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Rule Management in Expert Database Systems

Author

Listed:
  • Arie Segev

    (Walter A. Haas School of Business, The University of California and Information and Computing Sciences Division, Lawrence Berkeley Laboratory, Berkeley, California 94720)

  • J. Leon Zhao

    (Graduate School of Business Administration, The College of William and Mary, Williamsburg, Virginia 23187)

Abstract

Expert database systems combine database and expert systems technologies to support the effective management of both rules and data. This paper studies rule processing strategies in expert database systems involving rules that are conditional on joins of relational data. Auxiliary constructs for processing join rules are proposed, and a framework of join rule processing strategies is developed. Cost functions of several strategies are derived based on a stochastic model that characterizes the arrival processes of transactions and queries to the database. Performance evaluation shows that the proposed data constructs and strategies provide an effective method for processing rules.

Suggested Citation

  • Arie Segev & J. Leon Zhao, 1994. "Rule Management in Expert Database Systems," Management Science, INFORMS, vol. 40(6), pages 685-707, June.
  • Handle: RePEc:inm:ormnsc:v:40:y:1994:i:6:p:685-707
    DOI: 10.1287/mnsc.40.6.685
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    Citations

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    Cited by:

    1. Akhil Kumar & J. Leon Zhao, 1999. "Dynamic Routing and Operational Controls in Workflow Management Systems," Management Science, INFORMS, vol. 45(2), pages 253-272, February.
    2. Anindya Datta & Igor R. Viguier, 2000. "Handling Sensor Data in Rapidly Changing Environments to Support Soft Real-Time Requirements," INFORMS Journal on Computing, INFORMS, vol. 12(2), pages 84-103, May.
    3. Sumit Sarkar & Mysore Ramaswamy, 2000. "Knowledge Base Decomposition to Facilitate Verification," Information Systems Research, INFORMS, vol. 11(3), pages 260-283, September.

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