Parallelizing multidimensional index structures

K. Kanth, D. Agrawal, A. E. Abbadi, Ambuj K. Singh, Terence R. Smith
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引用次数: 4

Abstract

Indexing multidimensional data is inherently complex leading to slow query processing. This behavior becomes more pronounced with the increase in database size and/or number of dimensions. In this paper we address this issue by processing an index structure in parallel. First, we study different ways of partitioning an index structure. We then propose efficient algorithms for processing each query in parallel on the index structure. Using these strategies, we parallelized two multidimensional index structures-R* and LIB and evaluated the performance gains for the Gazetteer and the Catalog data of the Alexandria Digital Library on the Meiko CS-2.
并行化多维索引结构
索引多维数据本身就很复杂,导致查询处理缓慢。随着数据库大小和/或维数的增加,这种行为变得更加明显。在本文中,我们通过并行处理索引结构来解决这个问题。首先,我们研究了索引结构的不同划分方法。然后,我们提出了在索引结构上并行处理每个查询的有效算法。使用这些策略,我们并行化了两个多维索引结构——r *和LIB,并评估了在Meiko CS-2上对亚历山大数字图书馆的gazeteer和Catalog数据的性能增益。
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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