Liat Ein-Dor, Y. Goldschmidt, et al.
IBM J. Res. Dev
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. © 2010 VLDB Endowment.
Liat Ein-Dor, Y. Goldschmidt, et al.
IBM J. Res. Dev
Raghu Krishnapuram, Krishna Kummamuru
IFSA 2003
B. Wagle
EJOR
Hans Becker, Frank Schmidt, et al.
Photomask and Next-Generation Lithography Mask Technology 2004