PRESS

Abstract

Location data becomes more and more important. In this paper, we focus on the trajectory data, and propose a new framework, namely PRESS ( <u>P</u>aralleled <u>R</u>oad-Network-Based Trajectory Compr<u>ess</u>ion ), to effectively compress trajectory data under road network constraints. Different from existing work, PRESS proposes a novel representation for trajectories to separate the spatial representation of a trajectory from the temporal representation, and proposes a Hybrid Spatial Compression (HSC) algorithm and error Bounded Temporal Compression (BTC) algorithm to compress the spatial and temporal information of trajectories respectively. PRESS also supports common spatial-temporal queries without fully decompressing the data. Through an extensive experimental study on real trajectory dataset, PRESS significantly outperforms existing approaches in terms of saving storage cost of trajectory data with bounded errors.