Towards parallel spatial query processing for big spatial data

Abstract—In recent years, spatial applications have become more and more important in both scientific research and in-dustry. Spatial query processing is the fundamental functioning component to support spatial applications. However, the state-of-the-art techniques of spatial query processing are facing significant challenges as the data expand and user accesses increase. In this paper we propose and implement a novel scheme (named VegaGiStore) to provide efficient spatial query processing over big spatial data and numerous concurrent user queries. Firstly, a geography-aware approach is proposed to organize spatial data in terms of geographic proximity, and this approach can achieve high aggregate I/O throughput. Secondly, in order to improve data retrieval efficiency, we design a two-tier distributed spatial index for efficient pruning of the search space. Thirdly, we propose an “indexing + MapReduce ” data processing architecture to improve the computation capability of spatial query. Performance evaluations of the real-deployed VegaGiStore system confirm its effectiveness. Keywords-spatial data management; distributed storage; s-patial index; spatial query; spatial applications