Show simple item record

dc.contributor.authorShang, Shuo
dc.contributor.authorchen, Lisi
dc.contributor.authorJensen, Christian S.
dc.contributor.authorWen, Ji-Rong
dc.contributor.authorKalnis, Panos
dc.date.accessioned2017-04-10T07:49:51Z
dc.date.available2017-04-10T07:49:51Z
dc.date.issued2017-03-22
dc.identifier.citationShang S, chen L, Jensen CS, Wen J-R, Kalnis P (2017) Searching Trajectories by Regions of Interest. IEEE Transactions on Knowledge and Data Engineering: 1–1. Available: http://dx.doi.org/10.1109/TKDE.2017.2685504.
dc.identifier.issn1041-4347
dc.identifier.doi10.1109/TKDE.2017.2685504
dc.identifier.urihttp://hdl.handle.net/10754/623103
dc.description.abstractWith the increasing availability of moving-object tracking data, trajectory search is increasingly important. We 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 many popular applications such as trip planning and recommendation, and location based services in general. TSR query processing faces three challenges: how to model the spatial-density correlation between query regions and data trajectories, how to effectively prune the search space, and how to effectively schedule multiple so-called query sources. To tackle these challenges, a series 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.urlhttp://ieeexplore.ieee.org/document/7883890/
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectCorrelation
dc.subjectElectronic mail
dc.subjectMeasurement
dc.subjectPlanning
dc.subjectQuery processing
dc.subjectSpatial databases
dc.subjectTrajectory
dc.titleSearching Trajectories by Regions of Interest
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalIEEE Transactions on Knowledge and Data Engineering
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
dc.contributor.institutionDepartment of Computer Science, Aalborg University, DK-9220 Aalborg Ost, Denmark
dc.contributor.institutionBeijing Key Laboratory of Big Data Management and Analysis Methods, and School of Information, Renmin University of China, P.R.China
kaust.personShang, Shuo
kaust.personKalnis, Panos
refterms.dateFOA2018-06-13T19:10:31Z
dc.date.published-online2017-03-22
dc.date.published-print2017-07-01


Files in this item

Thumbnail
Name:
07883890.pdf
Size:
613.8Kb
Format:
PDF
Description:
Accepted Manuscript

This item appears in the following Collection(s)

Show simple item record