MCDB-R: risk analysis in the database

Authors:
Subi Arumugam;Fei Xu;Ravi Jampani;Christopher Jermaine;Luis L. Perez;Peter J. Haas
Affiliations:
University of Florida, Gainesville, FL;Microsoft Corporation, Redmond, WA;University of Florida, Gainesville, FL;Rice University, Houston, TX;Rice University, Houston, TX;IBM Research - Almaden, San Jose, CA
Venue:
Proceedings of the VLDB Endowment
Year:
2010

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Abstract

Enterprises often need to assess and manage the risk arising from uncertainty in their data. Such uncertainty is typically modeled as a probability distribution over the uncertain data values, specified by means of a complex (often predictive) stochastic model. The probability distribution over data values leads to a probability distribution over database query results, and risk assessment amounts to exploration of the upper or lower tail of a query-result distribution. In this paper, we extend the Monte Carlo Database System to efficiently obtain a set of samples from the tail of a query-result distribution by adapting recent "Gibbs cloning" ideas from the simulation literature to a database setting.