Context-Aware Generalization for Cube Measures

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?''
Type de document :
Communication dans un congrès
DOLAP: International Workshop on Data warehousing and OLAP, 2010, Toronto, Canada. ACM, DOLAP'2010: 13th International Workshop on Data warehousing and OLAP, pp.99-104, 2010


https://hal-lirmm.ccsd.cnrs.fr/lirmm-00798821
Contributeur : Pascal Poncelet <>
Soumis le : dimanche 10 mars 2013 - 20:36:29
Dernière modification le : lundi 22 février 2016 - 18:23:17

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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: International Workshop on Data warehousing and OLAP, 2010, Toronto, Canada. ACM, DOLAP'2010: 13th International Workshop on Data warehousing and OLAP, pp.99-104, 2010. <lirmm-00798821>

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