, TiledVS was several times faster than
                 competing algorithms, such as the ones included in
                 GRASS. The source code of TiledVS is freely available
                 for nonprofit researchers to study, use, and extend. A
                 preliminary version of this algorithm appeared in a
                 four-page ACM SIGSPATIAL GIS 2012 conference paper,
                 ``More Efficient Terrain Viewshed Computation on
                 Massive Datasets Using External Memory.'' This more
                 detailed version adds the fast lossless compression
                 stage that reduces the time by 30\% to 40\%, and many
                 more experiments and comparisons.",
  acknowledgement = ack-nhfb,
  articleno =    "6",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Agarwal:2016:TNN,
  author =       "Pankaj K. Agarwal and Alex Beutel and Thomas
                 M{\o}lhave",
  title =        "{TerraNNI}: Natural Neighbor Interpolation on {$2$D}
                 and {$3$D} Grids Using a {GPU}",
  journal =      j-TSAS,
  volume =       "2",
  number =       "2",
  pages =        "7:1--7:31",
  month =        jul,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2786757",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 15:01:39 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2786757",
  abstract =     "With modern focus on remote sensing technology, such
                 as LiDAR, the amount of spatial data, in the form of
                 massive point clouds, has increased dramatically.
                 Furthermore, repeated surveys of the same areas are
                 becoming more common. This trend will only increase as
                 topographic changes prompt surveys over already scanned
                 areas, in which case we obtain large spatiotemporal
                 datasets. An initial step in the analysis of such
                 spatial data is to create a digital elevation model
                 representing the terrain, possibly over time. In the
                 case of spatial (spatiotemporal, respectively)
                 datasets, these models often represent elevation on a
                 2D (3D, respectively) grid. This involves interpolating
                 the elevation of LiDAR points on these grid points. In
                 this article, we show how to efficiently perform
                 natural neighbor interpolation over a 2D and 3D grid.
                 Using a graphics processing unit (GPU), we describe
                 different algorithms to attain speed and GPU-memory
                 tradeoffs. Our experimental results demonstrate that
                 our algorithms not only are significantly faster than
                 earlier ones but also scale to much bigger datasets
                 that previous algorithms were unable to handle.",
  acknowledgement = ack-nhfb,
  articleno =    "7",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Ghinita:2016:PAV,
  author =       "Gabriel Ghinita and Maria Luisa Damiani and Claudio
                 Silvestri and Elisa Bertino",
  title =        "Protecting Against Velocity-Based, Proximity-Based,
                 and External Event Attacks in Location-Centric Social
                 Networks",
  journal =      j-TSAS,
  volume =       "2",
  number =       "2",
  pages =        "8:1--8:36",
  month =        jul,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2910580",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 15:01:39 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2910580",
  abstract =     "Mobile devices with positioning capabilities allow
                 users to participate in novel and exciting
                 location-based applications. For instance, users may
                 track the whereabouts of their acquaintances in
                 location-aware social networking applications (e.g.,
                 Foursquare). Furthermore, users can request information
                 about landmarks in their proximity. Such scenarios
                 require users to report their coordinates to other
                 parties, which may not be fully trusted. Reporting
                 precise locations may result in serious privacy
                 violations, such as disclosure of lifestyle details,
                 sexual orientation, and so forth. A typical approach to
                 preserve location privacy is to generate a cloaking
                 region (CR) that encloses the user position. However,
                 if locations are continuously reported, an attacker can
                 correlate CRs from multiple timestamps to accurately
                 pinpoint the user position within a CR. In this work,
                 we protect against a broad range of attacks that breach
                 location privacy using knowledge about (1) maximum user
                 velocity, (2) external events that may occur outside
                 the process of self-reporting locations (e.g., social
                 network posts tagged by peers), and (3) information
                 about mutual proximity between users. Assume user u who
                 reports two consecutive cloaked regions A and B. We
                 consider two distinct protection scenarios: in the
                 first case, the attacker does not have information
                 about the sensitive locations on the map, and the
                 objective is to ensure that u can reach some point in B
                 from any point in A; in the second case, the attacker
                 knows the placement of sensitive locations, and the
                 objective is to ensure that u can reach any point in B
                 from any point in A. We propose spatial and temporal
                 cloaking transformations to preserve user privacy, and
                 we show experimentally that privacy can be achieved
                 without significant quality-of-service deterioration.",
