A Knowledge-driven Data Warehouse Model for Analysis Evolution
Cecile J. Favre, Fadila Bentayeb, Omar Boussaïd · 2006
Abstract. A data warehouse is built by collecting data from external sources. Sev-eral changes on contents and structures can usually happen on these sources. There-fore, these changes have to be reflected in the data warehouse using schema updat-ing or versioning. However a data warehouse has also to evolve according to new users ’ analysis needs. In this case, the evolution is rather driven by knowledge than by data. In this paper, we propose a Rule-based Data Warehouse (R-DW) model, in which rules enable the integration of users ’ knowledge in the data warehouse. The R-DW model is composed of two parts: one fixed part that contains a fact table related to its first level dimensions, and a second evolving part, defined by means of rules. These rules are used to dynamically create dimension hierarchies, allow-ing the analysis contexts evolution, according to an automatic and concurrent way. Our proposal provides flexibility to data warehouse’s evolution by increasing users’ interaction with the decision support system.