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%%% -*-BibTeX-*-
%%% ====================================================================
%%%  BibTeX-file{
%%%     author          = "Nelson H. F. Beebe",
%%%     version         = "1.01",
%%%     date            = "10 March 2021",
%%%     time            = "06:31:30 MST",
%%%     filename        = "tds.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        = "65454 1188 5181 49538",
%%%     email           = "beebe at math.utah.edu, beebe at acm.org,
%%%                        beebe at computer.org (Internet)",
%%%     codetable       = "ISO/ASCII",
%%%     keywords        = "ACM/IMS Transactions on Data Science
%%%                        (TDS); bibliography; BibTeX",
%%%     license         = "public domain",
%%%     supported       = "yes",
%%%     docstring       = "This is a COMPLETE BibTeX bibliography for
%%%                        ACM/IMS Transactions on Data Science (TDS)
%%%                        (CODEN ????, ISSN 2691-1922).  The journal
%%%                        appears quarterly, and publication began with
%%%                        volume 1, number 1, in February 2020.
%%%
%%%                        At version 1.01, the COMPLETE journal
%%%                        coverage looked like this:
%%%
%%%                             2020 (  30)    2021 (   7)
%%%
%%%                             Article:         37
%%%
%%%                             Total entries:   37
%%%
%%%                        The journal Web page can be found at:
%%%
%%%                            http://tds.acm.org/
%%%                            https://dl.acm.org/journal/tds
%%%
%%%                        The journal table of contents page is at:
%%%
%%%                            https://dl.acm.org/loi/tds
%%%
%%%                        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.
%%%
%%%                        URL keys in the bibliography point to
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%%%                        information about the entry.
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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-TDS                   = "ACM Transactions on Data Science
                                  (TDS)"}

%%% ====================================================================
%%% Bibliography entries:
@Article{Ooi:2020:IIE,
  author =       "Beng Chin Ooi",
  title =        "Inaugural Issue Editorial",
  journal =      j-TDS,
  volume =       "1",
  number =       "1",
  pages =        "1:1--1:2",
  month =        mar,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3368254",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Tue Apr 7 15:14:47 MDT 2020",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3368254",
  acknowledgement = ack-nhfb,
  articleno =    "1",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Chakraborty:2020:EDA,
  author =       "Tanmoy Chakraborty and Noseong Park and Ayush Agarwal
                 and V. S. Subrahmanian",
  title =        "Ensemble Detection and Analysis of Communities in
                 Complex Networks",
  journal =      j-TDS,
  volume =       "1",
  number =       "1",
  pages =        "2:1--2:34",
  month =        mar,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3313374",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Tue Apr 7 15:14:47 MDT 2020",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3313374",
  abstract =     "Though much work has been done on ensemble clustering
                 in data mining, the application of ensemble methods to
                 community detection in networks is in its infancy. In
                 this article, we propose MeDOF, an ensemble method
                 which performs disjoint, overlapping, \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "2",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Li:2020:GBR,
  author =       "Guohui Li and Qi Chen and Bolong Zheng and Hongzhi Yin
                 and Quoc Viet Hung Nguyen and Xiaofang Zhou",
  title =        "Group-Based Recurrent Neural Networks for {POI}
                 Recommendation",
  journal =      j-TDS,
  volume =       "1",
  number =       "1",
  pages =        "3:1--3:18",
  month =        mar,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3343037",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Tue Apr 7 15:14:47 MDT 2020",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3343037",
  abstract =     "With the development of mobile Internet, many
                 location-based services have accumulated a large amount
                 of data that can be used for point-of-interest (POI)
                 recommendation. However, there are still challenges in
                 developing an unified framework to \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "3",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Li:2020:MAF,
  author =       "Xiangyang Li and Luis Herranz and Shuqiang Jiang",
  title =        "Multifaceted Analysis of Fine-Tuning in a Deep Model
                 for Visual Recognition",
  journal =      j-TDS,
  volume =       "1",
  number =       "1",
  pages =        "4:1--4:22",
  month =        mar,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3319500",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Tue Apr 7 15:14:47 MDT 2020",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3319500",
  abstract =     "In recent years, convolutional neural networks (CNNs)
                 have achieved impressive performance for various visual
                 recognition scenarios. CNNs trained on large labeled
                 datasets not only obtain significant performance on
                 most challenging benchmarks but also \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "4",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Ward:2020:PAA,
  author =       "Katrina Ward and Dan Lin and Sanjay Madria",
  title =        "A Parallel Algorithm For Anonymizing Large-scale
                 Trajectory Data",
  journal =      j-TDS,
  volume =       "1",
  number =       "1",
  pages =        "5:1--5:26",
  month =        mar,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3368639",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Tue Apr 7 15:14:47 MDT 2020",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3368639",
  abstract =     "With the proliferation of location-based services
                 enabled by a large number of mobile devices and
                 applications, the quantity of location data, such as
