Uncertain data often arises 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:
- A powerful query language for processing and transforming uncertain data
- Space-efficient representation and storage
- Support for data cleaning
- Efficient query evaluation
- Updates
MayBMS is a joint research project with Cornell University and headed by Professor Christoph Koch of Cornell University Database Group. The Oxford team consists of Dan Olteanu (co-I) and Jiewen Huang (research student). Efficient query processing techniques for probabilistic databases form also the subject of a more specific project called SPROUT.
MayBMS resources: system prototype , official Web site with publications.
Publications and software of the Oxford team can also be found on Dan Olteanu's OUCL pages.
