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dc.contributor.authorZhao, Yan
dc.contributor.authorShang, Shuo
dc.contributor.authorWang, Yu
dc.contributor.authorZheng, Bolong
dc.contributor.authorQuoc, Viet Hung Nguyen
dc.contributor.authorZheng, Kai
dc.contributor.editorR㭥r Rosales, Jiliang Tang
dc.date.accessioned2019-06-19T13:02:58Z
dc.date.available2019-06-19T13:02:58Z
dc.date.issued2018
dc.identifier.isbn9781450355520
dc.identifier.doi10.1145/3219819.3220030
dc.identifier.urihttp://hdl.handle.net/10072/379948
dc.description.abstractThe pervasiveness of GPS-enabled devices and wireless communication technologies results in massive trajectory data, incurring expensive cost for storage, transmission, and query processing. To relieve this problem, in this paper we propose a novel framework for compressing trajectory data, REST (Reference-based Spatio-temporal trajectory compression), by which a raw trajectory is represented by concatenation of a series of historical (sub-)trajectories (called reference trajectories) that form the compressed trajectory within a given spatio-temporal deviation threshold. In order to construct a reference trajectory set that can most benefit the subsequent compression, we propose three kinds of techniques to select reference trajectories wisely from a large dataset such that the resulting reference set is more compact yet covering most footprints of trajectories in the area of interest. To address the computational issue caused by the large number of combinations of reference trajectories that may exist for resembling a given trajectory, we propose efficient greedy algorithms that run in the blink of an eye and dynamic programming algorithms that can achieve the optimal compression ratio. Compared to existing work on trajectory compression, our framework has few assumptions about data such as moving within a road network or moving with constant direction and speed, and better compression performance with fairly small spatio-temporal loss. Extensive experiments on a real taxi trajectory dataset demonstrate the superiority of our framework over existing representative approaches in terms of both compression ratio and efficiency.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAssociation for Computing Machinery (ACM)
dc.publisher.placeUnited States
dc.relation.ispartofchapter42292
dc.relation.ispartofconferencename24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
dc.relation.ispartofconferencetitleKDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING
dc.relation.ispartofdatefrom2018-08-19
dc.relation.ispartofdateto2018-08-23
dc.relation.ispartoflocationLondon, ENGLAND
dc.relation.ispartofpagefrom2797
dc.relation.ispartofpageto2806
dc.subject.fieldofresearchDatabase systems
dc.subject.fieldofresearchcode460505
dc.titleREST: A Reference-based Framework for Spatio-temporal Trajectory Compression
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorNguyen, Henry


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