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Title:
Compressing large scale urban trajectory data
Author: Liu, Kuien (1) ; Li, Yaguang (1) ; Dai, Jian (1) ; Shang, Shuo (2) ; Zheng, Kai (3)
Conference Name: 4th International Workshop on Cloud Data and Platforms, CloudDP 2014
Conference Date: April 13, 2014 - April 13, 2014
Issued Date: 2014
Conference Place: Amsterdam, Netherlands
Publish Place: Association for Computing Machinery
Indexed Type: EI
Department: (1) Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; (2) China University of Petroleum, Beijing 102249, China; (3) University of Queensland, Brisbane QLD 4072, Australia
Abstract: 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 © 2014 ACM.
English Abstract: 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 © 2014 ACM.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16586
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Liu, Kuien ,Li, Yaguang ,Dai, Jian ,et al. Compressing large scale urban trajectory data[C]. 见:4th International Workshop on Cloud Data and Platforms, CloudDP 2014. Amsterdam, Netherlands. April 13, 2014 - April 13, 2014.
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