Parallel bulk-loading of spatial data with MapReduce: An R-tree case |
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Authors: | Yi Liu Ning Jing Luo Chen Huizhong Chen |
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Institution: | LIU Yi,JING Ning,CHEN Luo,CHEN Huizhong College of Electronic Science and Engineering,National University of Defense Technology,Changsha 410073,Hunan,China |
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Abstract: | Current literature on parallel bulk-loading of R-tree index has the disadvantage that the quality of produced spatial index
decrease considerably as the parallelism increases. To solve this problem, a novel method of bulk-loading spatial data using
the popular MapReduce framework is proposed. MapReduce combines Hilbert curve and random sampling method to parallel partition
and sort spatial data, thus it balances the number of spatial data in each partition. Then the bottom-up method is introduced
to simplify and accelerate the sub-index construction in each partition. Three area metrics are used to test the quality of
generated index under different partitions. The extensive experiments show that the generated R-trees have the similar quality
with the generated R-tree using sequential bulk-loading method, while the execution time is reduced considerably by exploiting
parallelism. |
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Keywords: | parallel bulk-loading MapReduce R-tree query processing |
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