Compressing large scale urban trajectory data

Liu, Kuien, Li, Yaguang, Dai, Jian, Shang, Shuo and Zheng, Kai (2014). Compressing large scale urban trajectory data. In: Zbigniew Jerzak, Etienne Riviere and Luís Viega, Proceedings of the 4th International Workshop on Cloud Data and Platforms : CloudDP'14 : co-located with European Conference on Computer Systems, EuroSys 2014. 4th International Workshop on Cloud Data and Platforms, CloudDP 2014, Amsterdam, The Netherlands, (). April 13, 2014. doi:10.1145/2592784.2592787

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Author Liu, Kuien
Li, Yaguang
Dai, Jian
Shang, Shuo
Zheng, Kai
Title of paper Compressing large scale urban trajectory data
Conference name 4th International Workshop on Cloud Data and Platforms, CloudDP 2014
Conference location Amsterdam, The Netherlands
Conference dates April 13, 2014
Proceedings title Proceedings of the 4th International Workshop on Cloud Data and Platforms : CloudDP'14 : co-located with European Conference on Computer Systems, EuroSys 2014
Series ACM international conference proceedings series
Place of Publication New York, NY United States
Publisher Association for Computing Machinery
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.1145/2592784.2592787
Open Access Status
Editor Zbigniew Jerzak
Etienne Riviere
Luís Viega
Issue Article No. 3
Total pages 6
Collection year 2015
Language eng
Abstract/Summary With the increasing size of trajectory data generated by location-based services and applications which are built from inexpensive GPS-enabled devices in urban environments, the need for com- pressing large scale trajectories becomes obvious. This paper pro- poses a scalable urban trajectory compression scheme (SUTC) that can compress a set of trajectories collectively by exploiting com- mon movement behaviors among the urban moving objects such as vehicles and smartphone users. SUTC exploits that urban objects moving in similar behaviors naturally, especially large-scale of hu- man and vehicle which are moving constrained by some geograph- ic context (e.g., road networks or routes). To exploit redundancy across a large set of trajectories, SUTC first transforms trajectory sequences from Euclidean space to network-constrained space and represents each trajectory with a sequence of symbolic positions in textual domain. Then, SUTC performs compression by encoding the symbolic sequences with general-purpose compression meth-ods. The key challenge in this process is how to transform the tra-jectory data from spatio-temporal domain to textual domain with-out introducing unbounded error. We develop two strategies (i.e.,velocity-based symbolization, and beacon-based symbolization) to enrich the symbol sequences and achieves high compression ratios by sacrificing a little bit the decoding accuracy. Besides, we al-so optimize the organization of trajectory data in order to adapt it to practical compression algorithms, and increase the efficiency of compressing processes. Our experiments on real large-scale trajec-tory datasets demonstrate the superiority and feasibility of the our proposed algorithms. Copyright
Keyword Data compression
Spatio temporal trajectory data
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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