Efficiently computing and querying multidimensional OLAP data cubes over probabilistic relational data

Authors:
Alfredo Cuzzocrea;Dimitrios Gunopulos
Affiliations:
ICAR, CNR and University of Calabria, Italy;Dept. of Informatics and Telecommunications, University of Athens, Greece
Venue:
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Year:
2010

Citing 35
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Abstract

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 and querying 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. Finally, we complete our analytical contribution by introducing an innovative Probability Distribution Function (PDF)-based approach for efficiently querying probabilistic OLAP data cubes.