Search
1993 Volume 8
Article Contents
RESEARCH ARTICLE   Open Access    

An overview of production rules in database systems

More Information
  • Abstract: Database researchers have recognized that integrating a production rules facility into a database system provides a uniform mechanism for a number of advanced database features including integrity constraint enforcement, derived data maintenance, triggers, protection, version control, and others. In addition, a database system with rule processing capabilities provides a useful platform for large and efficient knowledge-base and expert systems. Database systems with production rules are referred to as active database systems, and the field of active database systems has indeed been active. This paper summarizes current work in active database systems, and suggests future research directions. Topics covered include database rule languages, rule processing semantics, and implementation issues.
  • 加载中
  • Aiken A, Widom J and Hellerstein JM, 1992. “Behaviour of database production rules: Termination, confluence, and observable determinism” In: Proceedings of the ACM SIGMOD International Conference on Management of Data, June.

    Google Scholar

    ASK Computer Co., 1992. INGRESS/SQL Reference Manual, Version 6.4.

    Google Scholar

    Beeri C and Milo T, 1991. “A model for active object oriented database” In: Proceedings of the Seventeenth International Conference on Very Large Data Bases. September

    Google Scholar

    Brownston L, Farrell R, Kant E and Martin N, 1985. Programming Expert Systems in OPS5: An introduction to Rule-Based programming, Addison-Wesley.

    Google Scholar

    Ceri S, 1992. “A declarative approach to active databases” In: Proceedings of the Eighth International Conference on Data Engineering, February.

    Google Scholar

    Ceri S., Gottlob G and Tanca L, 1990. Logic Programming and Database, Springer-Verlag.

    Google Scholar

    Ceri S and Widom J, 1990. “Deriving production rules for constraint maintenance” In: Proceedings of the Sixteenth International Conference on Very Large Data Bases, August.

    Google Scholar

    Ceri S and Widom J, 1991. “Deriving production rules for incremental view maintenance” In: Proceedings of the Seventeenth international Conference on Very Large Data Bases, September.

    Google Scholar

    Chakravarthy S, , 1989. “HiPAC: A research project in active, time-constrained database management (final report)” Technical Report XAIT-89-02, Xerox Advanced Information Technology, Cambridge, MA, August.

    Google Scholar

    Cohen D, 1989. “Compiling complex database transition triggers” In: Proceedings of the ACM SIGMOD International Confernce on Management of Data, May.

    Google Scholar

    Dayal U, , 1988. “The HiPAC project: Combining active databases and timing constraints” SIGMOD Record17(1) 51–70, 03.

    Google Scholar

    de Maindreville C and Simon E, 1988. “A production rule based approach to deductive databases” In: Proceedings of the Fourth International Conference on Data Engineering, February.

    Google Scholar

    Delcambre LML and Etheredge JN, 1988/a. “The Relational Production Language: A production language for relational databases” In: Proceedings of the Second International Conference on Expert Database Systems, April.

    Google Scholar

    Delcambre LML and Etheredge JN, 1988/b. “A self-controlling interpreter for the relational production language” In: Proceedings of the ACM SIGMOD international Conference on Management of Data, June.

    Google Scholar

    Diaz O, Patom N and Gray P, 1991. “Rule management in object-oriented databases: A uniform approach” In: Proceedings of the Seventeenth international Conference on Very Large Data Bases, September.

    Google Scholar

    Digital Equipment Corporation, 1991. Rdb/VMS – SQL Reference Manual, November.

    Google Scholar

    Eswaran KP, 1976. “Specifications, implementations and interactions of a trigger subsystem in an integrated database system” Technical Report Ri 1820, IBM Research Laboratory, San Jose, CA.

    Google Scholar

    Forgy CL, 1982. “Rete: A fast algorithm for the many pattern/many object pattern match problem” Aruficial intelligence1917–37.

    Google Scholar

    Gehani N and Jagadish HV, 1991. “Ode as an active database: Constraints and triggers” In: Proceedings of the Seventeenth international Conference on Very Large Data Bases, September.

    Google Scholar

    Gehani N, Jagadish HV and Shmueli O, 1992. “Event specification in an active object-oriented database” In: Proceedings of the ACM SIGMOD international Conference on Management of Data, June.

    Google Scholar

    Gordin DN and Pasik AJ, 1991. “Set-oriented constructs: From Rete rule bases to database systems” In: Proceedings of the ACM SIGMOD International Conference on Management of Data, May.

    Google Scholar

    Gupta A, 1987. Parallelism in Production Systems, Pitman.

    Google Scholar

    Haas L, 1990. “Starburst mid-flight: As the dust clears” IEEE Transactions on Knowledge and Data Engineering2(1) 143–160, 03.

    Google Scholar

    Hanson EN, 1992. “Rule condition testing and action execution in And” In: Proceedings of the ACM SIGMOD International Conference on Management of Data, June.

