Abstract
AbstractFocusing 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.KeywordsProbability Distribution FunctionRange QueryProbabilistic DataQuery EvaluationData CubeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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