MayBMS: A Database Management System for Uncertain and Probabilistic Data

Uncertain data arises often in practice. Examples include scientific databases, data integration, sensor data management, as well as scenarios where information is manually entered and is therefore prone to mistakes and incompleteness. MayBMS is a probabilistic database management system. Its main features include (among others) a powerful query language for processing and transforming uncertain data, space-efficient representation and storage, and support for data cleaning.

Efficient query processing techniques for probabilistic databases, which are also integrated in MayBMS, form the subject of a more specific project called SPROUT.

News

• June 2, 2011: The first book on the foundations of Probabilistic Databases by Dan Suciu, Dan Olteanu, Christopher Re, and Christoph Koch has been published by Morgan & Claypool!
• May 2011: An article on probabilistic databases authored by Robert and Dan is due to appear next month in the 10th anniversary edition of The Digit Magazine, the largest selling technology magazine in India, with a certified readership of 200,000.
• June 2009: MayBMS to be demonstrated at ACM SIGMOD 2009. See our demo paper MayBMS: A Probabilistic Database Management System and 3-minute video!
• March 2009: MayBMS 2.1 beta is released on sourceforge!
• April 2008: "Fast and Simple Relational Processing of Uncertain Data" by Antova, Jansen, Koch, and Olteanu is the runner-up to the best paper award at IEEE ICDE 2008! (full paper, acceptance rate for full papers: 12.1%)
• April 2008: "MayBMS: A System for Managing Large Uncertain and Probabilistic Databases" by Antova, Koch, and Olteanu wins First Prize in Poster Contest, Spring'08 DB/IR Day at Columbia University.

Resources

• System, manual, worked-out examples, and publications are publicly available at maybms.sourceforge.net.

Team

MayBMS is a joint research project led by Professor Christoph Koch of EPFL. Dan Olteanu of the University of Oxford is co-Investigator.

Oxford Alumni: Jiewen Huang (research student), Tibor Barna (3rd year student).