Probabilistic databases arise naturally in many data management scenarios, such as Web information extraction, data cleaning, data integration, sensor data management, and scientific databases. In this talk I present MayBMS, a state-of-the-art database management system for efficiently managing and processing large collections of probabilistic data. MayBMS is based on a clean yet expressive query language that captures many important use cases of probabilistic databases, including hypothetical query processing, managing evidence, and Bayesian inference. MayBMS employs a succinct representation system for probabilistic databases called U-relations, which unifies various approaches to representing uncertain data, such as c-tables and vertical decomposition. MayBMS follows a principled approach to leveraging the strengths of previous database research for achieving scalability. The talk covers the design of the query language, storage structures, as well as query evaluation and optimization techniques. |