                 trajectories collected by service providers, is
                 gigantic. If these datasets could be published, then
                 \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "5",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Theil:2020:EFU,
  author =       "Christoph Kilian Theil and Sanja Stajner and Heiner
                 Stuckenschmidt",
  title =        "Explaining Financial Uncertainty through Specialized
                 Word Embeddings",
  journal =      j-TDS,
  volume =       "1",
  number =       "1",
  pages =        "6:1--6:19",
  month =        mar,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3343039",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Tue Apr 7 15:14:47 MDT 2020",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3343039",
  abstract =     "The detection of vague, speculative, or otherwise
                 uncertain language has been performed in the
                 encyclopedic, political, and scientific domains yet
                 left relatively untouched in finance. However, the
                 latter benefits from public sources of big financial
                 \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "6",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Beigi:2020:SPS,
  author =       "Ghazaleh Beigi and Huan Liu",
  title =        "A Survey on Privacy in Social Media: Identification,
                 Mitigation, and Applications",
  journal =      j-TDS,
  volume =       "1",
  number =       "1",
  pages =        "7:1--7:38",
  month =        mar,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3343038",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Tue Apr 7 15:14:47 MDT 2020",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3343038",
  abstract =     "The increasing popularity of social media has
                 attracted a huge number of people to participate in
                 numerous activities on a daily basis. This results in
                 tremendous amounts of rich user-generated data. These
                 data provide opportunities for researchers and
                 \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "7",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Gan:2020:UMA,
  author =       "Wensheng Gan and Jerry Chun-Wei Lin and Jiexiong Zhang
                 and Philip S. Yu",
  title =        "Utility Mining across Multi-Sequences with
                 Individualized Thresholds",
  journal =      j-TDS,
  volume =       "1",
  number =       "2",
  pages =        "8:1--8:29",
  month =        jul,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3362070",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3362070",
  abstract =     "Utility-oriented pattern mining is an emerging topic,
                 since it can reveal high-utility patterns from
                 different types of data, which provides more
                 information than the traditional
                 frequency/confidence-based pattern mining models. The
                 utilities of various \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "8",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Yu:2020:MBI,
  author =       "Lei Yu and Guohui Li and Ling Yuan",
  title =        "Maximizing Boosted Influence Spread with Edge Addition
                 in Online Social Networks",
  journal =      j-TDS,
  volume =       "1",
  number =       "2",
  pages =        "9:1--9:21",
  month =        jul,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3364993",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3364993",
  abstract =     "Influence maximization with application to viral
                 marketing is a well-studied problem of finding a small
                 number of influential users in a social network to
                 maximize the spread of influence under certain
                 influence cascade models. However, almost all
                 \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "9",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Chowdhury:2020:RTP,
  author =       "Ranak Roy Chowdhury and Muhammad Abdullah Adnan and
                 Rajesh K. Gupta",
  title =        "Real-Time Principal Component Analysis",
  journal =      j-TDS,
  volume =       "1",
  number =       "2",
  pages =        "10:1--10:36",
  month =        jul,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3374750",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3374750",
  abstract =     "We propose a variant of Principal Component Analysis
                 (PCA) that is suited for real-time applications. In the
                 real-time version of the PCA problem, we maintain a
                 window over the most recent data and project every
                 incoming row of data into a lower-. \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "10",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Wu:2020:DST,
  author =       "Gene P. K. Wu and Keith C. C. Chan",
  title =        "Discovery of Spatio-Temporal Patterns in Multivariate
                 Spatial Time Series",
  journal =      j-TDS,
  volume =       "1",
  number =       "2",
  pages =        "11:1--11:22",
  month =        jul,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3374748",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3374748",
  abstract =     "With the advancement of the computing technology and
                 its wide range of applications, collecting large sets
                 of multivariate time series in multiple geographical
                 locations introduces a problem of identifying
                 interesting spatio-temporal patterns. We \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "11",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Bessa:2020:EDM,
  author =       "Aline Bessa and Juliana Freire and Tamraparni Dasu and
                 Divesh Srivastava",
  title =        "Effective Discovery of Meaningful Outlier
                 Relationships",
  journal =      j-TDS,
  volume =       "1",
  number =       "2",
  pages =        "12:1--12:33",
  month =        jul,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3385192",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3385192",
  abstract =     "We propose Predictable Outliers in Data-trendS (PODS),
                 a method that, given a collection of temporal datasets,
                 derives data-driven explanations for outliers by
                 identifying meaningful relationships between them.
