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This chapter is in the scope of static and dynamic discovery-driven explorations of a data cube. It presents different methods to facilitate the whole process of data exploration. Each kind of analysis (static or dynamic) is developed for either a count measure or a quantitative measure. Both are based on the calculation, on the fly, of specific statistical built-in indicators. Firstly, a global methodology is proposed to help a dynamic discovery-driven exploration. It aims at identifying the most relevant dimensions to expand. A built-in rank on dimensions is restituted interactively, at each step of the process. Secondly, to help a static discovery-driven exploration, generalized statistical criteria are detailed to detect and highlight interesting cells within a cube slice. The cell’s degree of interest is determined by the calculation of either test-value or Chi-Square contribution. Their display is done by a color-coding system. A proof of concept implementation on the ORACLE 10g system is described at the end of the chapter.

OLAP applications are widely used by business analysts as a decision support tool. While exploring the cube, end-users are rapidly confronted by analyzing 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. This paper fits in the scope of discovery-driven dynamic exploration. It presents a method coupling OLAP technologies and mining techniques to facilitate the whole process of exploration of the data cube by identifying the most relevant dimensions to expand. At each step of the process, a built-in rank on dimensions is restituted to the users. It is performed through indicators computed on the fly according to the user-defined data selection. A proof of the implementation of this concept on the Oracle 10g system is described at the end of the paper.

International audience; Discovering unexpected rules is essential, particularly for in- dustrial applications with marketing stakes. In this context, many works have been done for association rules. How- ever, non of them address sequences. In this paper, we thus propose to discover unexpected multidimensional sequential rules in data cubes. We define the concept of multidimen- sional sequential rule, and then unexpectedness. We formal- ize these concepts and define an algorithm for mining this kind of rules. Experiments on a real data cube are reported and highlight the interest of our approach.

In large companies, On-Line Analytical Processing (OLAP) technologies are widely used by business analysts as a decision support tool. Nevertheless, while exploring the cube, analysts are rapidly confronted by analyzing a huge number of visible cells to identify the most interesting ones. Coupling OLAP technologies and mining methods may help them by the automation of this tedious task. In the scope of discovery-driven exploration, this paper presents two methods to detect and highlight interesting cells within a cube slice. The cell's degree of interest is based on the calculation of either test-value or Chi-Square contribution. Indicators are computed instantaneously according to the user-defined dimensions drill-down. Their display is done by a colorcoding system. A proof of concept implementation on the ORACLE 10g system is described at the end of the paper.

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.