Mining trajectory databases for multi-object movement patterns

Abstract

In this thesis, we present our studies on “Mining Trajectory Databases for Multi-object Movement Patterns”. A multi-object movement pattern describes the characteristics of a collective-movement performed by multiple objects. Knowledge of these patterns has numerous applications in epidemiology, ecology, traffic monitoring and control, and Location-Based Services. Specifically, we present our research to find meeting patterns, sub-trajectory clique patterns to find frequent routes, and convoy patterns. We proposed three new algorithms based on a novel data-driven approach to extract meetings from trajectory databases. We had studied and proposed two techniques to approximate sub-trajectory cliques and, sub-sequently, the frequent routes in Trajectory Databases. Our proposed approximation techniques require no prior knowledge of the underlying spatial space. Finally, we proposed new concepts of dynamic convoys and evolving convoys, which reflect real-life scenarios, and developed algorithms to discover evolving convoys in an incremental manner.