MayBMS - A Probabilistic Database Management System
MAIN PAGE |
DOWNLOAD |
PUBLICATIONS
Cite: L. Antova, T. Jansen, C. Koch, and D. Olteanu. "Fast and Simple Relational Processing of Uncertain Data". Proc. 24th International Conference on Data Engineering, ICDE 2008, April 7-12, 2008, Cancun, Mexico, pp. 983-992.
Overview
MayBMS is a state-of-the-art probabilistic database management system developed as an extension of the Postgres server backend (download).
The MayBMS project is founded on the thesis that a principled effort to use and extend mature relational database technology will be essential for creating robust and scalable systems for managing and querying large uncertain datasets.
MayBMS stands alone as a complete probabilistic database management system that supports a very powerful, compositional query language (examples) for which nevertheless worst-case efficiency and result quality guarantees can be made. Central to this is our choice of essentially using probabilistic versions of conditional tables as the representation system, but in a form engineered for admitting the efficient evaluation and automatic optimization of most operations of our language using robust and mature relational database technology.
Overview Papers and Slides
- C. Koch. "MayBMS: A System for Managing Large Uncertain and Probabilistic Databases" [pdf]. To appear as Chapter 6 of Charu Aggarwal, ed., Managing and Mining Uncertain Data, Springer-Verlag, 2008/9.
- Slides on MayBMS2.
- Slides on MayBMS1.
Research
Central themes in our research include the creation of foundations of query languages for probabilistic databases by developing analogs of relational algebra and SQL and the development of efficient query processing techniques. In practice, the efficient evaluation of queries on probabilistic data requires approximation techniques, and another important goal is to understand which approximation guarantees can be made for complex, realistic query languages. Apart from data representation and storage mechanisms, a query language, and query processing techniques, our work covers query optimization, an update language, concurrency control and recovery, and APIs for uncertain data.
People
- Lyublena Antova
- Michaela Götz
- Jiewen Huang (Oxford University)
- Oliver Kennedy
- Christoph Koch
- Dan Olteanu (Oxford University)