Business Intelligence

Ranking Continuous Probabilistic Datasets

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Executive Summary

Ranking is a fundamental operation in data analysis and decision support, and plays an even more crucial role if the dataset being explored exhibits uncertainty. This has led to much work in understanding how to rank uncertain datasets in recent years. In this paper, the authors address the problem of ranking when the tuple scores are uncertain, and the uncertainty is captured using continuous probability distributions (e.g. Gaussian distributions). They present a comprehensive solution to compute the values of a Parameterized Ranking Function (PRF) for arbitrary continuous probability distributions (and thus rank the uncertain dataset); PRF can be used to simulate or approximate many other ranking functions proposed in prior work.

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