                 First, we formalize the notion of \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "12",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Nashaat:2020:AGL,
  author =       "Mona Nashaat and Aindrila Ghosh and James Miller and
                 Shaikh Quader",
  title =        "{Asterisk}: Generating Large Training Datasets with
                 Automatic Active Supervision",
  journal =      j-TDS,
  volume =       "1",
  number =       "2",
  pages =        "13:1--13:25",
  month =        jul,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3385188",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3385188",
  abstract =     "Labeling datasets is one of the most expensive
                 bottlenecks in data preprocessing tasks in machine
                 learning. Therefore, organizations, in many domains,
                 are applying weak supervision to produce noisy labels.
                 However, since weak supervision relies on \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "13",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Li:2020:ISI,
  author =       "Yanhua Li and Jie Bao and Zhi-Li Zhang and Saif
                 Benjaafar",
  title =        "Introduction to the Special Issue on Urban Computing
                 and Smart Cities",
  journal =      j-TDS,
  volume =       "1",
  number =       "3",
  pages =        "14:1--14:2",
  month =        oct,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3412392",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3412392",
  acknowledgement = ack-nhfb,
  articleno =    "14",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Sainju:2020:MRS,
  author =       "Arpan Man Sainju and Zhe Jiang",
  title =        "Mapping Road Safety Features from Streetview Imagery:
                 a Deep Learning Approach",
  journal =      j-TDS,
  volume =       "1",
  number =       "3",
  pages =        "15:1--15:20",
  month =        oct,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3362069",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3362069",
  abstract =     "Each year, an average of around 6 million car
                 accidents occur in the United States. Road safety
                 features (e.g., concrete barriers, metal crash
                 barriers, rumble strips) play an important role in
                 preventing or mitigating vehicle crashes. Accurate maps
                 of \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "15",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Tian:2020:UEB,
  author =       "Zhihong Tian and Chaochao Luo and Hui Lu and Shen Su
                 and Yanbin Sun and Man Zhang",
  title =        "User and Entity Behavior Analysis under Urban Big
                 Data",
  journal =      j-TDS,
  volume =       "1",
  number =       "3",
  pages =        "16:1--16:19",
  month =        oct,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3374749",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3374749",
  abstract =     "Recently, the urban network infrastructure has
                 undergone a rapid expansion that is increasingly
                 generating a large quantity of data and transforming
                 our cities into smart cities. However, serious security
                 problems arise with this development with more
                 \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "16",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Xie:2020:UFR,
  author =       "Yiqun Xie and Shashi Shekhar",
  title =        "A Unified Framework for Robust and Efficient Hotspot
                 Detection in Smart Cities",
  journal =      j-TDS,
  volume =       "1",
  number =       "3",
  pages =        "17:1--17:29",
  month =        oct,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3379562",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3379562",
  abstract =     "Given N geo-located point instances (e.g., crime or
                 disease cases) in a spatial domain, we aim to detect
                 sub-regions (i.e., hotspots) that have a higher
                 probability density of generating such instances than
                 the others. Hotspot detection has been widely
                 \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "17",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Ding:2020:TAH,
  author =       "Weilong Ding and Zhuofeng Zhao and Jianwu Wang and Han
                 Li",
  title =        "Task Allocation in Hybrid Big Data Analytics for Urban
                 {IoT} Applications",
  journal =      j-TDS,
  volume =       "1",
  number =       "3",
  pages =        "18:1--18:22",
  month =        oct,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3374751",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3374751",
  abstract =     "In urban Internet of Things (IoT) environments, data
                 generated in real time could be processed by analytical
                 applications in online or offline mode. In the
                 management perspective of runtime environments, such
                 modes can hardly be supported in a unified \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "18",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Yang:2020:SAT,
  author =       "Zhong Yang and Bolong Zheng and Guohui Li and Nguyen
                 Quoc Viet Hung and Guanfeng Liu and Kai Zheng",
  title =        "Searching Activity Trajectories by Exemplar",
  journal =      j-TDS,
  volume =       "1",
  number =       "3",
  pages =        "19:1--19:18",
  month =        oct,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3379561",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3379561",
  abstract =     "The rapid explosion of urban cities has modernized the
                 residents' lives and generated a large amount of data
                 (e.g., human mobility data, traffic data, and
                 geographical data), especially the activity trajectory
                 data that contains spatial and temporal as \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "19",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Xu:2020:AWF,
  author =       "Xin Xu and Yanjie Fu and Jingyi Wu and Yuqi Wang and
                 Zeyu Huang and Zhiguo Fu and Minghao Yin",
  title =        "Adaptive Weighted Finite Mixture Model: Identifying
                 the Feature-Influence of Real Estate",
  journal =      j-TDS,
  volume =       "1",
  number =       "3",
  pages =        "20:1--20:16",
  month =        oct,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3379560",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3379560",
  abstract =     "It is significant for real estate investors to
                 understand how the construction environments and
                 building characteristics impact the housing unit price.
