Colloquium
"MayBMS: A System for Managing Large Uncertain and Probabilistic Databases"
Christoph Koch, Cornell University
Thursday, January 24, 2008 at 4:00 P.M.
Room 368 (CIT 3rd Floor)
Databases that contain uncertain data arise naturally in many data management scenarios, such as Web information extraction, data cleaning, data integration, sensor data management, and scientific databases. There are currently no scalable systems for managing and querying such databases. In this talk I present MayBMS, a database management system for efficiently managing and processing large collections of uncertain data that is currently under development at Cornell. MayBMS is based on a clean yet expressive query language that captures many important use cases of probabilistic databases, including what-if queries and the conditioning of databases using new evidence. MayBMS employs a carefully designed succinct representation system for probabilistic databases called U-relations, which nicely unifies various approaches to representing uncertain data, such as c-tables, relational, in particular vertical, decomposition, and probabilistic graphical models. U-relations allow for the natural reuse of mature relational storage, indexing and query processing techniques to build scalable probabilistic database systems. In addition to the exact processing of probabilistic database queries on U-relations, I will discuss the approximability and efficient approximation of expressive, compositional queries on probabilistic databases.
Bio:
Christoph Koch is Associate Professor of Computer Science at Cornell University. He is interested in both the theoretical and systems-oriented aspects of data management, and currently works on managing uncertain data, community data management systems, data-driven games, and Web information extraction and management.Host: Ugur Cetintemel
| Page Owner: Webmaster | Last Modified: Fri Jan 4 14:31:54 2008 |