Cocktail de Cubes.
Sébastien Nedjar, Alain Casali, Rosine Cicchetti, Lotfi Lakhal · BDA · 2006
In various approaches, data cubes are pre-computed in order to answer e ciently Olap queries. Such cubes are also successfully used for multidimensional analysis of data streams. The notion of data cube has been declined in various ways : iceberg cubes, range cubes or di erential cubes. In this paper, we introduce the concept of convex cube which captures all the tuples of a datacube satisfying a constraint combination. It can be represented in a very compact way in order to optimize both computation time and required storage space. The convex cube is not an additional structure appended to the list of cube variants but we propose it as a unifying structure that we use to characterize, in a simple, sound and homogeneous way, the other quoted types of cubes. Finally, we introduce the concept of emerging cube which captures the signi cant trend inversions and is represented in a compact way, coherent with the previous characterizations. Mots cles Analyse multi-dimensionnelle, Cubes de donnees, Cubes convexes, Cubes emergents, Transversaux cubiques.