pflock.bib

@inproceedings{welzl_smallest_1991,
	title = {Smallest enclosing disks (balls and ellipsoids)},
	booktitle = {New {Results} and {New} {Trends} in {Computer} {Science}},
	publisher = {Springer},
	author = {Welzl, E.},
	year = {1991},
	pages = {359--370},
}

@inproceedings{eppstein_listing_2010,
	title = {Listing {All} {Maximal} {Cliques} in {Sparse} {Graphs} in {Near}-{Optimal} {Time}},
	booktitle = {Algorithms and {Computation}},
	author = {Eppstein, D. and Löffler, M. and Strash, D.},
	year = {2010},
	pages = {403--414},
}

@article{bron_algorithm_1973,
	title = {Algorithm 457: finding all cliques of an undirected graph},
	volume = {16},
	number = {9},
	journal = {Communications of the ACM},
	author = {Bron, C. and Kerbosch, J.},
	year = {1973},
	pages = {575--577},
}

@article{zheng_geolife_2010,
	title = {{GeoLife}: {A} {Collaborative} {Social} {Networking} {Service} among {User}, {Location} and {Trajectory}.},
	volume = {33},
	shorttitle = {{GeoLife}},
	number = {2},
	urldate = {2016-12-09},
	journal = {IEEE Data Eng. Bull.},
	author = {Zheng, Yu and Xie, Xing and Ma, Wei-Ying},
	year = {2010},
	pages = {32--39},
}

@inproceedings{zheng_mining_2009,
	title = {Mining {Interesting} {Locations} and {Travel} {Sequences} from {GPS} {Trajectories}},
	urldate = {2016-12-09},
	booktitle = {Proceedings of the 18th {International} {Conference} on {World} {Wide} {Web}},
	publisher = {ACM},
	author = {Zheng, Yu and Zhang, Lizhu and Xie, Xing and Ma, Wei-Ying},
	year = {2009},
	pages = {791--800},
}

@article{moreira-matias_predicting_2013,
	title = {Predicting {Taxi}-{Passenger} {Demand} {Using} {Streaming} {Data}},
	volume = {14},
	issn = {1524-9050},
	doi = {10.1109/TITS.2013.2262376},
	number = {3},
	journal = {Trans. Intell. Transport. Sys.},
	author = {Moreira-Matias, Luis and Gama, Joao and Ferreira, Michel and Mendes-Moreira, Joao and Damas, Luis},
	month = sep,
	year = {2013},
	pages = {1393--1402},
}

@inproceedings{zheng_understanding_2008,
	title = {Understanding {Mobility} {Based} on {GPS} {Data}},
	urldate = {2016-12-09},
	booktitle = {Proceedings of the 10th {International} {Conference} on {Ubiquitous} {Computing}},
	publisher = {ACM},
	author = {Zheng, Yu and Li, Quannan and Chen, Yukun and Xie, Xing and Ma, Wei-Ying},
	year = {2008},
	pages = {312--321},
}

@inproceedings{xie_simba_2016,
	title = {Simba: {Efficient} {In}-{Memory} {Spatial} {Analytics}},
	booktitle = {{ICMD}},
	author = {Xie, D. and Li, F. and Yao, B. and Li, G. and Zhou, L. and Guo, M.},
	year = {2016},
	pages = {1071--1085},
}

@inproceedings{eldawy_spatialhadoop_2014,
	title = {{SpatialHadoop}: {Towards} {Flexible} and {Scalable} {Spatial} {Processing} {Using} {Mapreduce}},
	booktitle = {{SIGMOD} {PhD} {Symposium}},
	author = {Eldawy, A.},
	year = {2014},
	pages = {46--50},
}

@article{lam_blue_2015,
	title = {({Blue}) {Taxi} {Destination} and {Trip} {Time} {Prediction} from {Partial} {Trajectories}},
	urldate = {2016-12-09},
	journal = {ECML/PKDD Discovery Challenge 2015},
	author = {Lam, Hoang Thanh and Diaz-Aviles, Ernesto and Pascale, Alessandra and Gkoufas, Yiannis and Chen, Bei},
	year = {2015},
}

@inproceedings{yu_geospark_2015,
	title = {{GeoSpark}: {A} {Cluster} {Computing} {Framework} for {Processing} {Large}-{Scale} {Spatial} {Data}},
	isbn = {978-1-4503-3967-4},
	shorttitle = {{GeoSpark}},
	doi = {10.1145/2820783.2820860},
	language = {en},
	urldate = {2016-12-08},
	publisher = {ACM Press},
	author = {Yu, Jia and Wu, Jinxuan and Sarwat, Mohamed},
	year = {2015},
	pages = {1--4},
}

@article{fort_parallel_2014,
	title = {A {Parallel} {GPU}-{Based} {Approach} for {Reporting} {Flock} {Patterns}},
	volume = {28},
	number = {9},
	journal = {IJGIS},
	author = {Fort, M. and Antoni, J. and Valladares, N.},
	year = {2014},
	pages = {1877--1903},
}

@inproceedings{geng_enumeration_2014,
	title = {Enumeration of {Complete} {Set} of {Flock} {Patterns} in {Trajectories}},
	booktitle = {{IWGS}},
	author = {Geng, X. and Takagi, T. and Arimura, H. and Uno, T.},
	year = {2014},
	pages = {53--61},
}

@inproceedings{hughes_geomesa_2015,
	title = {{GeoMesa}: a distributed architecture for spatio-temporal fusion},
	booktitle = {Defense + {Security} {Symposium}},
	author = {Hughes, J. and Annex, A. and Eichelberger, C. and Fox, A. and Hulbert, A. and Ronquest, M.},
	year = {2015},
}

@book{li_trends_2015,
	title = {Trends and {Applications} in {Knowledge} {Discovery} and {Data} {Mining}},
	volume = {9441},
	publisher = {Springer},
	editor = {Li, X. and Cao, T. and Lim, E. and Zhou, Z. and Ho, T. and Cheung, D.},
	year = {2015},
}

@unpublished{arimura_finding_2014,
	title = {Finding {All} {Maximal} {Duration} {Flock} {Patterns} in {High}-{Dimensional} {Trajectories}},
	author = {Arimura, H. and Takagi, T. and Geng, X. and Uno, T.},
	year = {2014},
}

@article{benkert_reporting_2008,
	title = {Reporting {Flock} {Patterns}},
	volume = {41},
	number = {3},
	journal = {Computational Geometry},
	author = {Benkert, M. and Gudmundsson, J. and Hübner, F. and Wolle, T.},
	year = {2008},
	pages = {111--125},
}

@article{tanaka_improved_2016,
	title = {An {Improved} {Base} {Algorithm} for {Online} {Discovery} of {Flock} {Patterns} in {Trajectories}},
	volume = {7},
	number = {1},
	journal = {JIDM},
	author = {Tanaka, P. and Vieira, M.. and Kaster, D.},
	year = {2016},
}

@inproceedings{geng_trajectory_2013,
	title = {Trajectory {Pattern} {Mining} in {Practice}-{Algorithms} for {Mining} {Flock} {Patterns} from {Trajectories}.},
	urldate = {2016-12-08},
	booktitle = {{KDIR}/{KMIS}},
	author = {Geng, Xiaoliang and Uno, Takeaki and Arimura, Hiroki},
	year = {2013},
	pages = {143--151},
}

