Searching Trajectories by Regions of Interest

Handle URI:
http://hdl.handle.net/10754/623103
Title:
Searching Trajectories by Regions of Interest
Authors:
Shang, Shuo; chen, Lisi; Jensen, Christian S.; Wen, Ji-Rong; Kalnis, Panos ( 0000-0002-5060-1360 )
Abstract:
With 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.
KAUST Department:
King Abdullah University of Science and Technology, Saudi Arabia
Citation:
Shang 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.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Knowledge and Data Engineering
Issue Date:
22-Mar-2017
DOI:
10.1109/TKDE.2017.2685504
Type:
Article
ISSN:
1041-4347
Additional Links:
http://ieeexplore.ieee.org/document/7883890/
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorShang, Shuoen
dc.contributor.authorchen, Lisien
dc.contributor.authorJensen, Christian S.en
dc.contributor.authorWen, Ji-Rongen
dc.contributor.authorKalnis, Panosen
dc.date.accessioned2017-04-10T07:49:51Z-
dc.date.available2017-04-10T07:49:51Z-
dc.date.issued2017-03-22en
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.en
dc.identifier.issn1041-4347en
dc.identifier.doi10.1109/TKDE.2017.2685504en
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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7883890/en
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.en
dc.subjectCorrelationen
dc.subjectElectronic mailen
dc.subjectMeasurementen
dc.subjectPlanningen
dc.subjectQuery processingen
dc.subjectSpatial databasesen
dc.subjectTrajectoryen
dc.titleSearching Trajectories by Regions of Interesten
dc.typeArticleen
dc.contributor.departmentKing Abdullah University of Science and Technology, Saudi Arabiaen
dc.identifier.journalIEEE Transactions on Knowledge and Data Engineeringen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Computer Science, Hong Kong Baptist University, Hong Kong SAR, Chinaen
dc.contributor.institutionDepartment of Computer Science, Aalborg University, DK-9220 Aalborg Ost, Denmarken
dc.contributor.institutionBeijing Key Laboratory of Big Data Management and Analysis Methods, and School of Information, Renmin University of China, P.R.Chinaen
kaust.authorShang, Shuoen
kaust.authorKalnis, Panosen
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