MCDB-R: Risk Analysis in the Database

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.

• Type: Journal
• Year: 2010
• Where: PVLDB
• Authors: Peter J. Haas, Christopher M. Jermaine, Subi Arumugam, Fei Xu, Luis Leopoldo Perez, Ravi Jampani
• Keywords: Data Values, Probability Distribution, PVLDB 2010, Uncertain Data Values