@article{zheng_trajectory_2015,
	title = {Trajectory {Data} {Mining}: {An} {Overview}},
	volume = {6},
	issn = {21576904},
	shorttitle = {Trajectory {Data} {Mining}},
	doi = {10.1145/2743025},
	language = {en},
	number = {3},
	urldate = {2016-12-08},
	journal = {ACM Transactions on Intelligent Systems and Technology},
	author = {Zheng, Yu},
	month = may,
	year = {2015},
	pages = {1--41},
}

@inproceedings{gudmundsson_efficient_2004,
	title = {Efficient {Detection} of {Motion} {Patterns} in {Spatio}-{Temporal} {Data} {Sets}},
	booktitle = {{IWGIS}},
	publisher = {ACM},
	author = {Gudmundsson, J. and van Kreveld, M. and Speckmann, B.},
	year = {2004},
	pages = {250--257},
}

@article{long_combining_2015,
	series = {Special {Issue} on {Volunteered} {Geographic} {Information}},
	title = {Combining {Smart} {Card} {Data} and {Household} {Travel} {Survey} to {Analyze} {Jobs}–housing {Relationships} in {Beijing}},
	volume = {53},
	issn = {0198-9715},
	doi = {10.1016/j.compenvurbsys.2015.02.005},
	abstract = {Location Based Services (LBS) provide a new perspective for spatiotemporally analyzing dynamic urban systems. Research has investigated urban dynamics using LBS. However, less attention has been paid to the analysis of urban structure (especially commuting pattern) using smart card data (SCD), which are widely available in most large cities in China, and even in the world. This paper combines bus SCD for a one-week period with a oneday household travel survey, as well as a parcel-level land use map to identify job–housing locations and commuting trip routes in Beijing. Two data forms are proposed, one for jobs–housing identification and the other for commuting trip route identification. The results of the identification are aggregated in the bus stop and traffic analysis zone (TAZ) scales, respectively. Particularly, commuting trips from three typical residential communities to six main business zones are mapped and compared to analyze commuting patterns in Beijing. The identified commuting trips are validated by comparison with those from the survey in terms of commuting time and distance, and the positive validation results prove the applicability of our approach. Our experiment, as a first step toward enriching LBS data using conventional survey and urban GIS data, can obtain solid identification results based on rules extracted from existing surveys or censuses.},
	urldate = {2016-12-07},
	journal = {Computers, Environment and Urban Systems},
	author = {Long, Ying and Thill, Jean-Claude},
	month = sep,
	year = {2015},
	keywords = {Beijing, Bus smart card data, Commuting trip, Jobs–housing spatial mismatch, Rule-based},
	pages = {19--35},
}

@article{huang_trajgraph_2016,
	title = {{TrajGraph}: {A} {Graph}-{Based} {Visual} {Analytics} {Approach} to {Studying} {Urban} {Network} {Centralities} {Using} {Taxi} {Trajectory} {Data}},
	volume = {22},
	issn = {1077-2626},
	shorttitle = {{TrajGraph}},
	doi = {10.1109/TVCG.2015.2467771},
	number = {1},
	urldate = {2016-12-07},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	author = {Huang, Xiaoke and Zhao, Ye and Ma, Chao and Yang, Jing and Ye, Xinyue and Zhang, Chong},
	month = jan,
	year = {2016},
	pages = {160--169},
}

@inproceedings{piciarelli_trajectory_2005,
	title = {Trajectory {Clustering} and {Its} {Applications} for {Video} {Surveillance}},
	doi = {10.1109/AVSS.2005.1577240},
	abstract = {In this paper we present a trajectory clustering method suited for video surveillance and monitoring systems. The clusters are dynamic and built in real-time as the trajectory data is acquired, without the need of an off-line processing step. We show how the obtained clusters can be successfully used both to give proper feedback to the low-level tracking system and to collect valuable information for the high-level event analysis modules.},
	booktitle = {{IEEE} {Conference} on {Advanced} {Video} and {Signal} {Based} {Surveillance}, 2005.},
	author = {Piciarelli, C. and Foresti, G. L. and Snidaro, L.},
	month = sep,
	year = {2005},
	keywords = {Application software, Clustering algorithms, Clustering methods, Computer science, Computerized monitoring, Hidden Markov models, Layout, Mathematics, Vector quantization, high-level event analysis modules, monitoring, monitoring systems, pattern clustering, surveillance, trajectory clustering, video signal processing, video surveillance},
	pages = {40--45},
}

@article{makris_path_2002,
	title = {Path {Detection} in {Video} {Surveillance}},
	volume = {20},
	issn = {0262-8856},
	doi = {10.1016/S0262-8856(02)00098-7},
	abstract = {This paper addresses the problem of automatically extracting frequently used pedestrian pathways from video sequences of natural outdoor scenes. Path models are learnt from the accumulation of trajectory data over long time periods, and can be used to augment the classification of subsequent track data. In particular, labelled paths provide an efficient means for compressing the trajectory data for logging purposes. In addition, the model can be used to compute a probabilistic prediction of the pedestrian's location many time steps ahead, and to aid the recognition of unusual behaviour identified as atypical object motion.},
	number = {12},
	urldate = {2016-12-07},
	journal = {Image and Vision Computing},
	author = {Makris, Dimitrios and Ellis, Tim},
	month = oct,
	year = {2002},
	keywords = {Learning paths, People tracking, Route detection, Scene labelling, Track prediction, Video annotation},
	pages = {895--903},
}

@book{frank_life_2000,
	address = {London ; New York},
	edition = {1 edition},
	title = {Life and {Motion} of {Socio}-{Economic} {Units}.},
	isbn = {978-0-7484-0845-0},
	shorttitle = {Life and {Motion} of {Socio}-{Economic} {Units}},
	abstract = {One of the ongoing problems researchers in geography and GIS have is studying data that is inherently spatial over a long period of time. One of the main hurdles they have to overcome is the study of groups of people classified by their socio-economic status (one of the main means for governments, companies and research organisations to group together segments of the population).The amount of data collected by governments, business and research organisations has increased markedly in recent years. Geographic Information Systems have been more widely used than ever before for the storage and analysis of this information. Most GIS can handle this information spatially rather than temporally, and have difficulty with the management of socio-economic time series, which relate to spatial units. Accordingly, this book covers the issues ranging from the formal model to differentiate aspects of spatio-temporal data, through philosophical and fundamental reconsideration of time and space to the development of practical solutions to the problem. This book draws together an interdisciplinary group of scientists in the field of geography, computing, surveying and philosophy. It presents the definitive sourcebook on temporal GIS as applied to socio-economic units.},
	language = {English},
	publisher = {CRC Press},
	editor = {Frank, Andrew and Raper, Jonathan and Cheylan, J. P.},
	month = dec,
	year = {2000},
}

@inproceedings{cudre-mauroux_trajstore_2010,
	title = {Trajstore: {An} {Adaptive} {Storage} {System} for {Very} {Large} {Trajectory} {Data} {Sets}},
	shorttitle = {Trajstore},
	urldate = {2016-12-06},
	booktitle = {2010 {IEEE} 26th {International} {Conference} on {Data} {Engineering} ({ICDE} 2010)},
	publisher = {IEEE},
	author = {Cudre-Mauroux, Philippe and Wu, Eugene and Madden, Samuel},
	year = {2010},
	pages = {109--120},
}

