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Article: Scalable Algorithms for Nearest-Neighbor Joins on Big Trajectory Data

TitleScalable Algorithms for Nearest-Neighbor Joins on Big Trajectory Data
Authors
Issue Date2016
PublisherIEEE. The Journal's web site is located at http://www.computer.org/tkde
Citation
IEEE Transactions on Knowledge and Data Engineering, 2016, v. 28 n. 3, p. 785-800 How to Cite?
AbstractTrajectory data are prevalent in systems that monitor the locations of moving objects. In a location-based service, for instance, the positions of vehicles are continuously monitored through GPS; the trajectory of each vehicle describes its movement history. We study joins on two sets of trajectories, generated by two sets M and R of moving objects. For each entity in M, a join returns its k nearest neighbors from R. We examine how this query can be evaluated in cloud environments. This problem is not trivial, due to the complexity of the trajectory, and the fact that both the spatial and temporal dimensions of the data have to be handled. To facilitate this operation, we propose a parallel solution framework based on MapReduce. We also develop a novel bounding technique, which enables trajectories to be pruned in parallel. Our approach can be used to parallelize existing single-machine trajectory join algorithms. We also study a variant of the join, which can further improve query efficiency. To evaluate the efficiency and the scalability of our solutions, we have performed extensive experiments on large real and synthetic datasets.
Persistent Identifierhttp://hdl.handle.net/10722/232841
ISSN
2015 Impact Factor: 2.476
2015 SCImago Journal Rankings: 2.087

 

DC FieldValueLanguage
dc.contributor.authorFANG, Y-
dc.contributor.authorCheng, CK-
dc.contributor.authorTANG, W-
dc.contributor.authorManiu, S-
dc.contributor.authorYang, X-
dc.date.accessioned2016-09-20T05:32:49Z-
dc.date.available2016-09-20T05:32:49Z-
dc.date.issued2016-
dc.identifier.citationIEEE Transactions on Knowledge and Data Engineering, 2016, v. 28 n. 3, p. 785-800-
dc.identifier.issn1041-4347-
dc.identifier.urihttp://hdl.handle.net/10722/232841-
dc.description.abstractTrajectory data are prevalent in systems that monitor the locations of moving objects. In a location-based service, for instance, the positions of vehicles are continuously monitored through GPS; the trajectory of each vehicle describes its movement history. We study joins on two sets of trajectories, generated by two sets M and R of moving objects. For each entity in M, a join returns its k nearest neighbors from R. We examine how this query can be evaluated in cloud environments. This problem is not trivial, due to the complexity of the trajectory, and the fact that both the spatial and temporal dimensions of the data have to be handled. To facilitate this operation, we propose a parallel solution framework based on MapReduce. We also develop a novel bounding technique, which enables trajectories to be pruned in parallel. Our approach can be used to parallelize existing single-machine trajectory join algorithms. We also study a variant of the join, which can further improve query efficiency. To evaluate the efficiency and the scalability of our solutions, we have performed extensive experiments on large real and synthetic datasets.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://www.computer.org/tkde-
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineering-
dc.rightsIEEE Transactions on Knowledge and Data Engineering. Copyright © IEEE.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. -
dc.titleScalable Algorithms for Nearest-Neighbor Joins on Big Trajectory Data-
dc.typeArticle-
dc.identifier.emailCheng, CK: ckcheng@cs.hku.hk-
dc.identifier.authorityCheng, CK=rp00074-
dc.identifier.doi10.1109/TKDE.2015.2492561-
dc.identifier.hkuros265234-
dc.identifier.volume28-
dc.identifier.issue3-
dc.identifier.spage785-
dc.identifier.epage800-
dc.publisher.placeUnited States-

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