High Performance Spatial Data Warehouse over MapReduce

Querying and analyzing large volumes of spatially oriented scientific data becomes increasingly important for many applications. For example, analyzing high-resolution digital pathology images through computer algorithms provides rich spatially derived information of micro-anatomic objects of human tissues. The spatial oriented information and queries at both cellular and sub-cellular scales share common characteristics of "Geographic Information System (GIS)'', and provide an effective vehicle to support computer aided biomedical research and clinical diagnosis through digital pathology. The scale of data could reach a million derived spatial objects and hundred million features for a single image. Managing and querying such spatially derived data to support complex queries such as image-wise spatial cross-matching queries poses two major challenges:  the high complexity of geometric computation and the "big data'' challenge. In this paper, we present a system Hadoop-GIS to support high performance declarative spatial queries with MapReduce. Hadoop-GIS provides an efficient real-time spatial query engine  RESQUE with dynamically built indices to support on the fly spatial query processing. To support high performance queries with cost effective architecture, we develop a MapReduce based framework for data partitioning and staging,  parallel processing of spatial queries with RESQUE, and feature queries with Hive, running on commodity clusters. To provide a declarative query language and unified interface, we integrate spatial query processing into Hive to build an integrated query system. Hadoop-GIS demonstrates highly scalable performance to support our query cases.