@inproceedings{iwase_tracking_2002,
	title = {Tracking {Soccer} {Player} {Using} {Multiple} {Views}.},
	urldate = {2016-12-07},
	booktitle = {{MVA}},
	author = {Iwase, Sachiko and Saito, Hideo},
	year = {2002},
	pages = {102--105},
}

@incollection{kalnis_discovering_2005,
	title = {On {Discovering} {Moving} {Clusters} in {Spatio}-{Temporal} {Data}},
	booktitle = {{ASTD}},
	publisher = {Springer},
	author = {Kalnis, P. and Mamoulis, N. and Bakiras, S.},
	year = {2005},
	pages = {364--381},
}

@book{miller_geographic_2001,
	title = {Geographic {Data} {Mining} and {Knowledge} {Discovery}},
	publisher = {Taylor \& Francis, Inc.},
	author = {Miller, H. and Han, J.},
	year = {2001},
}

@article{jeung_discovery_2008,
	title = {Discovery of {Convoys} in {Trajectory} {Databases}},
	volume = {1},
	number = {1},
	journal = {VLDB},
	author = {Jeung, H. and Yiu, M. and Zhou, X. and Jensen, C. and Shen, H.},
	year = {2008},
	pages = {1068--1080},
}

@book{vieira_spatio-temporal_2013,
	title = {Spatio-{Temporal} {Databases}: {Complex} {Motion} {Pattern} {Queries}},
	publisher = {Springer},
	author = {Vieira, M. and Tsotras, V.},
	year = {2013},
}

@book{zheng_computing_2011,
	title = {Computing with {Spatial} {Trajectories}},
	publisher = {Springer},
	author = {Zheng, Y. and Zhou, X.},
	year = {2011},
}

@book{leung_knowledge_2010,
	title = {Knowledge {Discovery} in {Spatial} {Data}},
	publisher = {Springer},
	author = {Leung, Y.},
	year = {2010},
}

@article{turdukulov_visual_2014,
	title = {Visual {Mining} of {Moving} {Flock} {Patterns} in {Large} {Spatio}-{Temporal} {Data} {Sets} {Using} a {Frequent} {Pattern} {Approach}},
	volume = {28},
	number = {10},
	journal = {IJGIS},
	author = {Turdukulov, U. and Calderon, A. and Huisman, O. and Retsios, V.},
	year = {2014},
	pages = {2013--2029},
}

@article{wang_efficient_2006,
	title = {Efficient {Mining} of {Group} {Patterns} from {User} {Movement} {Data}},
	volume = {57},
	issn = {0169-023X},
	doi = {10.1016/j.datak.2005.04.006},
	abstract = {In this paper, we present a new approach to derive groupings of mobile users based on their movement data. We assume that the user movement data are collected by logging location data emitted from mobile devices tracking users. We formally define group pattern as a group of users that are within a distance threshold from one another for at least a minimum duration. To mine group patterns, we first propose two algorithms, namely AGP and VG-growth. In our first set of experiments, it is shown when both the number of users and logging duration are large, AGP and VG-growth are inefficient for the mining group patterns of size two. We therefore propose a framework that summarizes user movement data before group pattern mining. In the second series of experiments, we show that the methods using location summarization reduce the mining overheads for group patterns of size two significantly. We conclude that the cuboid based summarization methods give better performance when the summarized database size is small compared to the original movement database. In addition, we also evaluate the impact of parameters on the mining overhead.},
	number = {3},
	urldate = {2014-03-07},
	journal = {Data \& Knowledge Engineering},
	author = {Wang, Yida and Lim, Ee-Peng and Hwang, San-Yih},
	month = jun,
	year = {2006},
	keywords = {Group pattern mining, Location summarization, Mobile data mining},
	pages = {240--282},
}

@mastersthesis{calderon_mining_2011,
	title = {Mining {Moving} {Flock} {Patterns} in {Large} {Spatio}-{Temporal} {Datasets} {Using} a {Frequent} {Pattern} {Mining} {Approach}},
	school = {University of Twente},
	author = {Calderon, A.},
	year = {2011},
}

@incollection{kuntzsch_framework_2013,
	series = {Lecture {Notes} in {Geoinformation} and {Cartography}},
	title = {A {Framework} for {On}-{Line} {Detection} of {Custom} {Group} {Movement} {Patterns}},
	copyright = {©2013 Springer-Verlag Berlin Heidelberg},
	isbn = {978-3-642-34202-8 978-3-642-34203-5},
	abstract = {This chapter describes a lightweight approach for custom definition and detection of group patterns in a real-time analysis scenario, using a simple, yet flexible notion for groups of moving point objects (MPOs) travelling together. Groups are defined as sets of MPOs that are directly or transitively related to each other via freely definable binary relations. Group candidates are identified within a snapshot view of the MPOs at discrete time instances. By backtracking over previous snapshots, stable group compositions over previous time instances are identified and reported. We give insight about the used data structures and algorithms for the group candidate calculation and backtracking steps and illustrate the approach's functionality with examples from a real data set.},
	language = {en},
	urldate = {2014-03-07},
	booktitle = {Progress in {Location}-{Based} {Services}},
	publisher = {Springer Berlin Heidelberg},
	author = {Kuntzsch, Colin and Bohn, Alexander},
	editor = {Krisp, Jukka M.},
	month = jan,
	year = {2013},
	keywords = {Geographical Information Systems/Cartography, Group motion, Information Systems and Communication Service, Pattern Recognition, Real-time movement analysis},
	pages = {91--107},
}

@incollection{jeung_trajectory_2011,
	title = {Trajectory {Pattern} {Mining}},
	booktitle = {Computing with {Spatial} {Trajectories}},
	publisher = {Springer},
	author = {Jeung, H. and Yiu, M. and Jensen, C.},
	year = {2011},
	pages = {143--177},
}

@article{holland_movements_1999,
	title = {Movements of {Tiger} {Sharks} ({Galeocerdo} {Cuvier}) in {Coastal} {Hawaiian} {Waters}},
	volume = {134},
	number = {4},
	journal = {Marine Biology},
	author = {Holland, K. and Wetherbee, B. and Lowe, C. and Meyer, C.},
	year = {1999},
	pages = {665--673},
}

@inproceedings{yu_demonstration_2016,
	title = {A {Demonstration} of {GeoSpark}: {A} {Cluster} {Computing} {Framework} for {Processing} {Big} {Spatial} {Data}},
	booktitle = {{ICDE}},
	author = {Yu, J. and Wu, J. and Sarwat, M.},
	year = {2016},
	pages = {1410--1413},
}

@article{amor_persistence_2016,
	title = {Persistence in {Eye} {Movement} during {Visual} {Search}},
	volume = {6},
	journal = {Scientific Reports},
	author = {Amor, T. and Reis, S. and Campos, D. and Herrmann, H. and Andrade, J.},
	year = {2016},
	pages = {20815},
}

@article{la_sorte_convergence_2016,
	title = {Convergence of {Broad}-{Scale} {Migration} {Strategies} in {Terrestrial} {Birds}},
	volume = {283},
	number = {1823},
	journal = {Royal Society: Biological Sciences},
	author = {La Sorte, F. and Fink, D. and Hochachka, W. and Kelling, S.},
	year = {2016},
	pages = {2588},
}

