Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories PDF
Preview Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories
SPRINGER BRIEFS IN COMPUTER SCIENCE Berkay Aydin · Rafal A. Angryk Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories 1 23 SpringerBriefs in Computer Science Serieseditors StanZdonik,BrownUniversity,Providence,RhodeIsland,USA ShashiShekhar,UniversityofMinnesota,Minneapolis,Minnesota,USA XindongWu,UniversityofVermont,Burlington,Vermont,USA LakhmiC.Jain,UniversityofSouthAustralia,Adelaide,SouthAustralia,Australia DavidPadua,UniversityofIllinoisUrbana-Champaign,Urbana,Illinois,USA XueminShermanShen,UniversityofWaterloo,Waterloo,Ontario,Canada BorkoFurht,FloridaAtlanticUniversity,BocaRaton,Florida,USA V.S.Subrahmanian,UniversityofMaryland,CollegePark,Maryland,USA MartialHebert,CarnegieMellonUniversity,Pittsburgh,Pennsylvania,USA KatsushiIkeuchi,UniversityofTokyo,Tokyo,Japan BrunoSiciliano,UniversitàdiNapoliFedericoII,Napoli,Italy SushilJajodia,GeorgeMasonUniversity,Fairfax,Virginia,USA Newton Lee, Institute for Education, Research, and Scholarships in Los Angeles, California,USA SpringerBriefs present concise summaries of cutting-edge research and practical applicationsacrossawidespectrumoffields.Featuringcompactvolumesof50to 125pages,theseriescoversarangeofcontentfromprofessionaltoacademic. 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Angryk Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories 123 BerkayAydin RafalA.Angryk DepartmentofComputerScience DepartmentofComputerScience GeorgiaStateUniversity GeorgiaStateUniversity Atlanta,GA,USA Atlanta,GA,USA ISSN2191-5768 ISSN2191-5776 (electronic) SpringerBriefsinComputerScience ISBN978-3-319-99872-5 ISBN978-3-319-99873-2 (eBook) https://doi.org/10.1007/978-3-319-99873-2 LibraryofCongressControlNumber:2018955469 ©TheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerlandAG2018 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland To mywife, Yagmur,who relentlessly supportsmethroughthickand thin. Berkay Aydin To allwonderfulwomen ofmylife: Aleksandra,Izabela,Gabriella,Nina, and Ella.Thankyou. Rafal A. Angryk Preface Oftenmisunderstood,knowledgediscoveryisaninherentlyinterdisciplinaryfield. Itencompassestheentireprocessofdiggingthroughthevastamountsofdatafrom various domains, analyzing these large-scale datasets, and extracting the meaning hidden within them. It helps us understand the underlying relationships, uncover insightfulpatterns,andactbasedonourdiscoveries.Westronglybelievethatallthe bigdatahype,comingtousrecentlyfromadynamicallygrowingbusinessworld,is justthetipoftheiceberg.Beyondit,therearevastamountsoftrulyinterconnected andincreasinglycomplexdatasets. Inthis book,we unveiljusta small, butin our opinion very useful part of data mining—spatiotemporal frequent pattern mining fromevolvingregiontrajectories. Evolving region trajectory is our base data type, providing foundation for majority of the presented works. We use this data type to represent multiple spatial objects that continuously change their shapes and locations over time. Such dynamically evolving spatial objects frequently occur in multiple scientific domains,suchasheliophysics,biology,andmedicine.Inthisbookwewilldiscuss differentrelationships between evolving regiontrajectories, methodsto assess the significance of these relationships, and algorithms, which we developed to mine them. Atlanta,GA,USA BerkayAydin July2018 RafalA.Angryk vii Acknowledgments Thisbookhasbeendevelopedoutofaseriesofstudies,whichwehaveconducted over the recent years. Many of our papers and research investigations have been inspired or influenced by our coworkers and more distant collaborators. We are grateful to our past and current coworkers, Piet Martens, Karthik Ganesan Pillai, Micheal Schuh, Dustin Kempton, Juan Banda, Doug Galarus, Tim Wylie, Stuart Jeffries,RuizheMa,SoukainaFilaliBoubrahimi,AhmetKucuk,ShahMuhammad Hamdi, Vijay Akkineni, Sajitha Naduvil-Vadukootu,and Azim Ahmadzadeh. We also thank our remote collaborators,specifically Jack Ireland, Kathy Reeves, Pete Riley, Joe Gurman,Craig DeForest, Gelu Nita, AndresMunoz-Jaramillo,Alisdair Davey,VeroniqueDelouille,andKevinReardon. WewouldliketothanktheNationalScienceFoundation(NSF),NationalAero- nauticsandSpaceAdministration(NASA),andGeorgiaStateUniversity(GSU)for generouslysupportingour research. The research presentedin this bookhas been supportedin parts by fundingfrom the Division of AdvancedCyberinfrastructure withintheDirectorateforComputerandInformationScienceandEngineering,the Division of Astronomical Sciences within the Directorate for Mathematical and PhysicalSciences,andtheDivisionofAtmosphericandGeospaceScienceswithin theDirectorateforGeosciences,underNSFawards#1443061and#1812964.Itwas also supported in parts by funding from NASA through awards #NNX09AB03G, #NNX11AM13A,and#NNX15AF39G.WehavealsobenefitedfromGeorgiaState University’sSecondCenturyInitiativeandNextGenerationprograms. Any opinions expressed herein are those of the authors and do not necessarily representtheviewsoftheNationalScienceFoundation(NSF),NationalAeronautics andSpaceAdministration(NASA),orGeorgiaStateUniversity(GSU). ix Contents 1 AGentleIntroductiontoSpatiotemporalDataMining ................. 