基于网格的多属性记录聚类方法性能评价

Bhaskar Himatsingka, J. Srivastava
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引用次数: 14

摘要

我们主要研究基于网格划分数据空间的多属性聚类方法。理论结果表明,对于磁盘数量大于5的范围查询,没有一种聚类方法是严格最优的。执行了详细的性能评估,以查看不同的集群方案在各种查询和数据库场景下的执行情况(相对于彼此和最优方案)。不同的参数包括查询的形状和大小、数据库大小、属性数量和磁盘数量。结果表明,关于一个关系的常见查询的信息是非常重要的,应该用于决定它的聚类,这对于小查询尤其重要。此外,没有明确的赢家,因此并行数据库系统必须支持许多解簇方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance evaluation of grid based multi-attribute record declustering methods
We focus on multi-attribute declustering methods which are based on some type of grid-based partitioning of the data space. Theoretical results are derived which show that no declustering method can be strictly optimal for range queries if the number of disks is greater than 5. A detailed performance evaluation is carried out to see how various declustering schemes perform under a wide range of query and database scenarios (both relative to each other and to the optimal). Parameters that are varied include shape and size of queries, database size, number of attributes and the number of disks. The results show that information about common queries on a relation is very important and ought to be used in deciding the declustering for it, and that this is especially crucial for small queries. Also, there is no clear winner, and as such parallel database systems must support a number of declustering methods.<>
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