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Article: Discovery of periodic patterns in spatiotemporal sequences

TitleDiscovery of periodic patterns in spatiotemporal sequences
Authors
KeywordsData mining
Periodic patterns
Spatiotemporal data
Issue Date2007
PublisherI E E E. The Journal's web site is located at http://www.computer.org/tkde
Citation
Ieee Transactions On Knowledge And Data Engineering, 2007, v. 19 n. 4, p. 453-467 How to Cite?
AbstractIn many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. For example, people wake up at the same time and follow more or less the same route to their work everyday. The discovery of hidden periodic patterns in spatiotemporal data could unveil important information to the data analyst. Existing approaches for discovering periodic patterns focus on symbol sequences. However, these methods cannot directly be applied to a spatiotemporal sequence because of the fuzziness of spatial locations in the sequence. In this paper, we define the problem of mining periodic patterns in spatiotemporal data and propose an effective and efficient algorithm for retrieving maximal periodic patterns. In addition, we study two interesting variants of the problem. The first is the retrieval of periodic patterns that are frequent only during a continuous subinterval of the whole history. The second problem is the discovery of periodic patterns, whose instances may be shifted or distorted. We demonstrate how our mining technique can be adapted for these variants. Finally, we present a comprehensive experimental evaluation, where we show the effectiveness and efficiency of the proposed techniques © 2007 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/47085
ISSN
2015 Impact Factor: 2.476
2015 SCImago Journal Rankings: 2.087
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCao, Hen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.contributor.authorCheung, DWen_HK
dc.date.accessioned2007-10-30T07:06:47Z-
dc.date.available2007-10-30T07:06:47Z-
dc.date.issued2007en_HK
dc.identifier.citationIeee Transactions On Knowledge And Data Engineering, 2007, v. 19 n. 4, p. 453-467en_HK
dc.identifier.issn1041-4347en_HK
dc.identifier.urihttp://hdl.handle.net/10722/47085-
dc.description.abstractIn many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. For example, people wake up at the same time and follow more or less the same route to their work everyday. The discovery of hidden periodic patterns in spatiotemporal data could unveil important information to the data analyst. Existing approaches for discovering periodic patterns focus on symbol sequences. However, these methods cannot directly be applied to a spatiotemporal sequence because of the fuzziness of spatial locations in the sequence. In this paper, we define the problem of mining periodic patterns in spatiotemporal data and propose an effective and efficient algorithm for retrieving maximal periodic patterns. In addition, we study two interesting variants of the problem. The first is the retrieval of periodic patterns that are frequent only during a continuous subinterval of the whole history. The second problem is the discovery of periodic patterns, whose instances may be shifted or distorted. We demonstrate how our mining technique can be adapted for these variants. Finally, we present a comprehensive experimental evaluation, where we show the effectiveness and efficiency of the proposed techniques © 2007 IEEE.en_HK
dc.format.extent3116840 bytes-
dc.format.extent4295 bytes-
dc.format.extent6619 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherI E E E. The Journal's web site is located at http://www.computer.org/tkdeen_HK
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineeringen_HK
dc.rights©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectData miningen_HK
dc.subjectPeriodic patternsen_HK
dc.subjectSpatiotemporal dataen_HK
dc.titleDiscovery of periodic patterns in spatiotemporal sequencesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1041-4347&volume=19&issue=4&spage=453&epage=467&date=2007&atitle=Discovery+of+Periodic+Patterns+in+Spatiotemporal+Sequencesen_HK
dc.identifier.emailMamoulis, N:nikos@cs.hku.hken_HK
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_HK
dc.identifier.authorityMamoulis, N=rp00155en_HK
dc.identifier.authorityCheung, DW=rp00101en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/TKDE.2007.1002en_HK
dc.identifier.scopuseid_2-s2.0-64149088126en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-64149088126&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume19en_HK
dc.identifier.issue4en_HK
dc.identifier.spage453en_HK
dc.identifier.epage467en_HK
dc.identifier.isiWOS:000244332000002-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridCao, H=7403346030en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK
dc.identifier.citeulike2305871-

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