计算机科学 ›› 2010, Vol. 37 ›› Issue (8): 198-200.
• 数据库与数据挖掘 • 上一篇 下一篇
贾婷,魏祖宽,唐曙光,金在弘
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JIA Ting,WEI Zu-kuan,TANG Shu-guang,KIM Jae-hhong
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摘要： 在并行空间数据库中，空间数据集在各计算节点是否聚集划分，对提高空间并行查询效率起着关键的作用。Oracle Spatial采用的基于格网的划分方法只考虑了数据集在各节点是否均衡划分，而未考虑空间数据的拓扑特征。基于空间数据聚集划分的目的，提出了一种基于K一平均聚类算法的空间数据划分方法。实验证明，该方法极大地提高了空间数据并行检索和查询效率。
关键词: K-平均算法，聚类，数据划分，分布式并行计算环境
Abstract: In parallel spatial database, it is necessary to make the spatial data set cluster in each node, because it can improve the efficiency of parallel database query. The partition approach of Oracle Spatial is based on grid. It only consi deres data sets in each node are a balanced division, without taking into account the topological characteristics of these data. In order to improve the problem, this paper presented a new spatial data partition approach which is based on K-means clustering algorithm. Experiments show that the method greatly improves the spatial data retrieval and ctuery efficiency in parallel.
Key words: K-means algorithm,Cluster,Data partition,Distributed & parallel computing
贾婷,魏祖宽,唐曙光,金在弘. 一种面向并行空间查询的数据划分方法[J]. 计算机科学, 2010, 37(8): 198-200. https://doi.org/
JIA Ting,WEI Zu-kuan,TANG Shu-guang,KIM Jae-hhong. New Spatial Data Partition Approach for Spatial Data Query[J]. Computer Science, 2010, 37(8): 198-200. https://doi.org/
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