                 However, it is challenging for identifying the complex
                 feature-influence from construction \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "20",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Zeidan:2020:GEL,
  author =       "Ayman Zeidan and Eemil Lagerspetz and Kai Zhao and
                 Petteri Nurmi and Sasu Tarkoma and Huy T. Vo",
  title =        "{GeoMatch}: Efficient Large-scale Map Matching on
                 {Apache Spark}",
  journal =      j-TDS,
  volume =       "1",
  number =       "3",
  pages =        "21:1--21:30",
  month =        oct,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3402904",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3402904",
  abstract =     "We develop GeoMatch as a novel, scalable, and
                 efficient big-data pipeline for large-scale map
                 matching on Apache Spark. GeoMatch improves existing
                 spatial big-data solutions by utilizing a novel spatial
                 partitioning scheme inspired by Hilbert space-.
                 \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "21",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Li:2020:PGE,
  author =       "Yan Li and Pratik Kotwal and Pengyue Wang and Yiqun
                 Xie and Shashi Shekhar and William Northrop",
  title =        "Physics-guided Energy-efficient Path Selection Using
                 On-board Diagnostics Data",
  journal =      j-TDS,
  volume =       "1",
  number =       "3",
  pages =        "22:1--22:28",
  month =        oct,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3406596",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3406596",
  abstract =     "Given a spatial graph, an origin and a destination,
                 and on-board diagnostics (OBD) data, the
                 energy-efficient path selection problem aims to find
                 the path with the least expected energy consumption
                 (EEC). Two main objectives of smart cities are
                 \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "22",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Arabghalizi:2020:DDB,
  author =       "Tahereh Arabghalizi and Alexandros Labrinidis",
  title =        "Data-driven Bus Crowding Prediction Models Using
                 Context-specific Features",
  journal =      j-TDS,
  volume =       "1",
  number =       "3",
  pages =        "23:1--23:33",
  month =        oct,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3406962",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:05 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3406962",
  abstract =     "Public transit is one of the first things that come to
                 mind when someone talks about ``smart cities.'' As a
                 result, many technologies, applications, and
                 infrastructure have already been deployed to bring the
                 promise of the smart city to public \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "23",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Hu:2020:ISI,
  author =       "Haibo Hu and Rik Sarkar and Zhengzhang Chen",
  title =        "Introduction to the Special Issue on Retrieving and
                 Learning from {Internet of Things} Data",
  journal =      j-TDS,
  volume =       "1",
  number =       "4",
  pages =        "24:1--24:1",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3426368",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:06 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3426368",
  acknowledgement = ack-nhfb,
  articleno =    "24",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Fang:2020:HHR,
  author =       "Liming Fang and Hongwei Zhu and Boqing Lv and Zhe Liu
                 and Weizhi Meng and Yu Yu and Shouling Ji and Zehong
                 Cao",
  title =        "{HandiText}: Handwriting Recognition Based on Dynamic
                 Characteristics with Incremental {LSTM}",
  journal =      j-TDS,
  volume =       "1",
  number =       "4",
  pages =        "25:1--25:18",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3385189",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:06 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
                 http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3385189",
  abstract =     "The Internet of Things (IoT) is a new manifestation of
                 data science. To ensure the credibility of data about
                 IoT devices, authentication has gradually become an
                 important research topic in the IoT ecosystem. However,
                 traditional graphical passwords and text passwords can
                 cause user's serious memory burdens. Therefore, a
                 convenient method for determining user identity is
                 needed. In this article, we propose a handwriting
                 recognition authentication scheme named HandiText based
                 on behavior and biometrics features. When people write
                 a word by hand, HandiText captures their static
                 biological features and dynamic behavior features
                 during the writing process (writing speed, pressure,
                 etc.). The features are related to habits, which make
                 it difficult for attackers to imitate. We also carry
                 out algorithms comparisons and experiments evaluation
                 to prove the reliability of our scheme. The experiment
                 results show that the Long Short-Term Memory has the
                 best classification accuracy, reaching 99% while
                 keeping relatively low false-positive rate and
                 false-negative rate. We also test other datasets, the
                 average accuracy of HandiText reach 98%, with strong
