High performance spatial queries for spatial big data: from medical imaging to GIS

Support of high performance queries on large volumes of spatial data has become increasingly important in many application domains, including geospatial problems in numerous disciplines, location based services, and emerging medical imaging applications. There are two major challenges for managing massive spatial data to support spatial queries: the explosion of spatial data, and the high computational complexity of spatial queries. Our goal is to develop a general framework to support high performance spatial queries and analytics for spatial big data on MapReduce and CPU-GPU hybrid platforms. In this paper, we introduce Hadoop-GIS -- a scalable and high performance spatial data warehousing system for running large scale spatial queries on Hadoop. Hadoop-GIS supports multiple types of spatial queries on MapReduce through skew-aware spatial partitioning, on-demand indexing, customizable spatial query engine RESQUE, implicit parallel spatial query execution on MapReduce, and effective methods for amending query results through handling boundary objects. To accelerate compute-intensive geometric operations, GPU based geometric computation algorithms are integrated into MapReduce pipelines. Our experiments have demonstrated that Hadoop-GIS is highly efficient and scalable, and outperforms parallel spatial DBMS for compute-intensive spatial queries.