[ARS15] Facilitate Effective Decision-Making by Warehousing Reduced Data: Is It Feasible?

Revue Internationale avec comité de lecture : Journal International Journal of Decision Support System Technology, vol. 7(7), pp. 36-64, 2015, (doi:10.4018/ijdsst.2015070103)
Résumé: Our 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, we 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, we 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, we describe our experimental assessments and discuss their results. Evaluating our solution implies executing different queries in various contexts: unreduced single fact table, unreduced relational star schema, reduced star schema and reduced snowflake schema. We show that queries are more efficiently calculated within a reduced star schema.


@article {
title="{Facilitate Effective Decision-Making by Warehousing Reduced Data: Is It Feasible?}",
author="F. Atigui and F. Ravat and J. Song and O. Teste and G. Zurfluh",
journal="International Journal of Decision Support System Technology",