A Bayesian Framework for Modifications of Probabilistic Relational Data

The inherent uncertainty pervasive over the real world often forces business decisions to be made using uncertain data. The conventional relational model does not have the ability to handle uncertain data. In recent years, several approaches have been proposed in the literature for representing uncertain data by extending the relational model, primarily using probability theory. However, the aspect of database modification has been overlooked in these investigations. It is clear that any modification of existing probabilistic data, based on new information, amounts to the revision of one’s belief about real world objects. In this paper, we examine the aspect of belief revision and develop a generalized algorithm that can be used for modification of existing data in a probabilistic relational database.