基于主成分分析的数据仓库设计新方法

Wafa Tebourski, W. Karaa, H. Ghézala
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引用次数: 3

摘要

决策已成为任何企业的战略需要。事实上,这是资本业务的优先事项之一。决策信息系统的建立有利于数据的开发和分析。我们将数据仓库作为商业智能的核心系统,确保多维数据的结构化和分析。因此,数据仓库的设计已成为一个主要问题,需要开发适当的方法来实现数据仓库。在本文中,我们提出了一种基于描述统计技术的设计和构建数据仓库的方法,用于主成分分析(PCA)中多维数据的分析。本文的发现主要体现在两个方面:(i)概念模型数据仓库,(ii)确定度量和维度的算法。一个案例研究被用来验证我们的建议。
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New data warehouse designing approach based on principal component analysis
Decision making has become a strategic need for any business. Indeed, it is among the priorities of capital business. The establishment of decision information systems facilitates the data exploitation and analysis. We distinguish data warehouses as the core system of business intelligence to ensure the structuring and analysis of multidimensional data. Consequently, the design of data warehouses has become a major problem, leading to the development of appropriate approaches to implement data warehouses. In this paper, we propose an approach to design and to construct data warehouses based on a descriptive statistics technique for the analysis of multidimensional data in the Principal Components Analysis (PCA). The findings of this article appear in two main areas: (i) a conceptual model data warehouse, (ii) an algorithm for the determination of measures and dimensions. A case study is used to validate our proposal.
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