Skip to Main content Skip to Navigation
Conference papers

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?''
Document type :
Conference papers
Complete list of metadata

Cited literature [8 references]  Display  Hide  Download
Contributor : Pascal Poncelet <>
Submitted on : Tuesday, April 2, 2019 - 6:19:14 PM
Last modification on : Thursday, June 3, 2021 - 3:32:11 PM
Long-term archiving on: : Wednesday, July 3, 2019 - 4:45:08 PM


Files produced by the author(s)



Yoann Pitarch, Cécile Favre, Anne Laurent, Pascal Poncelet. Context-Aware Generalization for Cube Measures. DOLAP: Data Warehousing and OLAP, Oct 2010, Toronto, Canada. pp.99-104, ⟨10.1145/1871940.1871961⟩. ⟨lirmm-00798821⟩



Record views


Files downloads