A novel framework for estimating OLAP queries over uncertain and imprecise multidimensional data streams is introduced and experimentally assessed in this paper. We complete our theoretical contributions by means of an innovative approach for providing theoretically-founded estimates to OLAP queries over uncertain and imprecise multidimensional data streams that exploits the well-recognized probabilistic estimators theory. Finally, we provide an experimental assessment and analysis of the performance of our framework against several classes of synthetic data stream sets.
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