  acknowledgement = ack-nhfb,
  articleno =    "8",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Efstathiades:2016:EPR,
  author =       "Christodoulos Efstathiades and Alexandros Efentakis
                 and Dieter Pfoser",
  title =        "Efficient Processing of Relevant Nearest-Neighbor
                 Queries",
  journal =      j-TSAS,
  volume =       "2",
  number =       "3",
  pages =        "9:1--9:28",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2934675",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:02 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2934675",
  abstract =     "Novel Web technologies and resulting applications have
                 led to a participatory data ecosystem that, when
                 utilized properly, will lead to more rewarding
                 services. In this work, we investigate the case of
                 Location-Based Services, specifically how to improve
                 the typical location-based Point-of-Interest (POI)
                 request processed as a k -Nearest-Neighbor query. This
                 work introduces Links-of-Interest (LOI) between POIs as
                 a means to increase the relevance and overall result
                 quality of such queries. By analyzing user-contributed
                 content in the form of travel blogs, we establish the
                 overall popularity of an LOI, that is, how frequently
                 the respective POI pair was visited and is mentioned in
                 the same context. Our contribution is a
                 query-processing method for so-called k -Relevant
                 Nearest Neighbor ( k -RNN) queries that considers
                 spatial proximity in combination with LOI information
                 to retrieve close-by and relevant (as judged by the
                 crowd) POIs. Our method is based on intelligently
                 combining indices for spatial data (a spatial grid) and
                 for relevance data (a graph) during query processing.
                 Using landmarks as a means to prune the search space in
                 the Relevance Graph, we improve the proposed methods.
                 Using in addition A*-directed search, the query
                 performance can be further improved. An experimental
                 evaluation using real and synthetic data establishes
                 that our approach efficiently solves the k -RNN
                 problem.",
  acknowledgement = ack-nhfb,
  articleno =    "9",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Pillai:2016:MMT,
  author =       "Karthik Ganesan Pillai and Rafal A. Angryk and Juan M.
                 Banda and Dustin Kempton and Berkay Aydin and Petrus C.
                 Martens",
  title =        "Mining At Most Top-{$K$ \%} Spatiotemporal
                 Co-occurrence Patterns in Datasets with Extended
                 Spatial Representations",
  journal =      j-TSAS,
  volume =       "2",
  number =       "3",
  pages =        "10:1--10:27",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2936775",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:02 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2936775",
  abstract =     "Spatiotemporal co-occurrence patterns (STCOPs) in
                 datasets with extended spatial representations are two
                 or more different event types, represented as polygons
                 evolving in time, whose instances often occur together
                 in both space and time. Finding STCOPs is an important
                 problem in domains such as weather monitoring, wildlife
                 migration, and solar physics. Nevertheless, in real
                 life, it is difficult to find a suitable prevalence
                 threshold without prior domain-specific knowledge. In
                 this article, we focus our work on the problem of
                 mining at most top-K\% of STCOPs from continuously
                 evolving spatiotemporal events that have polygon-like
                 representations, without using a user-specified
                 prevalence threshold.",
  acknowledgement = ack-nhfb,
  articleno =    "10",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Pelekis:2016:SOL,
  author =       "Nikos Pelekis and Stylianos Sideridis and Panagiotis
                 Tampakis and Yannis Theodoridis",
  title =        "Simulating Our {LifeSteps} by Example",
  journal =      j-TSAS,
  volume =       "2",
  number =       "3",
  pages =        "11:1--11:39",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2937753",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:02 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2937753",
  abstract =     "During the past few decades, a number of effective
                 methods for indexing, query processing, and knowledge
                 discovery in moving object databases have been
                 proposed. An interesting research direction that has
                 recently emerged handles semantics of movement instead
                 of raw spatio-temporal data. Semantic annotations, such
                 as ``stop,'' ``move,'' ``at home,'' ``shopping,''
                 ``driving,'' and so on, are either declared by the
                 users (e.g., through social network apps) or
                 automatically inferred by some annotation method and
                 are typically presented as textual counterparts along
                 with spatial and temporal information of raw
                 trajectories. It is natural to argue that such
                 ``spatio-temporal-textual'' sequences, called semantic
                 trajectories, form a realistic representation model of
                 the complex everyday life (hence, mobility) of
                 individuals. Towards handling semantic trajectories of
                 moving objects in Semantic Mobility Databases, the lack