1 1.1 TypesofSpatiotemporalKnowledge................................... 2 1.2 MotivationandChallenges ............................................. 3 1.2.1 SolarPhysics.................................................... 4 1.2.2 BiomedicalSciences............................................ 5 1.2.3 Epidemiology................................................... 6 1.3 Challenges............................................................... 7 2 ModelingSpatiotemporalTrajectories.................................... 9 2.1 BasicSpatiotemporalDataTypes...................................... 9 2.2 MovingObjects ......................................................... 10 2.3 EvolvingRegionTrajectories........................................... 11 2.3.1 ModelingSpatiotemporalEventInstancesandExamples.... 13 3 ModelingSpatiotemporalRelationshipsAmongTrajectories.......... 17 3.1 GenericSpatialandTemporalRelationships .......................... 17 3.1.1 TemporalRelationships ........................................ 18 3.1.2 SpatialRelationships ........................................... 19 3.1.3 SpatialCo-locations ............................................ 21 3.2 SpatiotemporalRelationships .......................................... 22 3.2.1 SpatiotemporalCo-occurrence................................. 23 3.2.2 SpatiotemporalSequences...................................... 25 4 SignificanceMeasurementsforSpatiotemporalCo-occurrences....... 29 4.1 TheFamilyofJaccardMeasures....................................... 31 4.1.1 J Measure....................................................... 32 + 4.1.2 J Measure...................................................... 32 ∗ 4.1.3 J Measure ...................................................... 36 4.1.4 AlgorithmsforCalculatingJaccard-DerivedMeasures....... 40 4.2 OverlapMeasures....................................................... 45 4.2.1 KeyPropertiesofOverlapMeasures........................... 45 4.2.2 OMINandOMAXCalculationAlgorithms................... 47 xi xii Contents 4.3 CosineMeasure......................................................... 49 4.3.1 KeyPropertiesofCosineMeasure............................. 50 4.3.2 AlgorithmforCalculatingCosineMeasure ................... 51 4.4 Summary ................................................................ 51 5 SpatiotemporalCo-occurrencePattern(STCOP)Mining .............. 55 5.1 PreliminariesofSTCOPMining....................................... 56 5.2 SignificanceandPrevalenceMeasurements........................... 57 5.3 STCOPMiningfromEvolvingRegionTrajectories .................. 58 5.4 EfficientSpatiotemporalJoinsforSTCOPMining.................... 62 5.4.1 Grid-MappedIntervalTrees(GITs)............................ 62 5.4.2 ChebyshevPolynomialIndexing............................... 65 5.5 Summary ................................................................ 68 6 SpatiotemporalEventSequence(STES)Mining......................... 71 6.1 ModelingSpatiotemporalEventSequences ........................... 73 6.1.1 HeadandTailWindowofanInstance......................... 74 6.1.2 GeneratingHeadandTailWindows ........................... 74 6.1.3 StrategiesforHeadandTailWindowGeneration............. 76 6.2 SpatiotemporalFollowRelationshipandMeasuringthe Significance ............................................................. 79 6.2.1 SignificanceofInstanceSequences............................ 80 6.2.2 PrevalenceoftheEventSequences ............................ 82 6.3 Apriori-BasedAlgorithmsforMiningSpatiotemporalEvent Sequences ............................................................... 83 6.3.1 Initialization..................................................... 83 6.3.2 SequenceConnectAlgorithm................................... 83 6.3.3 AvoidingSpatiotemporalJoins................................. 85 6.4 APatternGrowth-BasedApproachforMiningSpatiotemporal EventSequences ........................................................ 87 6.4.1 EventSequencesandGraphRepresentation .................. 88 6.4.2 EsGrowthAlgorithm ........................................... 91 6.5 MiningtheMostPrevalentSpatiotemporalEventSequences: Top-(R%,K)Approach................................................. 93 6.6 Summary ................................................................ 95 References......................................................................... 97 Index............................................................................... 105
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