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Modifications of Uncertain Data: A Bayesian Framework for Belief Revision

Author

Listed:
  • Debabrata Dey

    (University of Washington, School of Business Administration, Department of Management Science, Seattle, Washington 98195-3200)

  • Sumit Sarkar

    (University of Texas at Dallas, School of Management, P.O. Box 830688, JO44, Richardson, Texas 75803-0688)

Abstract

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 be closed , consistent , and complete .

Suggested Citation

  • Debabrata Dey & Sumit Sarkar, 2000. "Modifications of Uncertain Data: A Bayesian Framework for Belief Revision," Information Systems Research, INFORMS, vol. 11(1), pages 1-16, March.
    • Handle: RePEc:inm:orisre:v:11:y:2000:i:1:p:1-16
    DOI: 10.1287/isre.11.1.1.11785
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    Cited by:

    1. Kunpeng Zhang & Wendy Moe, 2021. "Measuring Brand Favorability Using Large-Scale Social Media Data," Information Systems Research, INFORMS, vol. 32(4), pages 1128-1139, December.
    2. Debabrata Dey, 2003. "Record Matching in Data Warehouses: A Decision Model for Data Consolidation," Operations Research, INFORMS, vol. 51(2), pages 240-254, April.
    3. Sandeep Purao & Veda C. Storey & Taedong Han, 2003. "Improving Analysis Pattern Reuse in Conceptual Design: Augmenting Automated Processes with Supervised Learning," Information Systems Research, INFORMS, vol. 14(3), pages 269-290, September.

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