Kaoutar El Maghraoui, Gokul Kandiraju, et al.
WOSP/SIPEW 2010
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.
Kaoutar El Maghraoui, Gokul Kandiraju, et al.
WOSP/SIPEW 2010
Raymond F. Boyce, Donald D. Chamberlin, et al.
CACM
Yun Mao, Hani Jamjoom, et al.
CoNEXT 2006
Frank R. Libsch, Takatoshi Tsujimura
Active Matrix Liquid Crystal Displays Technology and Applications 1997