REST: A Reference-based Framework for Spatio-temporal Trajectory Compression

被引:51
|
作者
Zhao, Yan [1 ,2 ]
Shang, Shuo [3 ]
Wang, Yu [4 ]
Zheng, Bolong [5 ,6 ]
Quoc Viet Hung Nguyen [7 ]
Zheng, Kai [8 ]
机构
[1] Soochow Univ, Inst Artificial Intelligence, Suzhou, Peoples R China
[2] Soochow Univ, Sch Comp Sci & Technol, Suzhou, Peoples R China
[3] King Abdullah Univ Sci & Technol, Thuwal, Saudi Arabia
[4] Chinese Univ Hong Kong, Hong Kong, Peoples R China
[5] Huazhong Univ Sci & Technol, Wuhan, Hubei, Peoples R China
[6] Aalborg Univ, Aalborg, Denmark
[7] Griffith Univ, Brisbane, Qld, Australia
[8] Univ Elect Sci & Technol China, Chengdu, Sichuan, Peoples R China
关键词
compression algorithm; trajectory; spatio-temporal data;
D O I
10.1145/3219819.3220030
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The 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.
引用
收藏
页码:2797 / 2806
页数:10
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