Facilitate effective decision-making by warehousing reduced data

Faten Atigui (1) , Franck Ravat (2, 3) , Jiefu Song (2, 3) , Olivier Teste (2, 4, 5) , Gilles Zurfluh (2, 3)

1 CEDRIC - ISID - CEDRIC. Ingénierie des Systèmes d'Information et de Décision
2 IRIT-SIG - Systèmes d’Informations Généralisées
3 UT Capitole - Université Toulouse Capitole
4 UT2J - Université Toulouse - Jean Jaurès
5 IUT Toulouse 2 Blagnac

The authors' aim is to provide a solution for multidimensional data warehouse's reduction based on analysts' needs which will specify aggregated schema applicable over a period of time as well as retain only useful data for decision support. Firstly, they describe a conceptual modeling for multidimensional data warehouse. A multidimensional data warehouse's schema is composed of a set of states. Each state is defined as a star schema composed of one fact and its related dimensions. The derivation between states is carried out through combination of reduction operators. Secondly, they present a meta-model which allows managing different states of multidimensional data warehouse. The definition of reduced and unreduced multidimensional data warehouse schema can be carried out by instantiating the meta-model. Finally, they describe their experimental assessments and discuss their results. Evaluating their solution implies executing different queries in various contexts: unreduced single fact table, unreduced relational star schema, reduced star schema and reduced snowflake schema. The authors show that queries are more efficiently calculated within a reduced star schema.

Domaines: Informatique [cs]

Faten Atigui, Franck Ravat, Jiefu Song, Olivier Teste, Gilles Zurfluh. Facilitate effective decision-making by warehousing reduced data: is it feasible?. International Journal of Decision Support System Technology, 2015, 7 (3), pp.36-64. ⟨10.4018/ijdsst.2015070103⟩. ⟨hal-03193310⟩