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Proceedings Paper

GIS spatial data partitioning method for distributed data processing
Author(s): Yan Zhou; Qing Zhu; Yeting Zhang
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Paper Abstract

Spatial data partitioning strategy plays an important role in GIS spatial data distributed storage and processing, its key problem is how to partition spatial data to distributed nodes in network environment. Existing main spatial data partitioning methods doesn't consider spatial locality and unstructured variable length characteristics of spatial data, these methods simply partition spatial data based on one or more attributes value that could result in storage capacity imbalance between distributed processing nodes. Aiming at these, we point out the two basic principles that spatial data partitioning should meet to in this paper. We propose a new spatial data partitioning method based on hierarchical decomposition method of low order Hilbert space-filling curve, which could avoid excessively intensive space partitioning by hierarchically decomposing subspaces. The proposed method uses Hilbert curve to impose a linear ordering on the multidimensional spatial objects, and partition the spatial objects according to this ordering. Experimental results show the proposed spatial data partitioning method not only achieves better storage load balance between distributed nodes, but also keeps well spatial locality of data objects after partitioning.

Paper Details

Date Published: 14 November 2007
PDF: 7 pages
Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 679008 (14 November 2007); doi: 10.1117/12.739790
Show Author Affiliations
Yan Zhou, Wuhan Univ. (China)
Qing Zhu, Wuhan Univ. (China)
Yeting Zhang, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 6790:
MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications

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