                 generalization ability. Besides, the 324 users we
                 investigated indicated that they are willing to use
                 this scheme on IoT devices.",
  acknowledgement = ack-nhfb,
  articleno =    "25",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Li:2020:TTR,
  author =       "Huan Li and Hua Lu and Gang Chen and Ke Chen and
                 Qinkuang Chen and Lidan Shou",
  title =        "Toward Translating Raw Indoor Positioning Data into
                 Mobility Semantics",
  journal =      j-TDS,
  volume =       "1",
  number =       "4",
  pages =        "26:1--26:37",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3385190",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:06 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3385190",
  abstract =     "Indoor mobility analyses are increasingly interesting
                 due to the rapid growth of raw indoor positioning data
                 obtained from IoT infrastructure. However, high-level
                 analyses are still in urgent need of a concise but
                 semantics-oriented representation of \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "26",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Busany:2020:IBQ,
  author =       "Nimrod Busany and Han {Van Der Aa} and Arik
                 Senderovich and Avigdor Gal and Matthias Weidlich",
  title =        "Interval-based Queries over Lossy {IoT} Event
                 Streams",
  journal =      j-TDS,
  volume =       "1",
  number =       "4",
  pages =        "27:1--27:27",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3385191",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:06 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3385191",
  abstract =     "Recognising patterns that correlate multiple events
                 over time becomes increasingly important in
                 applications that exploit the Internet of Things,
                 reaching from urban transportation through surveillance
                 monitoring to business workflows. In many real-.
                 \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "27",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Schmeisser:2020:CCS,
  author =       "Stephan Schmei{\ss}er and Gregor Schiele",
  title =        "{coSense}: The Collaborative Sensing Middleware for
                 the {Internet}-of-Things",
  journal =      j-TDS,
  volume =       "1",
  number =       "4",
  pages =        "28:1--28:21",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3395233",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:06 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3395233",
  abstract =     "We present coSense -the collaborative, fault-tolerant,
                 and adaptive sensing middleware for the
                 Internet-of-Things (IoT). By actively harnessing the
                 greatest asset of the IoT, the sheer number of devices,
                 coSense is able to provide easy data acquisition
                 \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "28",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Elbassuoni:2020:FSO,
  author =       "Shady Elbassuoni and Sihem Amer-Yahia and Ahmad
                 Ghizzawi",
  title =        "Fairness of Scoring in Online Job Marketplaces",
  journal =      j-TDS,
  volume =       "1",
  number =       "4",
  pages =        "29:1--29:30",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3402883",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:06 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3402883",
  abstract =     "We study fairness of scoring in online job
                 marketplaces. We focus on group fairness and aim to
                 algorithmically explore how a scoring function, through
                 which individuals are ranked for jobs, treats different
                 demographic groups. Previous work on group-. \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "29",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Yang:2020:MFR,
  author =       "Luoying Yang and Zhou Xu and Jiebo Luo",
  title =        "Measuring Female Representation and Impact in Films
                 over Time",
  journal =      j-TDS,
  volume =       "1",
  number =       "4",
  pages =        "30:1--30:14",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3411213",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:06 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3411213",
  abstract =     "Women have always been underrepresented in movies and
                 not until recently has the representation of women in
                 movies improved. To investigate the improvement of
                 female representation and its relationship with a
                 movie's success, we propose a new measure, \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "30",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Shi:2021:NAT,
  author =       "Tian Shi and Yaser Keneshloo and Naren Ramakrishnan
                 and Chandan K. Reddy",
  title =        "Neural Abstractive Text Summarization with
                 Sequence-to-Sequence Models",
  journal =      j-TDS,
  volume =       "2",
  number =       "1",
  pages =        "1:1--1:37",
  month =        jan,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3419106",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:07 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3419106",
  abstract =     "In the past few years, neural abstractive text
                 summarization with sequence-to-sequence (seq2seq)
                 models have gained a lot of popularity. Many
                 interesting techniques have been proposed to improve
                 seq2seq models, making them capable of handling
