Towards reducing the multidimensionality of OLAP cubes using the Evolutionary Algorithms and Factor Analysis Methods

TL;DR

This paper proposes a hybrid method combining Genetic Algorithms and Factor Analysis to reduce the dimensionality of OLAP cubes, aiming to improve data analysis efficiency in data warehouses.

Contribution

It introduces a novel hybrid approach using GAs and MCA for effective data reduction in OLAP cubes, focusing on selecting relevant dimensions.

Findings

Reduces the number of dimensions in OLAP cubes effectively.

Improves computational efficiency in data warehouse analysis.

Provides a method to identify profiles closest to reference points.

Abstract

Data Warehouses are structures with large amount of data collected from heterogeneous sources to be used in a decision support system. Data Warehouses analysis identifies hidden patterns initially unexpected which analysis requires great memory and computation cost. Data reduction methods were proposed to make this analysis easier. In this paper, we present a hybrid approach based on Genetic Algorithms (GA) as Evolutionary Algorithms and the Multiple Correspondence Analysis (MCA) as Analysis Factor Methods to conduct this reduction. Our approach identifies reduced subset of dimensions from the initial subset p where p'<p where it is proposed to find the profile fact that is the closest to reference. GAs identify the possible subsets and the Khi formula of the ACM evaluates the quality of each subset. The study is based on a distance measurement between the reference and n facts profile extracted from the Warehouses.