The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Partition based spatial-merge join
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Pig latin: a not-so-foreign language for data processing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Experiences on Processing Spatial Data with MapReduce
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Communications of the ACM
YSmart: Yet Another SQL-to-MapReduce Translator
ICDCS '11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems
Hadoop GIS: a high performance spatial data warehousing system over mapreduce
Proceedings of the VLDB Endowment
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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.