                 different \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "1",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Li:2021:ISI,
  author =       "Yanhua Li and Jie Bao and Zhi-Li Zhang and Saif
                 Benjaafar",
  title =        "Introduction to the Special Issue on Urban Computing
                 and Smart Cities",
  journal =      j-TDS,
  volume =       "2",
  number =       "1",
  pages =        "2e:1--2e:2",
  month =        jan,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3441679",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:07 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3441679",
  acknowledgement = ack-nhfb,
  articleno =    "2e",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Liu:2021:SBU,
  author =       "Xiuming Liu and Edith Ngai and Dave Zachariah",
  title =        "Scalable Belief Updating for Urban Air Quality
                 Modeling and Prediction",
  journal =      j-TDS,
  volume =       "2",
  number =       "1",
  pages =        "2:1--2:19",
  month =        jan,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3402903",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:07 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3402903",
  abstract =     "Air pollution is one of the major concerns in global
                 urbanization. Data science can help to understand the
                 dynamics of air pollution and build reliable
                 statistical models to forecast air pollution levels. To
                 achieve these goals, one needs to learn the \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "2",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Iyengar:2021:WDD,
  author =       "Srinivasan Iyengar and Stephen Lee and David Irwin and
                 Prashant Shenoy and Benjamin Weil",
  title =        "{WattScale}: a Data-driven Approach for Energy
                 Efficiency Analytics of Buildings at Scale",
  journal =      j-TDS,
  volume =       "2",
  number =       "1",
  pages =        "3:1--3:25",
  month =        jan,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3406961",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:07 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3406961",
  abstract =     "Buildings consume over 40\% of the total energy in
                 modern societies, and improving their energy efficiency
                 can significantly reduce our energy footprint. In this
                 article, we present WattScale, a data-driven approach
                 to identify the least energy-efficient \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "3",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Jiang:2021:TUH,
  author =       "Renhe Jiang and Xuan Song and Zipei Fan and Tianqi Xia
                 and Zhaonan Wang and Quanjun Chen and Zekun Cai and
                 Ryosuke Shibasaki",
  title =        "Transfer Urban Human Mobility via {POI} Embedding over
                 Multiple Cities",
  journal =      j-TDS,
  volume =       "2",
  number =       "1",
  pages =        "4:1--4:26",
  month =        jan,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3416914",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:07 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3416914",
  abstract =     "Rapidly developing location acquisition technologies
                 provide a powerful tool for understanding and
                 predicting human mobility in cities, which is very
                 significant for urban planning, traffic regulation, and
                 emergency management. However, with the \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "4",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Babicheva:2021:EVR,
  author =       "Tatiana Babicheva and Matej Cebecauer and Dominique
                 Barth and Wilco Burghout and Le{\"\i}la Kloul",
  title =        "Empty Vehicle Redistribution with Time Windows in
                 Autonomous Taxi Systems",
  journal =      j-TDS,
  volume =       "2",
  number =       "1",
  pages =        "5:1--5:22",
  month =        jan,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3416915",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:07 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3416915",
  abstract =     "In this article, we investigate empty vehicle
                 redistribution algorithms with time windows for
                 personal rapid transit or autonomous station-based taxi
                 services, from a passenger service perspective. We
                 present an Index Based Redistribution Time Limited
                 \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "5",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}

@Article{Molinaro:2021:SST,
  author =       "Cristian Molinaro and Chiara Pulice and Anja Subasic
                 and Abigail Bartolome and V. S. Subrahmanian",
  title =        "{STAR: Summarizing Timed Association Rules}",
  journal =      j-TDS,
  volume =       "2",
  number =       "1",
  pages =        "6:1--6:36",
  month =        jan,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3419107",
  ISSN =         "2691-1922",
  ISSN-L =       "2691-1922",
  bibdate =      "Wed Mar 10 06:28:07 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tds.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3419107",
  abstract =     "Timed association rules (TARs) generalize classical
                 association rules (ARs) so that we can express temporal
                 dependencies of the form ``If $X$ is true at time $t$,
                 then $Y$ will likely be true at time $ (t + \tau)$.''
                 As with ARs, solving the TAR mining problem can
                 \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "6",
  fjournal =     "ACM Transactions on Data Science",
  journal-URL =  "https://dl.acm.org/loi/tds",
}