                 of real datasets leads to the need to design realistic
                 simulators. In the context of the above discussion, the
                 goal of this work is to realistically simulate the
                 mobility life of a large-scale population of moving
                 objects in an urban environment. Two simulator
                 variations are presented: the core Hermoupolis
                 simulator is parametric driven (i.e., user-defined
                 parameters tune every movement aspect), whereas the
                 expansion of the former, called Hermoupolis by-example,
                 follows the generate-by-example paradigm and is
                 self-tuned by looking inside a real small (sample)
                 dataset. We stress test our proposal and demonstrate
                 its novel characteristics with respect to related
                 work.",
  acknowledgement = ack-nhfb,
  articleno =    "11",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Hung:2016:SIA,
  author =       "Hui-Ju Hung and De-Nian Yang and Wang-Chien Lee",
  title =        "Social Influence-Aware Reverse Nearest Neighbor
                 Search",
  journal =      j-TSAS,
  volume =       "2",
  number =       "3",
  pages =        "12:1--12:35",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2964906",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:02 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2964906",
  abstract =     "Business-location planning, critical to the success of
                 many businesses, can be addressed by the reverse
                 nearest neighbors (RNN) query using geographical
                 proximity to the customers as the main metric to find a
                 store location close to many customers. Nevertheless,
                 we argue that other marketing factors, such as social
                 influence, could be considered in the process of
                 business-location planning. In this article, we propose
                 a framework for business-location planning that takes
                 into account both factors of geographical proximity and
                 social influence. An essential task in this framework
                 is to compute the ``influence spread'' of RNNs for
                 candidate locations. Here, the influence spread refers
                 to the number of people influenced via the
                 word-of-mouth effect. To alleviate the excessive
                 computational overhead and long latency in the
                 framework, we trade storage overhead for processing
                 speed by precomputing and storing the social influence
                 between pairs of customers. Based on Targeted Region
                 (TR)-Oriented and RNN-Oriented processing strategies,
                 we develop two suites of algorithms that incorporate
                 various efficient pruning and segmentation techniques
                 to enhance our framework. Experiments validate our
                 ideas and evaluate the efficiency of the proposed
                 algorithms over various parameter settings. The
                 experimental results show that (a) TR-oriented and
                 RNN-oriented processing are feasible for supporting the
                 task of location planning; (b) RNN-oriented processing
                 is more efficient than TR-oriented processing; and (c)
                 the optimization technique that we developed
                 significantly improves the efficiency of RNN-oriented
                 and TR-oriented processing.",
  acknowledgement = ack-nhfb,
  articleno =    "12",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Budig:2016:MLM,
  author =       "Benedikt Budig and Thomas C. {Van Dijk} and Alexander
                 Wolff",
  title =        "Matching Labels and Markers in Historical Maps: An
                 Algorithm with Interactive Postprocessing",
  journal =      j-TSAS,
  volume =       "2",
  number =       "4",
  pages =        "13:1--13:24",
  month =        nov,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2994598",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:03 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2994598",
  abstract =     "In this article, we present an algorithmic system for
                 determining the proper correspondence between place
                 markers and their labels in historical maps. We assume
                 that the locations of place markers (usually
                 pictographs) and labels (pieces of text) have already
                 been determined -- either algorithmically or by hand --
                 and we want to match the labels to the markers. This
                 time-consuming step in the digitization process of
                 historical maps is nontrivial even for humans but
                 provides valuable metadata (e.g., when subsequently
                 georeferencing the map). To speed up this process, we
                 model the problem in terms of combinatorial
                 optimization, solve that problem efficiently, and show
                 how user interaction can be used to improve the quality
                 of the results. We also consider a version of the model
                 where we are given label fragments and additionally
                 have to decide which fragments go together. We show
                 that this problem is NP-hard. However, we give a
                 polynomial-time algorithm for a restricted version of
                 this fragment assignment problem. We have implemented
                 the algorithm for the main problem and tested it on a
                 manually extracted ground truth for eight historical
                 maps with a combined total of more than 12,800 markers
                 and labels. On average, the algorithm correctly matches
                 96\% of the labels and is robust against noisy input.
