Fuzzy prophet: parameter exploration in uncertain enterprise scenarios

Authors:
Oliver A. Kennedy;Steve Lee;Charles Loboz;Slawek Smyl;Suman Nath
Affiliations:
EPFL, Lausanne, Switzerland;Microsoft Corporation, Seattle, WA, USA;Microsoft Corporation, Seattle, WA, USA;Microsoft Corporation, Seattle, WA, USA;Microsoft Research, Seattle, WA, USA
Venue:
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Year:
2011

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Abstract

We present Fuzzy Prophet, a probabilistic database tool for constructing, simulating and analyzing business scenarios with uncertain data. Fuzzy Prophet takes externally defined probability distribution (so called VG-Functions) and a declarative description of a target scenario, and performs Monte Carlo simulation to compute probability distribution of the scenario's outcomes. In addition, Fuzzy Prophet supports parameter optimization,where probabilistic models are parameterized and a large parameter space must be explored to find parameters that optimize or achieve a desired goal. Fuzzy Prophet's key innovation is to use 'fingerprints' that can identify parameter values producing correlated outputs of a user-provided stochastic function and to reuse computations across such values. Fingerprints significantly expedite the process of parameter exploration in offline optimization and interactive what-if exploration tasks.