Interval-Based Nearest Neighbor Queries Over Sliding Windows from Trajectory Data

This paper proposes a new type of query for moving object trajectories - Continuous Interval-based Nearest Neighbor (CINN) Query. We clearly define the CINN in the context of streaming trajectory data. To efficiently process CINN queries, we first propose a spatial hashing algorithm (SH). Then we show that a new temporal hashing algorithm (TH) using speed constraints can save substantial computation cost. To reduce memory cost, we further propose the temporal hashing with dropping optimization (THwD) algorithm. Extensive experiment results on large trajectory datasets show that CINN queries can be effectively answered using our proposed algorithms. With realistic speed constraints, the TH optimization can save the computation time by nearly an order of magnitude compared with the BF algorithm, and by 5 times compared with the SH algorithm. The THwD algorithm can further save the memory space by nearly an order of magnitude.