Mixed driven Refinement design of Multidimensional models based on Agglomerative Hierarchical Clustering

Abstract : 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) (Phipps and Davis, 2002). 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) (Sautot et al., 2014b). 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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Communication dans un congrès
17th International Conference on Enterprise Information Systems (ICEIS'15), Apr 2015, Barcelona, Spain. 2015
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Lucile Sautot, Sandro Bimonte, Ludovic Journaux, Bruno Faivre. Mixed driven Refinement design of Multidimensional models based on Agglomerative Hierarchical Clustering. 17th International Conference on Enterprise Information Systems (ICEIS'15), Apr 2015, Barcelona, Spain. 2015. 〈hal-01148873〉

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