In-Memory Distance Threshold Queries on Moving Object Trajectories

The need to query spatiotemporal databases that store trajectories of moving objects arises in a broad range of application domains. In this work, we focus on in-memory distance threshold queries which return all moving objects that are found within a given distance d of a fixed or moving object over a time interval. We propose algorithms to solve such queries efficiently, using an R-tree index to store trajectory data and two methods for filtering out trajectory segments so as to reduce segment processing time. We evaluate our algorithms on both real-world and synthetic in-memory trajectory datasets. Choosing an efficient trajectory splitting strategy to reduce index resolution increases the efficiency of distance threshold queries. Interestingly, the traditional notion of considering good trajectory splits by minimizing the volume of MBBs so as to reduce index overlap is not well-suited to improve the performance of inmemory distance threshold queries. Keywords-spatiotemporal databases; query optimization.