@article{johnston_abundance_2015,
	title = {Abundance {Models} {Improve} {Spatial} and {Temporal} {Prioritization} of {Conservation} {Resources}},
	volume = {25},
	number = {7},
	urldate = {2016-12-06},
	journal = {Ecological Applications},
	author = {Johnston, Alison and Fink, Daniel and Reynolds, Mark D. and Hochachka, Wesley M. and Sullivan, Brian L. and Bruns, Nicholas E. and Hallstein, Eric and Merrifield, Matt S. and Matsumoto, Sandi and Kelling, Steve},
	year = {2015},
	pages = {1749--1756},
}

@inproceedings{johansson_efficiency_2015,
	title = {An {Efficiency} {Measure} for {Road} {Transportation} {Networks} with {Application} to {Two} {Case} {Studies}},
	urldate = {2016-12-06},
	booktitle = {2015 54th {IEEE} {Conference} on {Decision} and {Control} ({CDC})},
	publisher = {IEEE},
	author = {Johansson, Karl and Terelius, Hakan},
	year = {2015},
	pages = {5149--5155},
}

@article{di_lorenzo_allaboard_2016,
	title = {{AllAboard}: {Visual} {Exploration} of {Cellphone} {Mobility} {Data} to {Optimise} {Public} {Transport}},
	volume = {22},
	number = {2},
	journal = {IEEE TVCG},
	author = {Di Lorenzo, G. and Sbodio, M. and Calabrese, F. and Berlingerio, M. and Pinelli, F. and Nair, R.},
	year = {2016},
	pages = {1036--1050},
}

@incollection{huang_mining_2015,
	title = {Mining {Massive}-{Scale} {Spatiotemporal} {Trajectories} in {Parallel}: {A} {Survey}},
	volume = {9441},
	booktitle = {{TAKDDM}},
	publisher = {Springer},
	author = {Huang, P. and Yuan, B.},
	year = {2015},
}

@phdthesis{valladares_cereceda_gpu_2013,
	type = {{PhD} {Thesis}},
	title = {{GPU} {Parallel} {Algorithms} for {Reporting} {Movement} {Behaviour} {Patterns} in {Spatiotemporal} {Databases}},
	urldate = {2016-06-23},
	author = {Valladares Cereceda, Nacho},
	year = {2013},
}

@book{han_data_2005,
	address = {San Francisco, CA, USA},
	title = {Data {Mining}: {Concepts} and {Techniques}},
	isbn = {1-55860-901-6},
	publisher = {Morgan Kaufmann Publishers Inc.},
	author = {Han, Jiawei},
	year = {2005},
}

@incollection{fournier-viger_spmf_2016,
	title = {The {SPMF} {Open}-{Source} {Data} {Mining} {Library} {Version} 2},
	booktitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}: {European} {Conference}, {ECML} {PKDD} 2016},
	publisher = {Springer},
	author = {Fournier-Viger, Philippe and Lin, Jerry Chun-Wei and Gomariz, Antonio and Gueniche, Ted and Soltani, Azadeh and Deng, Zhihong and Lam, Hoang Thanh},
	editor = {Berendt, Bettina and Bringmann, Björn and Fromont, Élisa and Garriga, Gemma and Miettinen, Pauli and Tatti, Nikolaj and Tresp, Volker},
	year = {2016},
	pages = {36--40},
}

@article{krajzewicz_recent_2012,
	title = {Recent {Development} and {Applications} of {SUMO} - {Simulation} of {Urban} {MObility}},
	volume = {5},
	number = {3\&4},
	journal = {International Journal On Advances in Systems and Measurements},
	author = {Krajzewicz, Daniel and Erdmann, Jakob and Behrisch, Michael and Bieker, Laura},
	month = dec,
	year = {2012},
	pages = {128--138},
}

@article{goethals_advances_2004,
	title = {Advances in {Frequent} {Itemset} {Mining} {Implementations}: {Report} of {FIMI}'03},
	volume = {6},
	number = {1},
	journal = {ACM SIGKDD Explorations},
	author = {Goethals, B. and Zaki, M. J.},
	month = jun,
	year = {2004},
	note = {Publisher: ACM},
	pages = {109--117},
}

@inproceedings{gudmundsson_computing_2006,
	title = {Computing {Longest} {Duration} {Flocks} in {Trajectory} {Data}},
	booktitle = {{ACM} {SIGSPATIAL}},
	author = {Gudmundsson, J. and van Kreveld, M.},
	year = {2006},
	pages = {35--42},
}

@inproceedings{uno_lcm_2004,
	title = {{LCM} {Ver}. 2: {Efficient} {Mining} {Algorithms} for {Frequent}/{Closed}/{Maximal} {Itemsets}},
	volume = {126},
	shorttitle = {{LCM} {Ver}. 2},
	urldate = {2017-03-04},
	booktitle = {Fimi},
	author = {Uno, Takeaki and Kiyomi, Masashi and Arimura, Hiroki},
	year = {2004},
}

@article{li_swarm_2010,
	title = {Swarm: {Mining} relaxed temporal moving object clusters},
	volume = {3},
	number = {1-2},
	journal = {VLDB},
	author = {Li, Z. and Ding, B. and Han, J. and Kays, R.},
	year = {2010},
	pages = {723--734},
}

@inproceedings{vieira_-line_2009,
	title = {On-{Line} {Discovery} of {Flock} {Patterns} in {Spatio}-{Temporal} {Data}},
	booktitle = {{ACM} {SIGSPATIAL}},
	author = {Vieira, M. and Bakalov, P. and Tsotras, V.},
	year = {2009},
	pages = {286--295},
}

@misc{mokbel_towards_2023,
	title = {Towards {Mobility} {Data} {Science} ({Vision} {Paper})},
	url = {http://arxiv.org/abs/2307.05717},
	abstract = {Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.},
	urldate = {2023-07-15},
	publisher = {arXiv},
	author = {Mokbel, Mohamed and Sakr, Mahmoud and Xiong, Li and Züfle, Andreas and Almeida, Jussara and Aref, Walid and Andrienko, Gennady and Andrienko, Natalia and Cao, Yang and Chawla, Sanjay and Cheng, Reynold and Chrysanthis, Panos and Fei, Xiqi and Ghinita, Gabriel and Graser, Anita and Gunopulos, Dimitrios and Jensen, Christian and Kim, Joon-Sook and Kim, Kyoung-Sook and Kröger, Peer and Krumm, John and Lauer, Johannes and Magdy, Amr and Nascimento, Mario and Ravada, Siva and Renz, Matthias and Sacharidis, Dimitris and Shahabi, Cyrus and Salim, Flora and Sarwat, Mohamed and Schoemans, Maxime and Speckmann, Bettina and Tanin, Egemen and Theodoridis, Yannis and Torp, Kristian and Trajcevski, Goce and van Kreveld, Marc and Wenk, Carola and Werner, Martin and Wong, Raymond and Wu, Song and Xu, Jianqiu and Youssef, Moustafa and Zeinalipour, Demetris and Zhang, Mengxuan and Zimányi, Esteban},
	month = jun,
	year = {2023},
	note = {arXiv:2307.05717 [cs]},
	keywords = {Computer Science - Other Computer Science},
}

