Abstract
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.
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Cariou, V., Cubillé, J., Derquenne, C., Goutier, S., Guisnel, F., Klajnmic, H. (2008). Built-In Indicators to Discover Interesting Drill Paths in a Cube. In: Song, IY., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2008. Lecture Notes in Computer Science, vol 5182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85836-2_4
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DOI: https://doi.org/10.1007/978-3-540-85836-2_4
Publisher Name: Springer, Berlin, Heidelberg
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