High performance spatial query processing for large scale scientific data

Analyzing and querying large volumes of spatially derived data from scientific experiments has posed major challenges in the past decade. For example, the systematic analysis of imaged pathology specimens result in rich spatially derived information with GIS characteristics at cellular and sub-cellular scales, with nearly a million derived markups and hundred million features per image. This provides critical information for evaluation of experimental results, support of biomedical studies and pathology image based diagnosis. However, the vast amount of spatially oriented morphological information poses major challenges for analytical medical imaging. The major challenges I attack include: i) How can we provide cost effective, scalable spatial query support for medical imaging GIS? ii) How can we provide fast response queries on analytical imaging data to support biomedical research and clinical diagnosis? and iii) How can we provide expressive queries to support spatial queries and spatial pattern discoveries for end users? In my thesis, I work towards developing a MapReduce based framework MIGIS to support expressive, cost effective and high performance spatial queries. The framework includes a real-time spatial query engine RESQUE consisting of a variety of optimized access methods, boundary and density aware spatial data partitioning, a declarative query language interface, a query translator which automates translation of the spatial queries into MapReduce programs and an execution engine which parallelizes and executes queries on Hadoop. Our preliminary experiments demonstrate that MIGIS is a cost effective architecture which achieves high performance spatial query execution. MIGIS is extensible and can be adapted to support similar complex spatial queries for large scale spatial data in other scientific domains.