Title: Embedded indicators to facilitate the exploration of a data cube

Authors: Veronique Cariou, Jerome Cubille, Christian Derquenne, Sabine Goutier, Francoise Guisnel, Henri Klajnmic

Addresses: ENITIAA, Rue de la Geraudiere, BP 82225, 44322 Nantes Cedex 3, France. ' EDF Research and Development, 1 avenue du General de Gaulle, 92 141 Clamart Cedex, France. ' EDF Research and Development, 1 avenue du General de Gaulle, 92 141 Clamart Cedex, France. ' EDF Research and Development, 1 avenue du General de Gaulle, 92 141 Clamart Cedex, France. ' EDF Research and Development, 1 avenue du General de Gaulle, 92 141 Clamart Cedex, France. ' EDF Research and Development, 1 avenue du General de Gaulle, 92 141 Clamart Cedex, France

Abstract: In large companies, Online Analytical Processing (OLAP) technologies are widely used by business analysts as a decision-support tool. The exploration of the data is performed using operators such as drill-down, roll-up or slice. While exploring the cube, end-users are rapidly confronted with analysing a huge number of drill-paths according to the different dimensions. Generally, analysts are only interested in a small part of them, which corresponds to either high statistical associations between dimensions or atypical cell values. Moreover, identifying the most interesting cells is a matter for business analysts. Coupling OLAP technologies and mining methods may help them by the automation of this tedious task. This paper, in the scope of discovery-driven exploration, presents a method to facilitate the whole process of exploration of the data cube by identifying the most relevant dimensions to expand. A built-in rank on dimensions is displayed, at each step of the process, to the users, who are still free to choose the right dimension to expand for their analysis. Built-in rank on dimensions is performed through indicators computed on the fly according to the user-defined data selection. We present how this methodology offers a support to the decision-making, directly integrated to a commercial OLAP management system. A proof of concept implementation on the ORACLE 10g system is described at the end of the paper.

Keywords: online analytical processing; discovery-driven exploration; data cubes; OLAP mining; embedded indicators; data mining; statistical associations; Oracle 10g.

DOI: 10.1504/IJBIDM.2009.029083

International Journal of Business Intelligence and Data Mining, 2009 Vol.4 No.3/4, pp.329 - 349

Published online: 03 Nov 2009 *

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