Generalized Normal Forms for Probabilistic Relational Data
IEEE Transactions on Knowledge and Data Engineering
Denormalization strategies for data retrieval from data warehouses
Decision Support Systems
Do web sites change customers' beliefs? A study of prior-posterior beliefs in e-commerce
Information and Management
The ecology of learning-by-building: bridging design science and natural history of knowledge
DESRIST'10 Proceedings of the 5th international conference on Global Perspectives on Design Science Research
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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. The aspect of database modification, however, has not been addressed in prior research. 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 the modification of existing data in a probabilistic relational database. The belief revision scheme is shown to beclosed,consistent, andcomplete.