                 It furthermore performs a sensitivity analysis and in
                 this way computes a measure of confidence for each of
                 the matches. We use this as the basis for an
                 interactive system where the user's effort is directed
                 to checking those parts of the map where the algorithm
                 is unsure; any corrections the user makes are
                 propagated by the algorithm. We discuss a prototype of
                 this system and statistically confirm that it
                 successfully locates those areas on the map where the
                 algorithm needs help.",
  acknowledgement = ack-nhfb,
  articleno =    "13",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Niu:2016:LED,
  author =       "Wei Niu and Zhijiao Liu and James Caverlee",
  title =        "On Local Expert Discovery via Geo-Located Crowds,
                 Queries, and Candidates",
  journal =      j-TSAS,
  volume =       "2",
  number =       "4",
  pages =        "14:1--14:24",
  month =        nov,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2994599",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:03 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2994599",
  abstract =     "Local experts are critical for many location-sensitive
                 information needs, and yet there is a research gap in
                 our understanding of the factors impacting who is
                 recognized as a local expert and in methods for
                 discovering local experts. Hence, in this article, we
                 explore a geo-spatial learning-to-rank framework for
                 identifying local experts. Three of the key features of
                 the proposed approach are: (i) a learning-based
                 framework for integrating multiple user-based,
                 content-based, list-based, and crowd-based factors
                 impacting local expertise that leverages the
                 fine-grained GPS coordinates of millions of social
                 media users; (ii) a location-sensitive random walk that
                 propagates crowd knowledge of a candidate's expertise;
                 and (iii) a comprehensive controlled study over
                 AMT-labeled local experts on eight topics and in four
                 cities. We find significant improvements of local
                 expert finding versus two state-of-the-art
                 alternatives, as well as evidence for the
                 generalizability of local expert ranking models to new
                 topics and new locations.",
  acknowledgement = ack-nhfb,
  articleno =    "14",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Zhao:2016:OSE,
  author =       "Liang Zhao and Feng Chen and Chang-Tien Lu and Naren
                 Ramakrishnan",
  title =        "Online Spatial Event Forecasting in Microblogs",
  journal =      j-TSAS,
  volume =       "2",
  number =       "4",
  pages =        "15:1--15:39",
  month =        nov,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2997642",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:03 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2997642",
  abstract =     "Event forecasting from social media data streams has
                 many applications. Existing approaches focus on
                 forecasting temporal events (such as elections and
                 sports) but as yet cannot forecast spatiotemporal
                 events such as civil unrest and influenza outbreaks,
                 which are much more challenging. To achieve
                 spatiotemporal event forecasting, spatial features that
                 evolve with time and their underlying correlations need
                 to be considered and characterized. In this article, we
                 propose novel batch and online approaches for
                 spatiotemporal event forecasting in social media such
                 as Twitter. Our models characterize the underlying
                 development of future events by simultaneously modeling
                 the structural contexts and their spatiotemporal
                 burstiness based on different strategies. Both batch
                 and online-based inference algorithms are developed to
                 optimize the model parameters. Utilizing the trained
                 model, the alignment likelihood of tweet sequences is
                 calculated by dynamic programming. Extensive
                 experimental evaluations on two different domains
                 demonstrate the effectiveness of our proposed
                 approach.",
  acknowledgement = ack-nhfb,
  articleno =    "15",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Purushotham:2016:PGR,
  author =       "Sanjay Purushotham and C.-C. Jay Kuo",
  title =        "Personalized Group Recommender Systems for Location-
                 and Event-Based Social Networks",
