Constraint-Based Learning of Distance Functions for Object Trajectories

Abstract. With the drastic increase of object trajectory data, the analysis and exploration of trajectories has become a major research focus with many applications. In particular, several approaches have been proposed in the context of similarity-based trajectory retrieval. While these approaches try to be comprehensive by considering the different properties of object trajectories at different degrees, the distance functions are always pre-defined and therefore do not support different views on what users consider (dis)similar trajectories in a particular domain. In this paper, we introduce a novel approach to learning distance functions in support of similarity-based retrieval of multi-dimensional object trajectories. Our approach is more generic than existing approaches in that distance functions are determined based on constraints, which specify what object trajectory pairs the user considers similar or dissimilar. Thus, using a single approach, different distance functions c...

• Type: Conference
• Year: 2009
• Where: SSDBM
• Authors: Wei Yu, Michael Gertz
• Keywords: Database, Distance Functions, Object Trajectories, Object Trajectory, SSDBM 2009