@article{publications29412,
          volume = {7},
          number = {3},
          author = {Faten Atigui and Franck Ravat and Jiefu Song and Olivier Teste and Gilles Zurfluh},
           title = {Facilitate effective decision-making by warehousing reduced data: is it feasible?},
       publisher = {IGI Global},
         journal = {International Journal of Decision Support System Technology},
           pages = {36--64},
            year = {2015},
        keywords = {Multidimensional design - Data reduction - Experimental assessment},
             url = {https://publications.ut-capitole.fr/id/eprint/29412/},
        abstract = {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.}
}