Clockwise compression for trajectory data under road network constraints

Yudian Ji, Yuda Zang, Wuman Luo, Xibo Zhou, Ye Ding, Lionel M. Ni

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

Big trajectory data introduces severe challenges for data storage and communication. In this paper, we propose a novel compression framework called Clockwise Compression Framework (CCF) for big trajectory data compression under road network constraints. In CCF, we design several new methods: 1) a spatial compression algorithm called Enhanced Clockwise Encoding (ECE), 2) a temporal compression algorithm called Fitting-based Temporal Simplification (FTS), and 3) a dedicated querier that processes queries based on the above spatial and temporal compression algorithms, without fully decompressing the trajectroy data. By leveraging the topological information of the road network, CCF is able to perform both spatial compression and temporal compression in on-line modes. We perform extensive experiments in a real big trajectory dataset to verify both effectiveness and efficiency of our methods. CCF shows promising performances in various metrics and outperforms the state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages472-481
Number of pages10
ISBN (Electronic)9781467390040
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: 5 Dec 20168 Dec 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Conference

Conference4th IEEE International Conference on Big Data, Big Data 2016
Country/TerritoryUnited States
CityWashington
Period5/12/168/12/16

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