REST: A Reference-based Framework for Spatio-temporal Trajectory Compression
Online Publication Date2018-07-19
Print Publication Date2018
Permanent link to this recordhttp://hdl.handle.net/10754/628894
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AbstractThe 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.
CitationZhao Y, Shang S, Wang Y, Zheng B, Nguyen QVH, et al. (2018) REST. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD ’18. Available: http://dx.doi.org/10.1145/3219819.3220030.
SponsorsThis research is partially supported by the Natural Science Foundation of China (Grant No. 61532018, 61502324).
JournalProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18
Conference/Event name24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018