Computing multidimensional OLAP data cubes over probabilistic relational data: A decomposition approach

Focusing on novel database application scenarios, where datasets arise more and more in uncertain and imprecise formats, in this paper we propose a novel framework for efficiently computing multidimensional OLAP data cubes over probabilistic data, which well-capture previous kinds of data. Several models and algorithms supported in our proposed framework are formally presented and described in details, based on well-understood theoretical statistical/ probabilistic tools, which converge to the definition of the so-called probabilistic OLAP data cubes, the most prominent result of our research.