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2010 Second IITA International Conference on Geoscience and Remote Sensing (IITA-GRS 2010)

作者： Yan Zhou School of Automation University of Electronic Science and Technology of China Chengdu,P.R.China Qing Zhu,Yeting Zhang State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan,P.R.China

spatial data declustering is an important data processing method for parallel spatial database especially in shared nothing parallel architecture. spatial data declustering can achieve parallel dataflow to exploit the I/O bandwidth of multiple parallel nodes by reading and writing them in parallel,which can improve the performance of parallel spatial database evidently. Aiming at the unique spatial objects locality,this paper presents a novel spatial data declustering method,which uses Hilbert space-filling curve to impose a linear ordering on multidimensional spatial objects,and to partition spatial objects logical segments according to this ordering to preserve spatial locality of spatial objects,and then to allocate logical segments to physical parallel nodes based on round-robin rule. Experimental results show that the proposed method can obtain well spatial data declustering results.

关键词： spaital data deculstering spatial locality parallel spatial database spatial data balancing

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Apply Hilbert spatial ordering code to partition massive spatial data in PC cluster system

Apply Hilbert spatial ordering code to partition massive spa...

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14th International Conference on Geoinformatics

作者： Wang Yongjie Hong Xinlan Meng Lingkui Zhao Chunyu Wuhan Univ Sch Remote Sensing & Informat Engn 129 Luoyu Rd Wuhan 430079 Peoples R China Zhejiang Highway Machinery Tech Sch Hangzhou 310023 Peoples R China

ISBN: (纸本)9780819465290

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.

关键词： cluster technique parallel spatial database Hilbert spatial ordering code cluster analysis spatial partitioning

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An Improved Hilbert Curve for parallel spatial Data Partitioning

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Geo-spatial Information Science 2007年第4期10卷 282-286页

作者： MENG Lingkui HUANG Changqing ZHAO Chunyu LIN Zhiyong School of Remote Sensing and Information Engineering Wuhan University 129 Luoyu Road Wuhan 430079 China

A novel Hilbert-curve is introduced for parallel spatial data partitioning, with consideration of the huge-amount property of spatial information and the variable-length characteristic of vector data items. Based on the improved Hilbert curve, the algorithm can be designed to achieve almost-uniform spatial data partitioning among multiple disks in parallel spatial databases. Thus, the phenomenon of data imbalance can be significantly avoided and search and query efficiency can be enhanced.

关键词： parallel spatial database spatial data partitioning data imbalance Hilbert curve

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