Modifications of uncertain data: A Bayesian framework for belief revision

The inherent uncertainty pervasive over the real world often forces lousine ss decisions to be made using uncertain data. The conventional relational m odel does not have the ability to handle uncertain data. In recent years, s everal approaches have been proposed in the literature for representing unc ertain data by extending the relational model, primarily using probability theory. The aspect of database modification, however, has not been addresse d in prior research. It is clear that any modification of existing probabil istic data, based on new information, amounts to the revision of one's beli ef about real-world objects. In this paper, we examine the aspect of belief revision and develop a generalized algorithm that can be used for the modi fication of existing data in a probabilistic relational database. The belie f revision scheme is shown to be closed, consistent, and complete.