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Article Dans Une Revue International Journal of Decision Support System Technology Année : 2015

Facilitate effective decision-making by warehousing reduced data

Résumé

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.
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Dates et versions

hal-03193310 , version 1 (23-11-2023)

Identifiants

Citer

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⟩
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