J.P. Locquet, J. Perret, et al.
SPIE Optical Science, Engineering, and Instrumentation 1998
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.
J.P. Locquet, J. Perret, et al.
SPIE Optical Science, Engineering, and Instrumentation 1998
Xinyi Su, Guangyu He, et al.
Dianli Xitong Zidonghua/Automation of Electric Power Systems
Michael D. Moffitt
ICCAD 2009
Gal Badishi, Idit Keidar, et al.
IEEE TDSC