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Built-In Indicators to Support Business Intelligence in OLAP Databases

Built-In Indicators to Support Business Intelligence in OLAP Databases

Jérôme Cubillé, Christian Derquenne, Sabine Goutier, Françoise Guisnel, Henri Klajnmic, Véronique Cariou
Copyright: © 2010 |Pages: 20
ISBN13: 9781605667485|ISBN10: 160566748X|ISBN13 Softcover: 9781616924522|EISBN13: 9781605667492
DOI: 10.4018/978-1-60566-748-5.ch006
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MLA

Cubillé, Jérôme, et al. "Built-In Indicators to Support Business Intelligence in OLAP Databases." Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, IGI Global, 2010, pp. 108-127. https://doi.org/10.4018/978-1-60566-748-5.ch006

APA

Cubillé, J., Derquenne, C., Goutier, S., Guisnel, F., Klajnmic, H., & Cariou, V. (2010). Built-In Indicators to Support Business Intelligence in OLAP Databases. In T. Nguyen (Ed.), Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications (pp. 108-127). IGI Global. https://doi.org/10.4018/978-1-60566-748-5.ch006

Chicago

Cubillé, Jérôme, et al. "Built-In Indicators to Support Business Intelligence in OLAP Databases." In Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, 108-127. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-748-5.ch006

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Abstract

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.

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