"MayBMS: A System for Managing Large Uncertain and Probabilistic Databases"

Christoph Koch, Cornell University

...In this talk I present MayBMS, a database management system for efficiently managing and processing large collections of uncertain data that is currently under development at Cornell. MayBMS is based on a clean yet expressive query language that captures many important use cases of probabilistic databases, including what-if queries and the conditioning of databases using new evidence. MayBMS employs a carefully designed succinct representation system for probabilistic databases called U-relations, which nicely unifies various approaches to representing uncertain data, such as c-tables, relational, in particular vertical, decomposition, and probabilistic graphical models. U-relations allow for the natural reuse of mature relational storage, indexing and query processing techniques to build scalable probabilistic database systems...