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
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Building a scaleable geo-spatial DBMS: technology, implementation, and evaluation
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data Partitioning for Parallel Spatial Join Processing
Geoinformatica
Parallel Processing of Spatial Joins Using R-trees
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
An Evaluation of Generic Bulk Loading Techniques
Proceedings of the 27th International Conference on Very Large Data Bases
Hilbert R-tree: An Improved R-tree using Fractals
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Pig latin: a not-so-foreign language for data processing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
SCOPE: easy and efficient parallel processing of massive data sets
Proceedings of the VLDB Endowment
GrayWulf: Scalable Clustered Architecture for Data Intensive Computing
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
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
MapReduce and parallel DBMSs: friends or foes?
Communications of the ACM - Amir Pnueli: Ahead of His Time
MapReduce: a flexible data processing tool
Communications of the ACM - Amir Pnueli: Ahead of His Time
Building a high-level dataflow system on top of Map-Reduce: the Pig experience
Proceedings of the VLDB Endowment
Hive: a warehousing solution over a map-reduce framework
Proceedings of the VLDB Endowment
HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads
Proceedings of the VLDB Endowment
Integrating hadoop and parallel DBMs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A comparison of join algorithms for log processing in MaPreduce
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Voronoi-Based Geospatial Query Processing with MapReduce
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
YSmart: Yet Another SQL-to-MapReduce Translator
ICDCS '11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems
High performance spatial query processing for large scale scientific data
PhD '12 Proceedings of the on SIGMOD/PODS 2012 PhD Symposium
Accelerating pathology image data cross-comparison on CPU-GPU hybrid systems
Proceedings of the VLDB Endowment
Towards Parallel Spatial Query Processing for Big Spatial Data
IPDPSW '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum
Towards building a high performance spatial query system for large scale medical imaging data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Processing multi-way spatial joins on map-reduce
Proceedings of the 16th International Conference on Extending Database Technology
Demonstration of Hadoop-GIS: a spatial data warehousing system over MapReduce
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
CG_Hadoop: computational geometry in MapReduce
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Support of high performance queries on large volumes of spatial data becomes increasingly important in many application domains, including geospatial problems in numerous fields, location based services, and emerging scientific applications that are increasingly data- and compute-intensive. The emergence of massive scale spatial data is due to the proliferation of cost effective and ubiquitous positioning technologies, development of high resolution imaging technologies, and contribution from a large number of community users. There are two major challenges for managing and querying massive spatial data to support spatial queries: the explosion of spatial data, and the high computational complexity of spatial queries. In this paper, we present 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 spatial partitioning, customizable spatial query engine RESQUE, implicit parallel spatial query execution on MapReduce, and effective methods for amending query results through handling boundary objects. Hadoop-GIS utilizes global partition indexing and customizable on demand local spatial indexing to achieve efficient query processing. Hadoop-GIS is integrated into Hive to support declarative spatial queries with an integrated architecture. Our experiments have demonstrated the high efficiency of Hadoop-GIS on query response and high scalability to run on commodity clusters. Our comparative experiments have showed that performance of Hadoop-GIS is on par with parallel SDBMS and outperforms SDBMS for compute-intensive queries. Hadoop-GIS is available as a set of library for processing spatial queries, and as an integrated software package in Hive.