Trajectory similarity join in spatial networks

Handle URI:
http://hdl.handle.net/10754/625506
Title:
Trajectory similarity join in spatial networks
Authors:
Shang, Shuo; Chen, Lisi; Wei, Zhewei; Jensen, Christian S.; Zheng, Kai; Kalnis, Panos ( 0000-0002-5060-1360 )
Abstract:
The matching of similar pairs of objects, called similarity join, is fundamental functionality in data management. We consider the case of trajectory similarity join (TS-Join), where the objects are trajectories of vehicles moving in road networks. Thus, given two sets of trajectories and a threshold θ, the TS-Join returns all pairs of trajectories from the two sets with similarity above θ. This join targets applications such as trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction. With these applications in mind, we provide a purposeful definition of similarity. To enable efficient TS-Join processing on large sets of trajectories, we develop search space pruning techniques and take into account the parallel processing capabilities of modern processors. Specifically, we present a two-phase divide-and-conquer algorithm. For each trajectory, the algorithm first finds similar trajectories. Then it merges the results to achieve a final result. The algorithm exploits an upper bound on the spatiotemporal similarity and a heuristic scheduling strategy for search space pruning. The algorithm's per-trajectory searches are independent of each other and can be performed in parallel, and the merging has constant cost. An empirical study with real data offers insight in the performance of the algorithm and demonstrates that is capable of outperforming a well-designed baseline algorithm by an order of magnitude.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Citation:
Shang S, Chen L, Wei Z, Jensen CS, Zheng K, et al. (2017) Trajectory similarity join in spatial networks. Proceedings of the VLDB Endowment 10: 1178–1189. Available: http://dx.doi.org/10.14778/3137628.3137630.
Publisher:
VLDB Endowment
Journal:
Proceedings of the VLDB Endowment
Issue Date:
7-Sep-2017
DOI:
10.14778/3137628.3137630
Type:
Article
ISSN:
2150-8097
Sponsors:
This work is partially supported by KAUST, the National Natural Science Foundation of China (61402532, 61532018), Beijing Nova Program (xx2016078), and by the DiCyPS center, funded by Innovation Fund Denmark.
Additional Links:
http://dl.acm.org/citation.cfm?doid=3137628.3137630
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorShang, Shuoen
dc.contributor.authorChen, Lisien
dc.contributor.authorWei, Zheweien
dc.contributor.authorJensen, Christian S.en
dc.contributor.authorZheng, Kaien
dc.contributor.authorKalnis, Panosen
dc.date.accessioned2017-09-21T09:25:34Z-
dc.date.available2017-09-21T09:25:34Z-
dc.date.issued2017-09-07en
dc.identifier.citationShang S, Chen L, Wei Z, Jensen CS, Zheng K, et al. (2017) Trajectory similarity join in spatial networks. Proceedings of the VLDB Endowment 10: 1178–1189. Available: http://dx.doi.org/10.14778/3137628.3137630.en
dc.identifier.issn2150-8097en
dc.identifier.doi10.14778/3137628.3137630en
dc.identifier.urihttp://hdl.handle.net/10754/625506-
dc.description.abstractThe matching of similar pairs of objects, called similarity join, is fundamental functionality in data management. We consider the case of trajectory similarity join (TS-Join), where the objects are trajectories of vehicles moving in road networks. Thus, given two sets of trajectories and a threshold θ, the TS-Join returns all pairs of trajectories from the two sets with similarity above θ. This join targets applications such as trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction. With these applications in mind, we provide a purposeful definition of similarity. To enable efficient TS-Join processing on large sets of trajectories, we develop search space pruning techniques and take into account the parallel processing capabilities of modern processors. Specifically, we present a two-phase divide-and-conquer algorithm. For each trajectory, the algorithm first finds similar trajectories. Then it merges the results to achieve a final result. The algorithm exploits an upper bound on the spatiotemporal similarity and a heuristic scheduling strategy for search space pruning. The algorithm's per-trajectory searches are independent of each other and can be performed in parallel, and the merging has constant cost. An empirical study with real data offers insight in the performance of the algorithm and demonstrates that is capable of outperforming a well-designed baseline algorithm by an order of magnitude.en
dc.description.sponsorshipThis work is partially supported by KAUST, the National Natural Science Foundation of China (61402532, 61532018), Beijing Nova Program (xx2016078), and by the DiCyPS center, funded by Innovation Fund Denmark.en
dc.publisherVLDB Endowmenten
dc.relation.urlhttp://dl.acm.org/citation.cfm?doid=3137628.3137630en
dc.rightsThis work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.titleTrajectory similarity join in spatial networksen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalProceedings of the VLDB Endowmenten
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionHKBUen
dc.contributor.institutionRenmin University of Chinaen
dc.contributor.institutionAalborg Universityen
dc.contributor.institutionSoochow Universityen
kaust.authorShang, Shuoen
kaust.authorKalnis, Panosen
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