SIGMOD '96 Proceedings of the 1996 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
Integration of spatial join algorithms for processing multiple inputs
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Clone join and shadow join: two parallel spatial join algorithms
Proceedings of the 8th ACM international symposium on Advances in geographic information systems
ACM Transactions on Database Systems (TODS)
Approximate Processing of Multiway Spatial Joins in Very Large Databases
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Parallel Processing of Spatial Joins Using R-trees
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Spatial Queries Evaluation with MapReduce
GCC '09 Proceedings of the 2009 Eighth International Conference on Grid and Cooperative Computing
Optimizing joins in a map-reduce environment
Proceedings of the 13th International Conference on Extending Database Technology
Efficient parallel set-similarity joins using MapReduce
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
Accelerating Spatial Data Processing with MapReduce
ICPADS '10 Proceedings of the 2010 IEEE 16th International Conference on Parallel and Distributed Systems
Processing theta-joins using MapReduce
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Efficient processing of k nearest neighbor joins using MapReduce
Proceedings of the VLDB Endowment
Hadoop GIS: a high performance spatial data warehousing system over mapreduce
Proceedings of the VLDB Endowment
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In this paper we investigate the problem of processing multi-way spatial joins on map-reduce platform. We look at two common spatial predicates - overlap and range. We address these two classes of join queries, discuss the challenges and outline novel approaches for executing these queries on a map-reduce framework. We then discuss how we can process join queries involving both overlap and range predicates. Specifically we present a Controlled-Replicate framework using which we design the approaches presented in this paper. The Controlled-Replicate framework is carefully engineered to minimize the communication among cluster nodes. Through experimental evaluations we discuss the complexity of the problem under investigation, details of Controlled-Replicate framework and demonstrate that the proposed approaches comfortably outperform naive approaches.