MayBMS: A System for Managing Large Uncertain and Probabilistic Databases

MayBMS is a state-of-the-art probabilistic database management system that has been built as an extension of Postgres, an open-source relational database management system. MayBMS follows a principled approach to leveraging the strengths of previous database research for achieving scalability. This article describes the main goals of this project, the design of query and update language, efficient exact and approximate query processing, and algorithmic and systems aspects. Acknowledgments. My collaborators on the MayBMS project are Dan Olteanu (Oxford University), Lyublena Antova (Cornell), Jiewen Huang (Oxford), and Michaela Goetz (Cornell). Thomas Jansen and Ali Baran Sari are alumni of the MayBMS team. I thank Dan Suciu for the inspirational talk he gave at a Dagstuhl seminar in February of 2005, which triggered my interest in probabilistic databases and the start of the project. I am also indebted to Joseph Halpern for insightful discussions. The project was previously supported by German Science Foundation (DFG) grant KO 3491/1-1 and by funding provided by the Center for Bioinformatics (ZBI) at Saarland University. It is currently supported by NSF grant IIS-0812272, a KDD grant, and a gift from Intel.