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Communication Dans Un Congrès Année : 2015

Mixed driven refinement design of multidimensional models based on agglomerative hierarchical clustering

Méthodologie mixte de conception de schémas multidimensionnels basée sur le clustering hiérarchique

Résumé

Data warehouses (DW) and OLAP systems are business intelligence technologies allowing the on-line analysis of huge volume of data according to users’ needs. The success of DW projects essentially depends on the design phase where functional requirements meet data sources (mixed design methodology). However, when dealing with complex applications existing design methodologies seem inefficient since decision-makers define functional requirements that cannot be deduced from data sources (data driven approach) and/or they have not sufficient application domain knowledge (user driven approach). Therefore, in this paper we propose a new mixed refinement design methodology where the classical data-driven approach is enhanced with data mining to create new dimensions hierarchies. A tool implementing our approach is also presented to validate our theoretical proposal.
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Dates et versions

hal-02601687 , version 1 (16-05-2020)

Identifiants

Citer

L. Sautot, S. Bimonte, L. Journaux, B. Faivre. Mixed driven refinement design of multidimensional models based on agglomerative hierarchical clustering. 17th International Conference on Enterprise Information Systems, Apr 2015, Barcelone, Spain. pp.547-555. ⟨hal-02601687⟩
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