Scalable Algorithms for Nearest-Neighbor Joins on Big Trajectory Data

 Scalable Algorithms for Nearest-Neighbor Joins on Big Trajectory Data

 Trajectory data are prevalent in systems that monitor the locations of moving objects. In a location-based service, for instance, the positions of vehicles are continuously monitored through GPS; the trajectory of each vehicle describes its movement history.We study joins on two sets of trajectories, generated by two setsM and R of moving objects. For each entity inM, a join returns its k nearest neighbors from R. We examine how this query can be evaluated in cloud environments.