Please use this identifier to cite or link to this item: https://doi.org/10.1109/TKDE.2012.193
DC FieldValue
dc.titleEffective online group discovery in trajectory databases
dc.contributor.authorLi, X.
dc.contributor.authorČeikute, V.
dc.contributor.authorJensen, C.S.
dc.contributor.authorTan, K.-L.
dc.date.accessioned2014-07-04T03:09:26Z
dc.date.available2014-07-04T03:09:26Z
dc.date.issued2013
dc.identifier.citationLi, X., Čeikute, V., Jensen, C.S., Tan, K.-L. (2013). Effective online group discovery in trajectory databases. IEEE Transactions on Knowledge and Data Engineering 25 (12) : 2752-2766. ScholarBank@NUS Repository. https://doi.org/10.1109/TKDE.2012.193
dc.identifier.issn10414347
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/77844
dc.description.abstractGPS-enabled devices are pervasive nowadays. Finding movement patterns in trajectory data stream is gaining in importance. We propose a group discovery framework that aims to efficiently support the online discovery of moving objects that travel together. The framework adopts a sampling-independent approach that makes no assumptions about when positions are sampled, gives no special importance to sampling points, and naturally supports the use of approximate trajectories. The framework's algorithms exploit state-of-the-art, density-based clustering (DBScan) to identify groups. The groups are scored based on their cardinality and duration, and the top-k groups are returned. To avoid returning similar subgroups in a result, notions of domination and similarity are introduced that enable the pruning of low-interest groups. Empirical studies on real and synthetic data sets offer insight into the effectiveness and efficiency of the proposed framework. © 1989-2012 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TKDE.2012.193
dc.sourceScopus
dc.subjectMoving objects
dc.subjectTrajectory
dc.subjectTravel patterns
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/TKDE.2012.193
dc.description.sourcetitleIEEE Transactions on Knowledge and Data Engineering
dc.description.volume25
dc.description.issue12
dc.description.page2752-2766
dc.description.codenITKEE
dc.identifier.isiut000326500600007
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