6533b828fe1ef96bd128837f
RESEARCH PRODUCT
Mixed Driven Refinement Design of Multidimensional Models based on Agglomerative Hierarchical Clustering
Bruno FaivreLudovic JournauxLucile SautotSandro Bimontesubject
[SDE] Environmental SciencesMultidimensional designData Warehouse[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingOLAPbusiness.industryComputer scienceOnline analytical processingCLUSTERING HIERARCHIQUEVolume (computing)Functional requirementcomputer.software_genreData warehouseData-driven[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingApplication domain[SDE]Environmental SciencesBusiness intelligenceData MiningData mining[SDE.BE]Environmental Sciences/Biodiversity and EcologybusinessDesign methodscomputerdescription
20 pages; International audience; 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-04-27 | Proceedings of the 17th International Conference on Enterprise Information Systems |