Embedded indicators to facilitate the exploration of a data cube

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.