A boundary filtering based spatial join query processing optimization algorithm

Aiming at the problems of the spatial join query processing in the cloud environment, an effective spatial join query processing optimization algorithm is proposed in this paper, which is based on the MapReduce framework. The algorithm applies a grid partitioning method to distribute spatial data objects and uses a boundary filtering strategy to reduce the computation and communication cost of spatial join query processing. Firstly, data space is divided into partition units with the same size, and spatial data objects are distributed into the corresponding partition units according to the inclusion relation between them. Then calculates the MBR of the spatial data objects within a partition unit and uses the MBR to filter the useless data objects across the partition unit, thereby reducing the corresponding computation cost. At the same time, a simple and effective data results duplication avoiding mechanism is applied to avoid repeating output of spatial join processing results, further reducing the computation cost at Reduce stage. Experiment results on synthetic and real datasets show that the proposed algorithm has obvious advantages and good performance than the original SJMR query processing algorithm.