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dc.contributor.authorAlves Peixoto, Douglas
dc.contributor.authorNguyen, Quoc Viet Hung
dc.date.accessioned2017-10-16T06:18:05Z
dc.date.available2017-10-16T06:18:05Z
dc.date.issued2016
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-319-46922-5_18
dc.identifier.urihttp://hdl.handle.net/10072/348256
dc.description.abstractTop-k most similar trajectories search (k-NN) is frequently used as classification algorithm and recommendation systems in spatial-temporal trajectory databases. However, k-NN trajectories is a complex operation, and a multi-user application should be able to process multiple k-NN trajectories search concurrently in large-scale data in an efficient manner. The k-NN trajectories problem has received plenty of attention, however, state-of-the-art works neither consider in-memory parallel processing of k-NN trajectories nor concurrent queries in distributed environments, or consider parallelization of k-NN search for simpler spatial objects (i.e. 2D points) using MapReduce, but ignore the temporal dimension of spatial-temporal trajectories. In this work we propose a distributed parallel approach for k-NN trajectories search in a multi-user environment using MapReduce in-memory. We propose a space/time data partitioning based on Voronoi diagrams and time pages, named Voronoi Pages, in order to provide both spatial-temporal data organization and process decentralization. In addition, we propose a spatial-temporal index for our partitions to efficiently prune the search space, improve system throughput and scalability. We implemented our solution on top of Spark’s RDD data structure, which provides a thread-safe environment for concurrent MapReduce tasks in main-memory. We perform extensive experiments to demonstrate the performance and scalability of our approach.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofpagefrom228
dc.relation.ispartofpageto241
dc.relation.ispartofjournalLecture Notes in Computer Science
dc.relation.ispartofvolume9877
dc.subject.fieldofresearchDatabase systems
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchcode460505
dc.subject.fieldofresearchcode4609
dc.subject.fieldofresearchcode46
dc.titleScalable and Fast Top-k Most Similar Trajectories Search Using MapReduce In-Memory
dc.typeBook chapter
dc.type.descriptionB1 - Chapters
dc.type.codeC - Journal Articles
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2016 Springer International Publishing AG. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com.
gro.hasfulltextFull Text
gro.griffith.authorNguyen, Henry


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