Abstract
The Data Warehouse (DW) is characterized by complex architecture, specific modeling and design approaches. It integrates data issued from operational data sources in order to meet decision-makers' needs by providing answers for OLAP queries (On-Line Analytical Processing). In practice, both data source models and decision-makers' analytical requirements evolve over time and, therefore, lead to changes in the DW multidimensional model. In this evolving context, we have developed the DWE (Data Warehouse Evolution) framework. DWE automatically propagates the changes of the data source data-model on the DW data-model. This paper proposes a model-driven approach for extending DWE in order to consider a further related evolutionary aspect: The evolution of decision-makers' needs. It deals with the propagation of these evolutions on the DW multidimensional model. This approach relies on a classification of evolution scenarios and a set of transformation rules for the identification of evolution operations to apply on the DW.