重新审视了范围聚类数据集的并行关系数据库算法

Proceedings of the International Symposium on Parallel Architectures, Algorithms and Networks (ISPAN) Pub Date : 1994-12-14 DOI:10.1109/ISPAN.1994.367168

E. Schikuta

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引用次数: 2

摘要

目前可用的并行数据库系统使用传统的并行硬件体系结构，采用高度并行的软件体系结构。通过将存储的数据集分散到多个并行和独立的磁盘驱动器中来加快执行速度是一种新兴的技术。本文回顾了并行关系数据库的距离聚类算法。我们采用传统的已知和研究的并行算法来处理散类数据，利用划分数据集的固有顺序特性，并对算法的性能进行分析比较。结果表明，并行范围聚类变体总体上优于传统的并行变体。

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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Parallel relational database algorithms revisited for range declustered data sets

Today available parallel database systems use conventional parallel hardware architectures employing a highly parallel software architecture. It is an emerging technique to speed up the execution by declustering the stored data sets among a number of parallel and independent disk drives. In this paper we revisit parallel relational database algorithms for range declustering. We adapt the conventional known and well studied parallel algorithms for declustered data, exploit the inherent order property of the partitioned data sets and compare analytically the performance of the algorithms. It is shown that the parallel range declustered variants generally outperform their conventional parallel counterparts.<>

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Proceedings of the International Symposium on Parallel Architectures, Algorithms and Networks (ISPAN)

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