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Lossless Reduction of Datacubes using Partitions

机译:使用分区无损减少数据立方体

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摘要

Datacubes are especially useful for answering efficiently queries on data warehouses. Nevertheless the amount of generated aggregated data is huge with respect to the initial data which is itself very large. Recent research has addressed the issue of a summary of Datacubes in order to reduce their size. The approach presented in this paper fits in a similar trend. We propose a concise representation, called Partition Cube, based on the concept of partition and we give a new algorithm to compute it. We propose a Relational Partition Cube, a novel ROLAP cubing solution for managing Partition Cubes using the relational technology. Analytical evaluations show that the storage space of Partition Cubes is smaller than Datacubes. In order to confirm analytical comparison, experiments are performed in order to compare our approach with Datacubes and with two of the best reduction methods, the Quotient Cube and the Closed Cube.
机译:数据多维数据集对于有效回答数据仓库中的查询特别有用。然而,相对于本身非常大的初始数据而言,生成的聚合数据量巨大。最近的研究已经解决了数据立方体的摘要问题,以减小其大小。本文提出的方法也符合类似趋势。基于分区的概念,我们提出了一个简洁的表示形式,称为Partition Cube,并给出了一种新的算法来对其进行计算。我们提出了一个关系分区多维数据集,这是一种新颖的ROLAP多维数据集解决方案,用于使用关系技术来管理分区多维数据集。分析评估表明,分区多维数据集的存储空间小于数据多维数据集。为了确认分析比较,进行了实验,以便将我们的方法与Datacubes以及两种最佳还原方法进行比较,即商方和封闭方。

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