@inproceedings{tanaka_efficient_2015,
	title = {Efficient {Algorithms} to {Discover} {Flock} {Patterns} in {Trajectories}},
	url = {https://www.semanticscholar.org/paper/Efficient-Algorithms-to-Discover-Flock-Patterns-in-Tanaka-Vieira/3fc695ffe5e14880cb26b2130a4168a69536dc04},
	abstract = {With the ubiquitous use of location enabled devices, pattern discovery in trajectories has been receiving increasing interest. Among such patterns, we have queries related to how groups of moving objects behave over time such as discovering flocks. A flock pattern is defined as a set of moving objects that move within a predefined distance to each other for a given continuous period of time. A typical application example is surveillance, where relies on discovering flocks on very large streaming spatiotemporal data efficiently. Previous work presented a polynomial solution to the problem of finding flocks with fixed time duration. And presented as well a set of algorithms based on this solution, which are the state-of-the-art algorithms regarding this problem. In this paper, we improve those algorithms by applying the plane sweeping technique in conjunction to an inverted index. The plane sweeping accelerates the detection of groups of objects that are candidates to be a flock in a time instant and the inverted index is used to compare candidate disks across time instants quickly. Using an assortment of real-world trajectory datasets, we show that our proposed methods are very efficient. When compared with the baseline flock algorithm, our proposed methods achieved up to 46x speedup reducing the elapsed time from thousands of seconds to milliseconds.},
	urldate = {2023-07-03},
	author = {Tanaka, Pedro Sena and Vieira, Marcos R. and Kaster, D. S.},
	year = {2015},
}

@article{tanaka_improved_2016-1,
	title = {An {Improved} {Base} {Algorithm} for {Online} {Discovery} of {Flock} {Patterns} in {Trajectories}},
	volume = {7},
	copyright = {Copyright (c) 2021 Journal of Information and Data Management},
	issn = {2178-7107},
	url = {https://sol.sbc.org.br/journals/index.php/jidm/article/view/1576},
	doi = {10.5753/jidm.2016.1576},
	abstract = {The high availability, cost, and usage of location-aware devices have increased the interest in the research of spatiotemporal patterns. The main goal in studying such patterns is to discover spatial relationships over time between moving objects. Recent articles have proposed a wide variety of such patterns, among them is the flock pattern. This pattern is defined as a set of moving objects with minimum size that stay together within a maximum distance for a continuous period of time. Typical application examples are monitoring and surveillance, which rely on efficiently identifying groups of suspicious people/vehicles in large spatiotemporal streaming data. Previous works proposed polynomial-time algorithms to the flock pattern problem with fixed time duration. In this article, we propose a new online method, called PSI, which is an improved base method to discover flock patterns that applies computational geometry techniques (e.g., plane sweeping) along with binary signatures and inverted indexes. In summary, plane sweeping speeds up the detection of candidate groups in a particular timestamp, binary signatures allow saving costly set intersection operations, and inverted indexes are employed to quickly compare candidate disks across timestamps. Using a variety of real-world datasets and a large synthetic one we show that our proposed methods are efficient compared to state-of-the-art solution. In our experimental evaluation our proposed method achieved up to 69 times speedup compared to the previous solution.},
	language = {en},
	number = {1},
	urldate = {2023-07-03},
	journal = {Journal of Information and Data Management},
	author = {Tanaka, Pedro Sena and Vieira, Marcos R. and Kaster, Daniel S.},
	month = oct,
	year = {2016},
	note = {Number: 1},
	keywords = {plane sweep},
	pages = {52--52},
}

@inproceedings{sanches_top-down_2018,
	address = {Cham},
	series = {Lecture {Notes} in {Geoinformation} and {Cartography}},
	title = {A {Top}-{Down} {Algorithm} with {Free} {Distance} {Parameter} for {Mining} {Top}-k {Flock} {Patterns}},
	isbn = {978-3-319-78208-9},
	doi = {10.1007/978-3-319-78208-9_12},
	abstract = {Spatiotemporal data is becoming more and more available due to the increase in the using of location-based systems. With such data, important information can be retrieved, where co-movement patterns stand out in finding groups of moving objects moving together. However, such pattern mining algorithms are not simple and commonly require non-trivial fixed parameters as input, which are extremely dependent on the data domain and also impacted by many others context variables, being such challenging task also to domain specialists. One example of these patterns is the flock pattern that has as its most challenging parameter the distance threshold that is the size of the disks that involves the objects. Although other density-based approaches reduce the impact of the restrictions of the disk, all of them still require a distance parameter for the density connectedness. Addressing this problem, we introduce the concept of discovering of k-co-movement patterns, which is finding the top-k patterns, according to the desired raking criterion. Especially for the flock pattern, we also define a new flock pattern query and propose a top-down algorithm with free distance parameter for the aforementioned problem.},
	language = {en},
	booktitle = {Geospatial {Technologies} for {All}},
	publisher = {Springer International Publishing},
	author = {Sanches, Denis Evangelista and Alvares, Luis O. and Bogorny, Vania and Vieira, Marcos R. and Kaster, Daniel S.},
	editor = {Mansourian, Ali and Pilesjö, Petter and Harrie, Lars and van Lammeren, Ron},
	year = {2018},
	keywords = {Co-movement patterns, Flock discovery, Flock pattern, Free diameter parameter, Top-down algorithm, Top-k flocks},
	pages = {233--249},
}

@article{fan_general_2016,
	title = {A general and parallel platform for mining co-movement patterns over large-scale trajectories},
	volume = {10},
	issn = {2150-8097},
	url = {https://dl.acm.org/doi/10.14778/3025111.3025114},
	doi = {10.14778/3025111.3025114},
	abstract = {Discovering co-movement patterns from large-scale trajectory databases is an important mining task and has a wide spectrum of applications. Previous studies have identified several types of interesting co-movement patterns and showcased their usefulness. In this paper, we make two key contributions to this research field. First, we propose a more general co-movement pattern to unify those defined in the past literature. Second, we propose two types of parallel and scalable frameworks and deploy them on Apache Spark. To the best of our knowledge, this is the first work to mine co-movement patterns in real life trajectory databases with hundreds of millions of points. Experiments on three real life large-scale trajectory datasets have verified the efficiency and scalability of our proposed solutions.},
	language = {en},
	number = {4},
	urldate = {2022-04-07},
	journal = {Proceedings of the VLDB Endowment},
	author = {Fan, Qi and Zhang, Dongxiang and Wu, Huayu and Tan, Kian-Lee},
	month = nov,
	year = {2016},
	pages = {313--324},
}

@article{graser_exploratory_2021,
	title = {An exploratory data analysis protocol for identifying problems in continuous movement data},
	volume = {0},
	issn = {1748-9725},
	url = {https://doi.org/10.1080/17489725.2021.1900612},
	doi = {10.1080/17489725.2021.1900612},
	abstract = {Movement datasets are often complex and require sophisticated processing and analysis. A thorough understanding of the dataset is needed to choose the right methods and to interpret their results. Misunderstandings and violations of assumptions about dataset characteristics can lead to flawed analysis results and wrong conclusions. To address this challenge, we propose a novel protocol for the systematic exploration of movement datasets. The individual protocol steps address the different types of movement data problems. The exploration tools recommended at each step are specifically tailored to identifying potential problems and avoiding common pitfalls when working with global navigation satellite system (GNSS) tracking data, commonly referred to as GPS tracks. However, the general steps should be transferable to continuous movement datasets with different characteristics, such as video trajectories. Furthermore, we provide an open-source implementation of our protocol in the form of a Jupyter notebook accompanying this paper.},
	number = {0},
	urldate = {2021-03-17},
	journal = {Journal of Location Based Services},
	author = {Graser, A.},
	month = mar,
	year = {2021},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/17489725.2021.1900612},
	keywords = {Exploratory data analysis, movement analytics, movement data, trajectory analysis},
	pages = {1--29},
}

