dc.contributor.author | Alves Peixoto, Douglas | |
dc.contributor.author | Nguyen, Quoc Viet Hung | |
dc.date.accessioned | 2017-10-16T06:18:05Z | |
dc.date.available | 2017-10-16T06:18:05Z | |
dc.date.issued | 2016 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.doi | 10.1007/978-3-319-46922-5_18 | |
dc.identifier.uri | http://hdl.handle.net/10072/348256 | |
dc.description.abstract | Top-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.peerreviewed | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Springer | |
dc.relation.ispartofpagefrom | 228 | |
dc.relation.ispartofpageto | 241 | |
dc.relation.ispartofjournal | Lecture Notes in Computer Science | |
dc.relation.ispartofvolume | 9877 | |
dc.subject.fieldofresearch | Database systems | |
dc.subject.fieldofresearch | Information systems | |
dc.subject.fieldofresearch | Information and computing sciences | |
dc.subject.fieldofresearchcode | 460505 | |
dc.subject.fieldofresearchcode | 4609 | |
dc.subject.fieldofresearchcode | 46 | |
dc.title | Scalable and Fast Top-k Most Similar Trajectories Search Using MapReduce In-Memory | |
dc.type | Book chapter | |
dc.type.description | B1 - Chapters | |
dc.type.code | C - Journal Articles | |
dc.description.version | Accepted 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.hasfulltext | Full Text | |
gro.griffith.author | Nguyen, Henry | |