Model-driven reverse engineering for data warehouse design

Abstract

Data warehouses integrate several operational sources to provide a multidimensional analysis of data, thus improving the decision making process. Therefore, an in-depth analysis of these data sources is crucial for data warehouse development. Traditionally, this analysis has been based on a set of informal guidelines or heuristics to support the manually discovery of multidimensional elements on a well-known documentation. Therefore, this task may become highly tedious and prone to fail. In this paper, MDA (Model Driven Architecture) is used to design a reverse engineering process in which the following tasks are performed (i) obtain a logical representation of data sources (ii) mark this logical representation with multidimensional concepts, and (iii) derive a conceptual multidimensional model from the marked model.