Shape-Based Similarity Query for Trajectory of Mobile Objects

In this paper, we describe an efficient indexing method for a shape-based similarity search of the trajectory of dynamically changing locations of people and mobile objects. In order to manage trajectories in database systems, we define a data model of trajectories as directed lines in a space, and the similarity between trajectories is defined as the Euclidean distance between directed discrete lines. Our proposed similarity query can be used to find interested patterns embedded into the trajectories, for example, the trajectories of mobile cars in a city may include patterns for expecting traffic jams. Furthermore, we propose an efficient indexing method to retrieve similar trajectories for a query by combining a spatial indexing technique (R-Tree) and a dimension reduction technique, which is called PAA (Piecewise Approximate Aggregate). The indexing method can efficiently retrieve trajectories whose shape in a space is similar to the shape of a candidate trajectory from the database.