  journal =      j-TSAS,
  volume =       "2",
  number =       "4",
  pages =        "16:1--16:29",
  month =        nov,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2987381",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:03 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=2987381",
  abstract =     "Location-Based Social Networks (LBSNs) such as
                 Foursquare, Google+ Local, and so on, and Event-Based
                 Social Networks (EBSNs) such as Meetup, Plancast, and
                 so on, have become popular platforms for users to plan,
                 organize, and attend social events with friends and
                 acquaintances. These LBSNs and EBSNs provide rich
                 content such as online and offline user interactions,
                 location/event descriptions that can be leveraged for
                 personalized group recommendations. In this article, we
                 propose novel Collaborative Filtering-based Bayesian
                 models to capture the location or event semantics and
                 group dynamics such as user interactions, user group
                 membership, user influence, and the like for
                 personalized group recommendations. Empirical
                 experiments on two large real-world datasets (Gowalla
                 LBSN dataset and Meetup EBSN dataset) show that our
                 models outperform the state-of-the-art group
                 recommender systems. We discuss the group
                 characteristics of our datasets and show that modeling
                 of group dynamics learns better group preferences than
                 aggregating individual user preferences. Moreover, our
                 model provides human interpretable results that can be
                 used to understand group participation behavior and
                 location/event popularity.",
  acknowledgement = ack-nhfb,
  articleno =    "16",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Amagata:2017:GFM,
  author =       "Daichi Amagata and Takahiro Hara",
  title =        "A General Framework for {MaxRS} and {MaxCRS}
                 Monitoring in Spatial Data Streams",
  journal =      j-TSAS,
  volume =       "3",
  number =       "1",
  pages =        "1:1--1:34",
  month =        may,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3080554",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:03 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=3080554",
  abstract =     "This article addresses the MaxRS (Maximizing Range
                 Sum) monitoring problem. Given a set of weighted
                 spatial stream objects, this problem is to monitor a
                 location of a user-specified sized rectangle where the
                 sum of the weights of the objects covered by the
                 rectangle is maximized. This problem supports modern
                 applications (e.g., traffic analysis and event
                 detection in urban sensing) but has not yet been
                 addressed. Although some algorithms for static objects
                 have been proposed, such algorithms are not scalable to
                 stream environments. These motivate us to devise an
                 algorithm for efficient MaxRS monitoring. We first
                 propose G2 (Graph in Grid index) and a G2-based
                 algorithm to incrementally update the result. We then
                 propose aG2 (aggregate G2), by enhancing G2, and a
                 branch-and-bound algorithm that employs aG2 and can
                 deal with error-guaranteed approximation. We also
                 address MaxCRS monitoring, which is the circle version
                 of the aforementioned problem. Its importance is
                 evident from the fact that distance is also popular as
                 a range criterion. We then have an emerging challenge
                 of developing a general and efficient solution for both
                 continuous MaxRS and MaxCRS queries. Based on a common
                 property of the two problems, we generalize aG2 so as
                 to be employed in both continuous MaxRS and MaxCRS
                 queries. The branch-and-bound algorithm is also
                 extended to suit the generalized index. We conduct
                 extensive experiments using synthetic and real
                 datasets. The experimental results show that our
                 algorithms support a fast result update and
                 significantly outperform the algorithms for static
                 data.",
  acknowledgement = ack-nhfb,
  articleno =    "1",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Iwata:2017:EPF,
  author =       "Tomoharu Iwata and Hitoshi Shimizu and Futoshi Naya
                 and Naonori Ueda",
  title =        "Estimating People Flow from Spatiotemporal Population
