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
- 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
Team
MayBMS is a joint research project between Cornell University (PI: Christoph Koch) and University of Oxford (co-I: Dan Olteanu).
Oxford Alumni: Jiewen Huang (research student).
