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dc.contributor.authorShang, Shuo
dc.contributor.authorChen, Lisi
dc.contributor.authorWei, Zhewei
dc.contributor.authorJensen, Christian S.
dc.contributor.authorZheng, Kai
dc.contributor.authorKalnis, Panos
dc.date.accessioned2018-04-10T08:37:48Z
dc.date.available2018-04-10T08:37:48Z
dc.date.issued2018-04-04
dc.identifier.citationShang S, Chen L, Wei Z, Jensen CS, Zheng K, et al. (2018) Parallel trajectory similarity joins in spatial networks. The VLDB Journal. Available: http://dx.doi.org/10.1007/s00778-018-0502-0.
dc.identifier.issn1066-8888
dc.identifier.issn0949-877X
dc.identifier.doi10.1007/s00778-018-0502-0
dc.identifier.urihttp://hdl.handle.net/10754/627421
dc.description.abstractThe matching of similar pairs of objects, called similarity join, is fundamental functionality in data management. We consider two cases of trajectory similarity joins (TS-Joins), including a threshold-based join (Tb-TS-Join) and a top-k TS-Join (k-TS-Join), where the objects are trajectories of vehicles moving in road networks. Given two sets of trajectories and a threshold θ, the Tb-TS-Join returns all pairs of trajectories from the two sets with similarity above θ. In contrast, the k-TS-Join does not take a threshold as a parameter, and it returns the top-k most similar trajectory pairs from the two sets. The TS-Joins target diverse applications such as trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction. With these applications in mind, we provide purposeful definitions of similarity. To enable efficient processing of the TS-Joins on large sets of trajectories, we develop search space pruning techniques and enable use of the parallel processing capabilities of modern processors. Specifically, we present a two-phase divide-and-conquer search framework that lays the foundation for the algorithms for the Tb-TS-Join and the k-TS-Join that rely on different pruning techniques to achieve efficiency. For each trajectory, the algorithms first find similar trajectories. Then they merge the results to obtain the final result. The algorithms for the two joins exploit different upper and lower bounds on the spatiotemporal trajectory similarity and different heuristic scheduling strategies for search space pruning. Their per-trajectory searches are independent of each other and can be performed in parallel, and the mergings have constant cost. An empirical study with real data offers insight in the performance of the algorithms and demonstrates that they are capable of outperforming well-designed baseline algorithms by an order of magnitude.
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/article/10.1007/s00778-018-0502-0
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s00778-018-0502-0
dc.subjectTrajectory similarity join
dc.subjectParallel processing
dc.subjectSpatial networks
dc.subjectSpatiotemporal databases
dc.titleParallel trajectory similarity joins in spatial networks
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalThe VLDB Journal
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Computer and Information Technology, University of Wollongong, Wollongong, Australia
dc.contributor.institutionSchool of Information, Renmin University of China, Beijing, China
dc.contributor.institutionDepartment of Computer Science, Aalborg University, Aalborg, Denmark
dc.contributor.institutionSchool of Computer Science and Engineering and Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
kaust.personShang, Shuo
kaust.personKalnis, Panos
refterms.dateFOA2019-04-04T00:00:00Z
dc.date.published-online2018-04-04
dc.date.published-print2018-06


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