Compressing large scale urban trajectory data
文献类型：会议论文
| 作者 | Liu, Kuien (1) ; Li, Yaguang (1) ; Dai, Jian (1) ; Shang, Shuo (2) ; Zheng, Kai (3) |
| 出版日期 | 2014 |
| 会议名称 | 4th International Workshop on Cloud Data and Platforms, CloudDP 2014 |
| 会议日期 | April 13, 2014 - April 13, 2014 |
| 会议地点 | Amsterdam, Netherlands |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议录出版地 | Association for Computing Machinery |
| 语种 | 英语 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16586]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 推荐引用方式 GB/T 7714 | 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. |
入库方式： OAI收割
来源：软件研究所
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