@inproceedings{welzl_smallest_1991-1,
	address = {Berlin, Heidelberg},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Smallest enclosing disks (balls and ellipsoids)},
	isbn = {978-3-540-46457-0},
	doi = {10.1007/BFb0038202},
	abstract = {A simple randomized algorithm is developed which computes the smallest enclosing disk of a finite set of points in the plane in expected linear time. The algorithm is based on Seidel's recent Linear Programming algorithm, and it can be generalized to computing smallest enclosing balls or ellipsoids of point sets in higher dimensions in a straightforward way. Experimental results of an implementation are presented.},
	language = {en},
	booktitle = {New {Results} and {New} {Trends} in {Computer} {Science}},
	publisher = {Springer},
	author = {Welzl, Emo},
	editor = {Maurer, Hermann},
	year = {1991},
	keywords = {Average Runtime, Closed Disk, Expected Running Time, Random Permutation, Small Disk},
	pages = {359--370},
}

@article{brscic_person_2013,
	title = {Person {Tracking} in {Large} {Public} {Spaces} {Using} 3-{D} {Range} {Sensors}},
	volume = {43},
	issn = {2168-2291},
	doi = {10.1109/THMS.2013.2283945},
	abstract = {A method for tracking the position, orientation, and height of persons in large public environments is presented. Such a piece of information is known to be useful both for understanding their actions, as well as for applications such as human-robot interaction. We use multiple 3-D range sensors, which are mounted above human height to have less occlusion between persons. A computationally simple-tracking method is proposed that works on single sensor data and combines multiple sensors so that large areas can be covered with a minimum number of sensors. Moreover, it can work with different sensor types and is robust to the imperfect sensor measurements; therefore, it is possible to combine currently available 3-D range sensor solutions to achieve tracking in wide public spaces. The method was implemented in a shopping center environment, and it was shown that good tracking performance can be achieved.},
	number = {6},
	journal = {IEEE Transactions on Human-Machine Systems},
	author = {Brščić, D. and Kanda, T. and Ikeda, T. and Miyashita, T.},
	month = nov,
	year = {2013},
	keywords = {3-D range sensors, 3D range sensor solutions, 3D range sensors, Cameras, Laser applications, Robots, Robustness, Sensor phenomena and characterization, Sensor systems, Tracking, distance measurement, height tracking, human-robot interaction, imperfect sensor measurements, orientation tracking, person tracking, position tracking, sensors, shopping center environment, simple-tracking method, tracking},
	pages = {522--534},
}

@misc{noauthor_atc_nodate,
	title = {{ATC} shopping center tracking dataset},
	url = {https://irc.atr.jp/crest2010_HRI/ATC_dataset/},
	urldate = {2019-06-17},
}

@inproceedings{hagedorn_big_2017,
	title = {Big {Spatial} {Data} {Processing} {Frameworks}: {Feature} and {Performance} {Evaluation}},
	shorttitle = {Big {Spatial} {Data} {Processing} {Frameworks}},
	doi = {10.5441/002/edbt.2017.52},
	abstract = {Nowadays, a vast amount of data is generated and collected every moment and often, this data has a spatial and/or temporal aspect. To analyze the massive data sets, big data platforms like Apache Hadoop MapReduce and Apache Spark emerged and extensions that take the spatial characteristics into account were created for them. In this paper, we analyze and compare existing solutions for spatial data processing on Hadoop and Spark. In our comparison, we investigate their features as well as their performances in a micro benchmark for spatial filter and join queries. Based on the results and our experiences with these frameworks, we outline the requirements for a general spatio-temporal benchmark for Big Spatial Data processing platforms and sketch first solutions to the identified problems.},
	booktitle = {{EDBT}},
	author = {Hagedorn, Stefan and Götze, Philipp and Sattler, Kai-Uwe},
	year = {2017},
	keywords = {Apache Hadoop, Apache Spark, Benchmark (computing), Big data, MapReduce, Performance Evaluation, Requirement},
}

@inproceedings{alam_performance_2018,
	address = {New York, NY, USA},
	series = {{BigSpatial} 2018},
	title = {A {Performance} {Study} of {Big} {Spatial} {Data} {Systems}},
	isbn = {978-1-4503-6041-8},
	url = {http://doi.acm.org/10.1145/3282834.3282841},
	doi = {10.1145/3282834.3282841},
	abstract = {With the accelerated growth in spatial data volume, being generated from a wide variety of sources, the need for efficient storage, retrieval, processing and analyzing of spatial data is ever more important. Hence, spatial data processing system has become an important field of research. In recent times a number of Big Spatial Data systems have been proposed by researchers around the world. These systems can be roughly categorized into Apache Hadoop-based and in-memory systems based on Apache Spark. The available features supported by these systems vary widely. However, there has not been any comprehensive evaluation study of these systems in terms of performance, scalability and functionality. To address this need, we propose a benchmark to evaluate Big Spatial Data systems. Although, Spark is a very popular framework, its performance is limited by the overhead associated with distributed resource management and coordination. The Big Spatial Data systems that are based on Spark, are also constrained by these. We introduce SpatialIgnite, a Big Spatial Data system that we have developed based on Apache Ignite. We investigate the present status of the Big Spatial Data systems by conducting a comprehensive feature analysis and performance evaluation of a few representative systems with our benchmark. Our study shows that SpatialIgnite performs better than Hadoop and Spark based systems that we have evaluated.},
	urldate = {2019-06-18},
	booktitle = {Proceedings of the 7th {ACM} {SIGSPATIAL} {International} {Workshop} on {Analytics} for {Big} {Geospatial} {Data}},
	publisher = {ACM},
	author = {Alam, Md Mahbub and Ray, Suprio and Bhavsar, Virendra C.},
	year = {2018},
	note = {event-place: Seattle, WA, USA},
	keywords = {Benchmark, Big Spatial Data, Hadoop, Ignite, In-Memory, Performance Evaluation, Spark},
	pages = {1--9},
}

@book{zheng_computing_2011-1,
	edition = {1st},
	title = {Computing with {Spatial} {Trajectories}},
	isbn = {978-1-4614-1628-9},
	abstract = {Spatial trajectories have been bringing the unprecedented wealth to a variety of research communities. A spatial trajectory records the paths of a variety of moving objects, such as people who log their travel routes with GPS trajectories. The field of moving objects related research has become extremely active within the last few years, especially with all major database and data mining conferences and journals. Computing with Spatial Trajectories introduces the algorithms, technologies, and systems used to process, manage and understand existing spatial trajectories for different applications. This book also presents an overview on both fundamentals and the state-of-the-art research inspired by spatial trajectory data, as well as a special focus on trajectory pattern mining, spatio-temporal data mining and location-based social networks. Each chapter provides readers with a tutorial-style introduction to one important aspect of location trajectory computing, case studies and many valuable references to other relevant research work. Computing with Spatial Trajectories is designed as a reference or secondary text book for advanced-level students and researchers mainly focused on computer science and geography. Professionals working on spatial trajectory computing will also find this book very useful.},
	publisher = {Springer Publishing Company, Incorporated},
	author = {Zheng, Yu and Zhou, Xiaofang},
	year = {2011},
}

