DBRank

International Workshop on Ranking in Databases

[Welcome] [Steering Committee]

Welcome to DBRank

The International Workshop on Ranking in Databases (DBRank) focuses on the semantics, the modeling and the implementation of ranking and ordering in database systems and applications. In recent years, there has been a great deal of interest in developing effective techniques for ad-hoc search and retrieval in relational databases, document and multimedia databases, scientific information systems, and so on. In particular, a large number of emerging applications require exploratory querying on such databases; examples include users wishing to search databases and catalogs of products such as homes, cars, cameras, restaurants, and photographs. To address the limitations of the traditional Boolean retrieval model in these emerging ad-hoc search and retrieval applications, Top-k queries and ranking query results are gaining increasing importance. In fact, in many of these applications, ranking is an integral part of the semantics, e.g., keyword search, similarity search in multimedia as well as document databases. The increasing importance of ranking is directly derived from the explosion in the volume of data handled by current applications. The sheer amount of data makes it almost impossible to process queries in the traditional compute-then-sort approach. Hence, ranking comes as a great tool for soliciting user preferences and data exploration.

DBRank aims at providing more insight into supporting ranking in database systems and it will be a great venue for the many research groups working on ranking worldwide, with a unique opportunity to share their experience in supporting ranking in various database systems, from relational to semi-structures and unstructured data; and on different levels from query formulation and preference modeling to query processing and optimization frameworks. The workshop covers (and is not limited to) the following topics:

Ranking relational data
Rank-aware query processing and optimization
New fundamental developments in top-k algorithms
Cost-models for top-k algorithms and operators
User preference specification and query languages
Ranking in Web and XML databases
Learning user preferences and ranking functions
Ranking in distributed and peer-to-peer databases
Ranking as a data exploration tool
Ranking queries in data streams and continuous monitoring systems
Applications of ranking and top-k retrieval from databases
Ranking multimedia data
Domain-specific ranking, e.g., in bibliographic, biological, clinical, and scientific data

Steering Committee

Gautam Das
Computer Science and Engineering Department
University of Texas at Arlington
Vagelis Hristidis
School of Computing and Information Sciences
Florida International University
Ihab F. Ilyas
David R. Cheriton School of Computer Science
University of Waterloo

Workshops