Context-Aware Generalization for Cube Measures

Yoann Pitarch 1 C. Favre 2 Anne Laurent 3 Pascal Poncelet 3
1 INFO/TATOO
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 INFO/TATOO
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Hierarchies are crucial for analysis in data warehouses. But they can hardly be defined on measure attributes. In this paper, we tackle this issue and we show that measure generalizations often depend on a context. For instance, a given blood pressure can be either low, normal or high regarding not only the collected measure but also characteristics of the patient such as the age. The contribution of this paper is threefold. (1) Thanks to an external database storing the expert knowledge, we propose an effective solution for considering these hierarchies. (2) In order to efficiently manage this knowledge, a Rich Internet Application is developed. (3) Finally, in order to provide a flexible analysis, query rewriting module is proposed. Thus, it is possible to answer queries such as: "Who had a low blood pressure last night?''
Document type :
Conference papers
DOLAP'2010: 13th International Workshop on Data warehousing and OLAP, Toronto, Canada. ACM, pp.99-104, 2010


http://hal-lirmm.ccsd.cnrs.fr/lirmm-00798821
Contributor : Pascal Poncelet <>
Submitted on : Sunday, March 10, 2013 - 8:36:29 PM
Last modification on : Friday, March 27, 2015 - 12:28:18 PM

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  • HAL Id : lirmm-00798821, version 1

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Yoann Pitarch, C. Favre, Anne Laurent, Pascal Poncelet. Context-Aware Generalization for Cube Measures. DOLAP'2010: 13th International Workshop on Data warehousing and OLAP, Toronto, Canada. ACM, pp.99-104, 2010. <lirmm-00798821>

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