The GMD Data Model for Multidimensional Information: A Brief Introduction

In this paper we introduce a novel data model for multidimensional information, GMD, generalising the MD data model first proposed in Cabibbo et al (EDBT-98). The aim of this work is not to propose yet another multidimensional data model, but to find the general precise formalism encompassing all the proposals for a logical data model in the data warehouse field. Our proposal is compatible with all these proposals, making therefore possible a formal comparison of the differences of the models in the literature, and to study formal properties or extensions of such data models. Starting with a logic-based definition of the semantics of the GMD data model and of the basic algebraic operations over it, we show how the most important approaches in DW modelling can be captured by it. The star and the snowflake schemas, Gray’s cube, Agrawal’s and Vassiliadis’ models, MD and other multidimensional conceptual data can be captured uniformly by GMD. In this way it is possible to formally understand the real differences in expressivity of the various models, their limits, and their potentials.