@article{demsar_establishing_2021,
	title = {Establishing the integrated science of movement: bringing together concepts and methods from animal and human movement analysis},
	volume = {0},
	issn = {1365-8816},
	shorttitle = {Establishing the integrated science of movement},
	url = {https://doi.org/10.1080/13658816.2021.1880589},
	doi = {10.1080/13658816.2021.1880589},
	abstract = {Movement analysis has become an integral part of many disciplines, yet with relatively little overlap. A foresight paper in this journal entitled “Towards an integrated science of movement: converging research on animal movement ecology and human mobility science” argued for a better integration of concepts across the divide of animal and human movement, which would lead to the Integrated Science of Movement, but did so from a top-down perspective based on a series of expert workshops. We argue that for a solid establishment of the Integrated Science of Movement, a bottom-up approach is necessary, one based on existing literature which identifies similarities and differences across disciplines. We therefore review, compare, and contrast movement analysis methodologies from GIScience, movement ecology, geography, transportation, public health, computer science, and physics. We structure our review along the dichotomy of individual versus population-based movement or, using terminology from wildlife ecology, between the Lagrangian and Eulerian perspectives. We further introduce a new unifying framework for movement research that is sufficiently general to cover any type of movement study in any discipline and that spans the Lagrangian/Eulerian divide, with the ambitious goal to bridge the gap between disciplines and lay a solid foundation for a new Integrated Science of Movement.},
	number = {0},
	urldate = {2021-02-25},
	journal = {International Journal of Geographical Information Science},
	author = {Demšar, Urška and Long, Jed A. and Benitez-Paez, Fernando and Bastos, Vanessa Brum and Marion, Solène and Martin, Gina and Sekulić, Sebastijan and Smolak, Kamil and Zein, Beate and Siła-Nowicka, Katarzyna},
	month = feb,
	year = {2021},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/13658816.2021.1880589},
	keywords = {GIScience, Movement analysis, animal movement, human mobility, interdisciplinary review},
	pages = {1--36},
}

@inproceedings{vieira_-line_2009-1,
	address = {New York, NY, USA},
	series = {{GIS} '09},
	title = {On-line discovery of flock patterns in spatio-temporal data},
	isbn = {978-1-60558-649-6},
	url = {https://doi.org/10.1145/1653771.1653812},
	doi = {10.1145/1653771.1653812},
	abstract = {With the recent advancements and wide usage of location detection devices, large quantities of data are collected by GPS and cellular technologies in the form of trajectories. While most previous work on trajectory-based queries has concentrated on traditional range, nearest-neighbor and similarity queries, there is an increasing interest in queries that capture the "aggregate" behavior of trajectories as groups. Consider, for example, finding groups of moving objects that move "together", i.e. within a predefined distance to each other, for a certain continuous period of time. Such queries typically arise in surveillance applications, e.g. identify groups of suspicious people, convoys of vehicles, flocks of animals, etc. In this paper we first show that the on-line flock discovery problem is polynomial and then propose a framework and several strategies to discover such patterns in streaming spatio-temporal data. Experiments with real and synthetic trajectorial datasets show that the proposed algorithms are efficient and scalable.},
	urldate = {2021-02-16},
	booktitle = {Proceedings of the 17th {ACM} {SIGSPATIAL} {International} {Conference} on {Advances} in {Geographic} {Information} {Systems}},
	publisher = {Association for Computing Machinery},
	author = {Vieira, Marcos R. and Bakalov, Petko and Tsotras, Vassilis J.},
	month = nov,
	year = {2009},
	keywords = {moving objects, spatio-temporal patterns},
	pages = {286--295},
}

@article{cai_discovering_2020,
	title = {Discovering regions of anomalous spatial co-locations},
	volume = {0},
	issn = {1365-8816},
	url = {https://doi.org/10.1080/13658816.2020.1830998},
	doi = {10.1080/13658816.2020.1830998},
	abstract = {Regions of anomalous spatial co-locations (ROASCs) are regions where co-locations between two different features are significantly stronger or weaker than expected. ROASC discovery can provide useful insights for studying unexpected spatial associations at regional scales. The main challenges are that the ROASCs are spatially arbitrary in geographic shape and the distributions of spatial features are unknown a priori. To avoid restrictive assumptions regarding the distribution of data, we propose a distribution-free method for discovering arbitrarily shaped ROASCs. First, we present a multidirectional optimization method to adaptively identify the candidate ROASCs, whose sizes and shapes are fully endogenized. Furthermore, the validity of the candidates is evaluated through significance tests under the null hypothesis that the expected spatial co-locations between two features occur consistently across space. To effectively model the null hypothesis, we develop a bivariate pattern reconstruction method by reconstructing the spatial auto- and cross-correlation structures observed in the data. Synthetic experiments and a case study conducted using Shanghai taxi datasets demonstrate the advantages of our method, in terms of effectiveness, over an available alternative method.},
	number = {0},
	urldate = {2020-11-16},
	journal = {International Journal of Geographical Information Science},
	author = {Cai, Jiannan and Deng, Min and Guo, Yiwen and Xie, Yiqun and Shekhar, Shashi},
	month = nov,
	year = {2020},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/13658816.2020.1830998},
	keywords = {Spatial data mining, anomalous spatial co-locations, multiple significance tests, pattern reconstruction, region detection},
	pages = {1--25},
}

@article{cazals_note_2008,
	title = {A note on the problem of reporting maximal cliques},
	volume = {407},
	issn = {03043975},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0304397508003903},
	doi = {10.1016/j.tcs.2008.05.010},
	abstract = {Reporting the maximal cliques of a graph is a fundamental problem arising in many areas. This note bridges the gap between three papers addressing this problem: the original paper of Bron–Kerbosh [C. Bron, J. Kerbosch, Algorithm 457: Finding all cliques of an undirected graph, Communication of ACM 16 (9) (1973) 575–577], and two papers recently published in TCS, namely that of Tomita et al. [Tomita, A. Tanaka, H. Takahashi, The worst-case time complexity for generating all maximal cliques and computational experiments, Theoretical Computer Science 363 (1) (2006) 28–42], and that of Koch [I. Koch, Fundamental study: Enumerating all connected maximal common subgraphs in two graphs, Theoretical Computer Science 250 (1–2) (2001) 1–30]. In particular, we show that the strategy of Tomita et al. is a simple modification of the Bron–Kerbosch algorithm, based on an (un-exploited) observation raised in Koch’s paper.},
	language = {en},
	number = {1-3},
	urldate = {2020-11-06},
	journal = {Theoretical Computer Science},
	author = {Cazals, F. and Karande, C.},
	month = nov,
	year = {2008},
	pages = {564--568},
}

@article{prosser_exact_2012,
	title = {Exact {Algorithms} for {Maximum} {Clique}: {A} {Computational} {Study}},
	volume = {5},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	shorttitle = {Exact {Algorithms} for {Maximum} {Clique}},
	url = {https://www.mdpi.com/1999-4893/5/4/545},
	doi = {10.3390/a5040545},
	abstract = {We investigate a number of recently reported exact algorithms for the maximum clique problem. The program code is presented and analyzed to show how small changes in implementation can have a drastic effect on performance. The computational study demonstrates how problem features and hardware platforms influence algorithm behaviour. The effect of vertex ordering is investigated. One of the algorithms (MCS) is broken into its constituent parts and we discover that one of these parts frequently degrades performance. It is shown that the standard procedure used for rescaling published results (i.e., adjusting run times based on the calibration of a standard program over a set of benchmarks) is unsafe and can lead to incorrect conclusions being drawn from empirical data.},
	language = {en},
	number = {4},
	urldate = {2020-11-06},
	journal = {Algorithms},
	author = {Prosser, Patrick},
	month = dec,
	year = {2012},
	note = {Number: 4
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {empirical study, exact algorithms, maximum clique},
	pages = {545--587},
}

