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Scalability analysis of declustering methods for multidimensional range queries

机译:多维范围查询的分簇方法的可伸缩性分析

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摘要

Efficient storage and retrieval of multi-attribute data sets has become one of the essential requirements for many data-intensive applications. The Cartesian product file has been known as an effective multi-attribute file structure for partial-match and best-match queries. Several heuristic methods have been developed to decluster Cartesian product files across multiple disks to obtain high performance for disk accesses. Although the scalability of the declustering methods becomes increasingly important for systems equipped with a large number of disks, no analytic studies have been done so far. The authors derive formulas describing the scalability of two popular declustering methods-Disk Module and Fieldwise Xor-for range queries, which are the most common type of queries. These formulas disclose the limited scalability of the declustering methods, and this is corroborated by extensive simulation experiments. From the practical point of view, the formulas given in the paper provide a simple measure that can be used to predict the response time of a given range query and to guide the selection of a declustering method under various conditions.
机译:多属性数据集的有效存储和检索已成为许多数据密集型应用程序的基本要求之一。笛卡尔乘积文件被称为用于部分匹配和最佳匹配查询的有效多属性文件结构。已经开发了几种启发式方法来跨多个磁盘对笛卡尔乘积文件进行分块以获得磁盘访问的高性能。尽管解簇方法的可伸缩性对于配备有大量磁盘的系统而言变得越来越重要,但迄今为止尚未进行任何分析研究。作者得出了描述两种流行的去簇方法(磁盘模块)和Fieldwise Xor-用于范围查询的可伸缩性的公式,这是最常见的查询类型。这些公式公开了解簇方法的可扩展性有限,并且广泛的仿真实验证实了这一点。从实际的角度来看,本文中给出的公式提供了一种简单的方法,可用于预测给定范围查询的响应时间,并指导在各种条件下的解聚方法的选择。

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