Rule Management in Expert Database Systems

Arie Segev and J. Leon Zhao
Additional contact information
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

Management Science, 1994, vol. 40, issue 6, 685-707

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.

Keywords: rule processing; derived data; join indexing; data materialization; expert database systems (search for similar items in EconPapers)
Date: 1994
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.40.6.685 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:40:y:1994:i:6:p:685-707

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().