Authors:
Lucile Sautot
1
;
Sandro Bimonte
2
;
Ludovic Journaux
3
and
Bruno Faivre
3
Affiliations:
1
University of Burgundy and AgroParisTech, France
;
2
IRSTEA, France
;
3
University of Burgundy, France
Keyword(s):
Multidimensional Design, Data Warehouse, OLAP, Data Mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Data Mining
;
Data Warehouses and OLAP
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Strategic Decision Support Systems
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.