A Trajectory Splitting Model for Efficient Spatio-Temporal Indexing

This paper addresses the problem of splitting trajectories optimally for the purpose of efficiently supporting spatio-temporal range queries using index structures (e.g., R-trees) that use minimum bounding hyper-rectangles as trajectory approximations. We derive a formal cost model for estimating the number of I/Os required to evaluate a spatio-temporal range query with respect to a given query size and an arbitrary split of a trajectory. Based on the proposed model, we introduce a dynamic programming algorithm for splitting a set of trajectories that minimizes the number of expected disk I/Os with respect to an average query size. In addition, we develop a linear time, near optimal solution for this problem to be used in a dynamic case where trajectory points are continuously updated. Our experimental evaluation confirms the effectiveness and efficiency of our proposed splitting policies when embedded into an R-tree structure.