Efficient trajectory joins using symbolic representations

Efficiently and accurately discovering similarities among moving object trajectories is a difficult problem that appears in many spatiotemporal applications. In this paper we consider how to efficiently evaluate trajectory joins, i.e., how to identify all pairs of similar trajectories between two datasets. Our approach represents an object trajectory as a sequence of symbols (i.e., a string). Based on special lower-bounding distances between two strings, we propose a pruning heuristic for reducing the number of trajectory pairs that need to be examined. Furthermore, we present an indexing scheme designed to support efficient evaluation of string similarities in secondary storage. Through a comprehensive experimental evaluation we present the advantages of the proposed techniques.