Paper Info
Title
Reference-Based Framework for Spatio-Temporal Trajectory Compression and Query Processing
Abstract
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 ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Re</underline> ference-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</underline> patio- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</underline> emporal 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. In addition, by indexing the reference trajectories directly with an in-memory R-tree and building connections to the raw trajectories with inverted index, we develop an extremely efficient algorithm that can answer spatio-temporal range queries over trajectories in their compressed form. 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.
Year
DOI
Venue
2020
10.1109/TKDE.2019.2914449
IEEE Transactions on Knowledge and Data Engineering
Keywords
DocType
Volume
Trajectory,Query processing,Indexes,Measurement,Roads,Computer science,Compression algorithms
Journal
32
Issue
ISSN
Citations 
11
1041-4347
6
PageRank 
References 
Authors
0.70
0
6
Name
Order
Citations
PageRank
Kai Zheng193669.43
Yan Zhao2459.79
Defu Lian375946.15
Bolong Zheng424726.67
Guanfeng Liu549354.18
Xiaofang Zhou65381342.70