Abstract
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 color-coding system. A proof of concept implementation 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. (2007). Built-In Indicators to Automatically Detect Interesting Cells in a Cube. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2007. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74553-2_12
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DOI: https://doi.org/10.1007/978-3-540-74553-2_12
Publisher Name: Springer, Berlin, Heidelberg
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