In this PhD project, we study multimedia query processing, and in particular its implications on database design. We assume a modern extensible database system such as Illustra or Monet. By extending the database, new representations of the multimedia data can be used, and advanced search techniques can be incoorporated in the database architecture.
From a user perspective, the main unsolved problem is how to make use of these different representations and techniques to fulfill an information need. We propose that a multimedia query processor must provide an iterative query process using relevance feedback. Also, the query processor must identify which of the available representations are most promising for answering the query. In addition, it should combine evidence from different sources.
Recently, we have started to design and implement a prototype database system that can provide this functionality to the user. In particular, we focus on information retrieval using Bayesian reasoning over a concept space of automatically generated clusters.