MayBMS

MayBMS - A Probabilistic Database Management System

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Latest version: 2.1-beta

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What is MayBMS?

The MayBMS system (note: MayBMS is read as “maybe-MS”, like DBMS) is a complete probabilistic database management system that leverages robust relational database technology: MayBMS is an extension of the Postgres server backend. MayBMS is open source and the source code is available under the BSD license.

MayBMS stands alone as a complete probabilistic database management system that supports a powerful, compositional query language for which nevertheless worst-case efficiency and result quality guarantees can be made. The MayBMS backend is accessible through several APIs, with efficient internal operators for computing and managing probabilistic data.

In summary, MayBMS has the following features:

  • Full support of all features of PostgreSQL 8.3.3, including unrestricted query functionality, query optimization, APIs, updates, concurrency control and recovery, etc.
  • Essentially no performance loss on PostgreSQL 8.3.3 functionality: After parsing a query or DML statement, a fast syntactic check is made to decide whether the statement uses the extended functionality of MayBMS. If it does not, the subsequently executed code is exactly that of PostgreSQL 8.3.3.
  • Support for efficiently creating and updating probabilistic databases, i.e., uncertain databases in which degrees of belief can be associated with uncertain data.
  • A powerful query and update language for processing uncertain data that gracefully extends SQL with a small number of well-designed language constructs.
  • State-of-the-art efficient techniques for exact and approximate probabilistic inference.

Applications

Database systems for uncertain and probabilistic data promise to have many applications. Query processing on uncertain data occurs in the contexts of data warehousing, data integration, and of processing data extracted from the Web. Data cleaning can be fruitfully approached as a problem of reducing uncertainty in data and requires the management and processing of large amounts of uncertain data. Decision support and diagnosis systems employ hypothetical (what-if) queries. Scientific databases, which store outcomes of scientific experiments, frequently contain uncertain data such as incomplete observations or imprecise measurements. Sensor and RFID data is inherently uncertain. Applications in the contexts of fighting crime or terrorism, tracking moving objects, surveillance, and plagiarism detection essentially rely on techniques for processing and managing large uncertain datasets. Beyond that, many further potential applications of probabilistic databases exist and will manifest themselves once such systems become available.

The MayBMS distribution comes with a number of examples that illustrate its use in these application domains. Some of these examples are described in the tutorial chapter of our manual.

Research

  • Find publications and slides related to the MayBMS project at its academic homepage.

Acknowledgments

The MayBMS project is supported by grant IIS-0812272 of the US National Science Foundation.