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A demonstration of SpatialHadoop: an efficient mapreduce framework for spatial data

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Published:01 August 2013Publication History
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Abstract

This demo presents SpatialHadoop as the first full-fledged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that pushes spatial data inside the core functionality of Hadoop. SpatialHadoop runs existing Hadoop programs as is, yet, it achieves order(s) of magnitude better performance than Hadoop when dealing with spatial data. SpatialHadoop employs a simple spatial high level language, a two-level spatial index structure, basic spatial components built inside the MapReduce layer, and three basic spatial operations: range queries, k-NN queries, and spatial join. Other spatial operations can be similarly deployed in SpatialHadoop. We demonstrate a real system prototype of SpatialHadoop running on an Amazon EC2 cluster against two sets of real spatial data obtained from Tiger Files and OpenStreetMap with sizes 60GB and 300GB, respectively.

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              cover image Proceedings of the VLDB Endowment
              Proceedings of the VLDB Endowment  Volume 6, Issue 12
              August 2013
              264 pages

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              VLDB Endowment

              Publication History

              • Published: 1 August 2013
              Published in pvldb Volume 6, Issue 12

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