                 Data via Collective Graphical Mixture Models",
  journal =      j-TSAS,
  volume =       "3",
  number =       "1",
  pages =        "2:1--2:18",
  month =        may,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3080555",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:03 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=3080555",
  abstract =     "Thanks to the prevalence of mobile phones and GPS
                 devices, spatiotemporal population data can be obtained
                 easily. In this article, we propose a mixture of
                 collective graphical models for estimating people flow
                 from spatiotemporal population data. The spatiotemporal
                 population data we use as input is the number of people
                 in each grid cell area over time, which is aggregated
                 information about many individuals; to preserve
                 privacy, they do not contain trajectories of each
                 individual. Therefore, it is impossible to directly
                 estimate people flow. To overcome this problem, the
                 proposed model assumes that transition populations are
                 hidden variables and estimates the hidden transition
                 populations and transition probabilities
                 simultaneously. The proposed model can handle changes
                 of people flow over time by segmenting time-of-day
                 points into multiple clusters, where different clusters
                 have different flow patterns. We develop an efficient
                 variational Bayesian inference procedure for the
                 collective graphical mixture model. In our experiments,
                 the effectiveness of the proposed method is
                 demonstrated by using four real-world spatiotemporal
                 population datasets in Tokyo, Osaka, Nagoya, and
                 Beijing.",
  acknowledgement = ack-nhfb,
  articleno =    "2",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}

@Article{Karagiorgou:2017:LAM,
  author =       "Sophia Karagiorgou and Dieter Pfoser and Dimitrios
                 Skoutas",
  title =        "A Layered Approach for More Robust Generation of Road
                 Network Maps from Vehicle Tracking Data",
  journal =      j-TSAS,
  volume =       "3",
  number =       "1",
  pages =        "3:1--3:21",
  month =        may,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3061713",
  ISSN =         "2374-0353 (print), 2374-0361 (electronic)",
  ISSN-L =       "2374-0353",
  bibdate =      "Thu Jun 15 14:51:03 MDT 2017",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsas.bib",
  URL =          "http://dl.acm.org/citation.cfm?id=3061713",
  abstract =     "Nowadays, large amounts of tracking data are generated
                 via GPS-enabled devices and other advanced tracking
                 technologies. These constitute a rich source for
                 inferring the structure of transportation networks. In
                 this work, we present a novel methodology for revealing
                 a road network map from vehicle trajectories.
                 Specifically, we propose an enhanced and robust map
                 construction algorithm that is based on segmenting the
                 original tracking data according to different types of
                 movement and then constructing the topology of the road
                 network hierarchically. The segmentation produces
                 separate road network layers, which are then fused into
                 a single network. This provides a more efficient way to
                 addresses the challenges imposed by noisy and low
                 sampling rate trajectories. It also allows for a
                 mechanism to accommodate automatic map maintenance on
                 updates. Thus, the proposed approach overcomes the
                 limitations of existing methods and introduces a map
                 construction algorithm that is robust against
                 heterogeneous and sparse data and capable to
                 incorporate changes and improvements. An experimental
                 evaluation extensively assesses the quality of the
                 proposed methodology by constructing large parts of the
                 road networks of four major cities, namely Athens,
                 Berlin, Vienna, and Chicago, using as input GPS
                 tracking data of utility vehicles and taxi fleets. Our
                 results show significant improvements concerning the
                 spatial accuracy and the quality of the constructed
                 road network over the current state of the art.",
  acknowledgement = ack-nhfb,
  articleno =    "3",
  fjournal =     "ACM Transactions on Spatial Algorithms and Systems
                 (TSAS)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1514",
}