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Proceedings Paper

Applying Hilbert spatial ordering code to partition massive spatial data in PC cluster system
Author(s): Yongjie Wang; Xinlan Hong; Lingkui Meng; Chunyu Zhao
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Paper Abstract

In order to handle massive spatial data quickly and efficiently, a superior solution is to store and handle them in parallel spatial database management systems under the environment of PC cluster at present, and thus its spatial partitioning strategy of data needs solving first. Hilbert spatial ordering code based on Hilbert space-filling curve is an excellent linear mapping method, and gets wider and wider applications in processing spatial data. After studying Hilbert curve, this paper proposes a new and efficient algorithm for the generation of Hilbert code, and it has overcome drawbacks of the traditional algorithm. Then Hilbert code is applied to spatial partitioning with the method of cluster analysis, and a concrete method is given, which fully considers characteristics of spatial data, such as the aggregation of spatial data, reduces the time of disks accesses, and achieves better performance by experiments than the compulsory partitioning of ORACLE Spatial based on X coordinate values and (or) Y coordinate values in subsequent parallel processing of spatial data.

Paper Details

Date Published: 28 October 2006
PDF: 7 pages
Proc. SPIE 6420, Geoinformatics 2006: Geospatial Information Science, 64200Q (28 October 2006); doi: 10.1117/12.712734
Show Author Affiliations
Yongjie Wang, Wuhan Univ. (China)
Xinlan Hong, Zhejiang Highway Machinery Technicians School (China)
Lingkui Meng, Wuhan Univ. (China)
Chunyu Zhao, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 6420:
Geoinformatics 2006: Geospatial Information Science
Jianya Gong; Jingxiong Zhang, Editor(s)

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