A spatial data partition algorithm based on statistical cluster

J. Ye, Bin Chen, Jian Chen, Yu Fang, Liang Wu
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引用次数: 4

Abstract

Nowadays, high performance parallel computation is deemed as a good solution to the complicated processing of massive spatial data. It is a very important precondition to make the most of this technology that data be partitioned. In this paper, we talk about the general strategy of spatial data partition and summarize its principles are good space proximity, balanced data load, small data redundancy and short time consumed. After analyzing the current partition algorithms, we find that there are many partition problems, such as the space division and load unbalanced. In order to solve these problems, we presented a new partition algorithm based on the statistical cluster method, which has better spatial proximity and data load than traditional algorithms.
一种基于统计聚类的空间数据分区算法
目前,高性能并行计算被认为是解决海量空间数据复杂处理的一个很好的方法。数据分区是充分利用该技术的一个重要前提。本文讨论了空间数据分区的一般策略,总结了空间数据分区的原则是空间接近性好、数据负载均衡、数据冗余小、耗时短。通过对现有分区算法的分析,发现分区存在空间划分和负载不均衡等问题。为了解决这些问题,我们提出了一种新的基于统计聚类方法的分区算法,该算法比传统算法具有更好的空间接近性和数据负载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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