A MapReduce approach to Gi*(d) spatial statistic
Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems
Spatial scene similarity assessment on Hadoop
Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems
Multidimensional arrays for warehousing data on clouds
Globe'10 Proceedings of the Third international conference on Data management in grid and peer-to-peer systems
Efficient parallel kNN joins for large data in MapReduce
Proceedings of the 15th International Conference on Extending Database Technology
Performance comparisons of spatial data processing techniques for a large scale mobile phone dataset
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Processing multi-way spatial joins on map-reduce
Proceedings of the 16th International Conference on Extending Database Technology
CG_Hadoop: computational geometry in MapReduce
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Spatial queries include spatial selection query, spatial join query, nearest neighbor query, etc. Most of spatial queries are computing intensive and individual query evaluation may take minutes or even hours. Parallelization seems a good solution for such problems. However, parallel programs must communicate efficiently, balance work across all nodes, and address problems such as failed nodes. We describe MapReduce and show how spatial queries can be naturally expressed in this model, without explicitly addressing any of the details of parallelization. We present performance evaluations for several spatial queries and prove that MapReduce is also appropriate for small scale clusters and computing intensive applications.