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Conference Paper: Scalable algorithms for nearest-neighbor joins on big trajectory data (Extended abstract)

TitleScalable algorithms for nearest-neighbor joins on big trajectory data (Extended abstract)
Authors
Issue Date2016
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178
Citation
The 32nd IEEE International Conference on Data Engineering (ICDE 2016), Helsinki, Finland, 16-20 May 2016. In Conference Proceedings, 2016, p. 1528-1529 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. To evaluate the efficiency and the scalability of our solutions, we have performed extensive experiments on a real dataset. © 2016 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/232183
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorFang, Y-
dc.contributor.authorCheng, RCK-
dc.contributor.authorTang, W-
dc.contributor.authorManiu, S-
dc.contributor.authorYang, X-
dc.date.accessioned2016-09-20T05:28:18Z-
dc.date.available2016-09-20T05:28:18Z-
dc.date.issued2016-
dc.identifier.citationThe 32nd IEEE International Conference on Data Engineering (ICDE 2016), Helsinki, Finland, 16-20 May 2016. In Conference Proceedings, 2016, p. 1528-1529-
dc.identifier.isbn978-150902019-5-
dc.identifier.issn1084-4627-
dc.identifier.urihttp://hdl.handle.net/10722/232183-
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. To evaluate the efficiency and the scalability of our solutions, we have performed extensive experiments on a real dataset. © 2016 IEEE.-
dc.languageeng-
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178-
dc.relation.ispartofInternational Conference on Data Engineering Proceedings-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleScalable algorithms for nearest-neighbor joins on big trajectory data (Extended abstract)-
dc.typeConference_Paper-
dc.identifier.emailCheng, RCK: ckcheng@cs.hku.hk-
dc.identifier.emailManiu, S: smaniu@cs.hku.hk-
dc.identifier.emailYang, X: xyang2@cs.hku.hk-
dc.identifier.authorityCheng, RCK=rp00074-
dc.description.naturepostprint-
dc.identifier.doi10.1109/ICDE.2016.7498408-
dc.identifier.scopuseid_2-s2.0-84980332094-
dc.identifier.hkuros265277-
dc.identifier.hkuros267172-
dc.identifier.spage1528-
dc.identifier.epage1529-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 161004 ; 161027 merged-

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