%%% -*-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)",
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%%%     keywords        = "ACM Transactions on Spatial Algorithms and
%%%                        Systems (TSAS); bibliography; BibTeX",
%%%     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
%%%
%%%
%%%                            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
%%%
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%%%
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%%%                        publication order, using bibsort -byvolume.''
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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
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
time and location) to a set of workers, which requires
the workers to physically travel to those locations in
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
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.
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
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
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",
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",
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
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
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",
}

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",
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
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",
}

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
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
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
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
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",
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
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
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
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",
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
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",
}