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dc.contributor.authorShang, Shuo
dc.contributor.authorChen, Lisi
dc.contributor.authorJensen, Christian S.
dc.contributor.authorWen, Ji-Rong
dc.contributor.authorKalnis, Panos
dc.date.accessioned2018-12-30T08:42:18Z
dc.date.available2018-12-30T08:42:18Z
dc.date.issued2018-10-25
dc.identifier.citationShang S, Chen L, Jensen CS, Wen J-R, Kalnis P (2018) Searching Trajectories by Regions of Interest. 2018 IEEE 34th International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2018.00228.
dc.identifier.doi10.1109/ICDE.2018.00228
dc.identifier.urihttp://hdl.handle.net/10754/630374
dc.description.abstractWe propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in applications such as trip planning and recommendation. To process the TSR query, a set of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8509449
dc.rightsArchived with thanks to 2018 IEEE 34th International Conference on Data Engineering (ICDE)
dc.subjectSpatial databases
dc.subjectSpatial density correlation
dc.subjectSpatial networks
dc.subjectTrajectory search by regions
dc.titleSearching Trajectories by Regions of Interest
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journal2018 IEEE 34th International Conference on Data Engineering (ICDE)
dc.conference.date2018-04-16 to 2018-04-19
dc.conference.name34th IEEE International Conference on Data Engineering, ICDE 2018
dc.conference.locationParis, FRA
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionWollongong University, , Australia
dc.contributor.institutionDepartment of Computer Science, Aalborg University, , Denmark
dc.contributor.institutionSchool of Information, Renmin University, , China
kaust.personShang, Shuo
kaust.personKalnis, Panos
refterms.dateFOA2018-12-30T08:43:47Z
dc.date.published-online2018-10-25
dc.date.published-print2018-04


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