International Business Machines Corporation

University of Florida Research Foundation, Incorporated

William Marsh Rice University

Christopher M Jermaine

Peter Haas

Fei Xu

Luis L Perez

Ravindranath Jampani

Mingxi Wu

Subramanian Arumugam

According to one embodiment of the present invention, a method for managing uncertain data is provided. The method includes specifying data uncertainty using at least one variable generation (VG) function. The VG function generates pseudorandom samples of uncertain data values. A random database based on the VG function is specified and multiple Monte Carlo instantiations of the random database are generated. Using a Monte Carlo method, a query is repeatedly executed over the multiple Monte Carlo instantiations to output a Monte Carlo method result and associated query-results. The Monte Carlo method result may then be used to estimate statistical properties of a probability distribution of the query-result.

Managing uncertain data using Monte Carlo techniques

A risk analysis system and method are provided. The system includes an analyzer for analyzing database instances by executing a query on each database instance and selecting a cutoff value. The analyzer also discards the sets of uncertainty data that yield query-result values below the cutoff value and retains the database instances that yield query-result values above the cutoff value as elite sets. The system also includes a cloner to replicate the elite sets, and a sampler to modify the elite sets so that each elite set is mutually statistically independent while still yielding query-result values above the cutoff value.

Risk analysis for data-intensive stochastic models

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Ocala, FL, United States of America

Christopher M Jermaine