@incollection{goos_fast_2003,
	address = {Berlin, Heidelberg},
	title = {Fast {Smallest}-{Enclosing}-{Ball} {Computation} in {High} {Dimensions}},
	volume = {2832},
	isbn = {978-3-540-20064-2 978-3-540-39658-1},
	url = {http://link.springer.com/10.1007/978-3-540-39658-1_57},
	abstract = {We develop a simple combinatorial algorithm for computing the smallest enclosing ball of a set of points in high dimensional Euclidean space. The resulting code is in most cases faster (sometimes significantly) than recent dedicated methods that only deliver approximate results, and it beats off-the-shelf solutions, based e.g. on quadratic programming solvers.},
	language = {en},
	urldate = {2020-10-09},
	booktitle = {Algorithms - {ESA} 2003},
	publisher = {Springer Berlin Heidelberg},
	author = {Fischer, Kaspar and Gärtner, Bernd and Kutz, Martin},
	editor = {Goos, Gerhard and Hartmanis, Juris and van Leeuwen, Jan and Di Battista, Giuseppe and Zwick, Uri},
	year = {2003},
	doi = {10.1007/978-3-540-39658-1_57},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {630--641},
}

@article{eppstein_listing_2011,
	title = {Listing {All} {Maximal} {Cliques} in {Large} {Sparse} {Real}-{World} {Graphs}},
	url = {http://arxiv.org/abs/1103.0318},
	abstract = {We implement a new algorithm for listing all maximal cliques in sparse graphs due to Eppstein, L{\textbackslash}"offler, and Strash (ISAAC 2010) and analyze its performance on a large corpus of real-world graphs. Our analysis shows that this algorithm is the first to offer a practical solution to listing all maximal cliques in large sparse graphs. All other theoretically-fast algorithms for sparse graphs have been shown to be significantly slower than the algorithm of Tomita et al. (Theoretical Computer Science, 2006) in practice. However, the algorithm of Tomita et al. uses an adjacency matrix, which requires too much space for large sparse graphs. Our new algorithm opens the door for fast analysis of large sparse graphs whose adjacency matrix will not fit into working memory.},
	urldate = {2020-10-09},
	journal = {arXiv:1103.0318 [cs]},
	author = {Eppstein, David and Strash, Darren},
	month = mar,
	year = {2011},
	note = {arXiv: 1103.0318},
	keywords = {Computer Science - Data Structures and Algorithms, F.2.2, G.2.2},
}

@incollection{cheong_listing_2010,
	address = {Berlin, Heidelberg},
	title = {Listing {All} {Maximal} {Cliques} in {Sparse} {Graphs} in {Near}-{Optimal} {Time}},
	volume = {6506},
	isbn = {978-3-642-17516-9 978-3-642-17517-6},
	url = {http://link.springer.com/10.1007/978-3-642-17517-6_36},
	abstract = {The degeneracy of an n-vertex graph G is the smallest number d such that every subgraph of G contains a vertex of degree at most d. We show that there exists a nearly-optimal fixed-parameter tractable algorithm for enumerating all maximal cliques, parametrized by degeneracy. To achieve this result, we modify the classic Bron–Kerbosch algorithm and show that it runs in time O(dn3d/3). We also provide matching upper and lower bounds showing that the largest possible number of maximal cliques in an n-vertex graph with degeneracy d (when d is a multiple of 3 and n ≥ d + 3) is (n − d)3d/3. Therefore, our algorithm matches the Θ (d(n − d)3d/3) worst-case output size of the problem whenever n − d = Ω (n).},
	language = {en},
	urldate = {2020-10-09},
	booktitle = {Algorithms and {Computation}},
	publisher = {Springer Berlin Heidelberg},
	author = {Eppstein, David and Löffler, Maarten and Strash, Darren},
	editor = {Cheong, Otfried and Chwa, Kyung-Yong and Park, Kunsoo},
	year = {2010},
	doi = {10.1007/978-3-642-17517-6_36},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {403--414},
}

@article{bron_algorithm_1973-1,
	title = {Algorithm 457: finding all cliques of an undirected graph},
	volume = {16},
	issn = {0001-0782, 1557-7317},
	shorttitle = {Algorithm 457},
	url = {https://dl.acm.org/doi/10.1145/362342.362367},
	doi = {10.1145/362342.362367},
	abstract = {Coen Bron* and Joep Kerboscht [Recd. 27 April 1971 and 23 August 1971] * Department of Mathematics t Department of Industrial Engineering, Technological University Eindhoven, P.O. Box 513, Eindhoven, The Netherlands Present address of C. Bron: Department of Electrical Engineering, Twente University of Technology, P.O. Box 217, Enschade, The Netherlands.},
	language = {en},
	number = {9},
	urldate = {2020-10-09},
	journal = {Communications of the ACM},
	author = {Bron, Coen and Kerbosch, Joep},
	month = sep,
	year = {1973},
	pages = {575--577},
}

@article{samudrala_graph-theoretic_nodate,
	title = {A {Graph}-theoretic {Algorithm} for {Comparative} {Modeling} of {Protein} {Structure}},
	language = {en},
	author = {Samudrala, R and Moult, J},
	pages = {16},
}

@article{tomita_simple_2013,
	title = {A {Simple} and {Faster} {Branch}-and-{Bound} {Algorithm} for {Finding} a {Maximum} {Clique} with {Computational} {Experiments}},
	volume = {E96.D},
	url = {https://www.jstage.jst.go.jp/article/transinf/E96.D/6/E96.D_1286/_article},
	doi = {10.1587/transinf.E96.D.1286},
	number = {6},
	urldate = {2020-11-06},
	journal = {IEICE Transactions on Information and Systems},
	author = {Tomita, Etsuji and Sutani, Yoichi and Higashi, Takanori and Wakatsuki, Mitsuo},
	year = {2013},
	pages = {1286--1298},
}

@article{tomita_worst-case_2006,
	title = {The worst-case time complexity for generating all maximal cliques and computational experiments},
	volume = {363},
	issn = {03043975},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0304397506003586},
	doi = {10.1016/j.tcs.2006.06.015},
	abstract = {We present a depth-first search algorithm for generating all maximal cliques of an undirected graph, in which pruning methods are employed as in the Bron–Kerbosch algorithm. All the maximal cliques generated are output in a tree-like form. Subsequently, we prove that its worst-case time complexity is O(3n/3) for an n-vertex graph. This is optimal as a function of n, since there exist up to 3n/3 maximal cliques in an n-vertex graph. The algorithm is also demonstrated to run very fast in practice by computational experiments.},
	language = {en},
	number = {1},
	urldate = {2020-10-09},
	journal = {Theoretical Computer Science},
	author = {Tomita, Etsuji and Tanaka, Akira and Takahashi, Haruhisa},
	month = oct,
	year = {2006},
	pages = {28--42},
}