    Google Scholar

    Hanson EN, Chaabouni M, Kim C-H and Wang Y-W, 1990. “A predicate matching algorithm for database rule systems” In: Proceedings of the ACM SIGMOD International Conference on Management of Data, May.

    Google Scholar

    Hanson EN and Johnson T, 1992. “The interval skip list: A data structure for finding all intervals that overlap a point” Technical Report TR92-016, Computer and Information Sciences Department, University of Flordia, Gainesville, FL, June.

    Google Scholar

    Hedberg S and Steizner M, 1987. Knowledge Engineering Environment (KEE) System: Summary of Release 3.1 Intellicorp Inc., July.

    Google Scholar

    Howe L, 1986. “Sybase data integrity for on-line applications” Technical report, Sybase Inc.

    Google Scholar

    Kelly MA and Seviora RE, 1989. “An evaluation of DRete on CUPID for OPS5” In: Proceedings of the Eleventh International Joint Conference on Artificial Intelligence.

    Google Scholar

    Kiernan G, de Maindreville C, and Simon E, 1990. “Making a deductive database a practical technology: A step forward” In: Proceedings of the ACM SIGMOD International Conference on Management of Data, May.

    Google Scholar

    Kotz AM, Dittrich KR and Mulle JA, 1988. “Supporting semantic rules by a generalized event/trigger mechanism” In: Proceedings of the International Conference on Extending Data Base Technology, March.

    Google Scholar

    McCarthy DR and Dayal U, 1989. “The architecture of an active database management system” In: Proceedings of the ACM SIGMOD International Conference on Management of Data, June.

    Google Scholar

    Miranker DP, 1987. “TREAT: A better match algorithm for AI production systems” In: Proceedings of the AAA1 Conference on Artificial Intelligence, August.

    Google Scholar

    Morgenstern M, 1983. “Active databases as a pardigm for enhanced computing environments” In: Proceedings of the Ninth International Conference on Very Large Data Bases, October.

    Google Scholar

    Moss E. 1985. Nested Transactions: An Approach to Reliable Distributed ComputingMIT Press.

    Google Scholar

    ORACLE Corporation, 1992. ORACLE Reference Manual.

    Google Scholar

    Schreicr U, Pirahesh H, Agrawal R and Mohan C, 1991. “Alert: An architecture for transforming a passive DBMS into an active DBMS” In: Proceedings of the Seventeenth International Conference on Very Large Data Bases, September.

    Google Scholar

    Sellis T, Lin C-C and Raschid L, 1988. “Implementing large production systems in a DBMS environment: Concepts and algorithms” In: Proceedings of the ACM SIGMOD International Conference on Management of Data, June.

    Google Scholar

    Simon E, Kiernan J and de Maindreville C, 1992. “Implementing high-level active rules on top of relational databases” In: Proceedings of the Eighteenth International Conference on Very Large Data Bases, August.

    Google Scholar

    Stonebraker M, Jhingran A, Goh J and Potamianos S, 1990. “On rules, procedures, caching and views in data base systems” In: Proceedings of the ACM SIGMOD International Conference on Management of Data, May.

    Google Scholar

    Stonebraker M and Kemnitz G, 1991. “The POSTORES next-generation database management system”, Communications of the ACM34(10) 78–92, 10.

    Google Scholar

    Tzvieli A, 1988. “On the coupling of a production system shell and a DBMS” In: Proceedings of the Third International Conference oil Data and Knowledge Bases, June.

    Google Scholar

    Ullman JD, 1989. Principles of Database and Knowledge-Base Systems, Volumes I and IIComputer Science Press.

    Google Scholar

    Wang Y-W and Hanson EN, 1992. “A performance comparison of the Rete and TREAT algorithms for testing database rule conditions” In: Proceedings of the Eighth International Conference on Data Engineering, February.

    Google Scholar

    Widom J, Cochrane RJ and Lindsay BG, 1991. “Implementing set-oriented production rules as an extension to Starburst” In: Proceedings of the Seventeenth International Conference on Very Large Data Bases, September.

    Google Scholar

    Widorn J and Finkelstein SJ, 1990. “Set-oriented production rules in relational database systems” In: Proceedings of the ACM SIGMOD International Conference on Management of Data, May.

    Google Scholar

  • Cite this article

    Eric N. Hanson, Jennifer Widom. 1993. An overview of production rules in database systems. The Knowledge Engineering Review. 8:126 doi: 10.1017/S0269888900000126
    Eric N. Hanson, Jennifer Widom. 1993. An overview of production rules in database systems. The Knowledge Engineering Review. 8:126 doi: 10.1017/S0269888900000126

Article Metrics

Article views(40) PDF downloads(387)

Other Articles By Authors

RESEARCH ARTICLE   Open Access    

An overview of production rules in database systems

The Knowledge Engineering Review  8 Article number: 10.1017/S0269888900000126  (1993)  |  Cite this article

Abstract: Abstract: Database researchers have recognized that integrating a production rules facility into a database system provides a uniform mechanism for a number of advanced database features including integrity constraint enforcement, derived data maintenance, triggers, protection, version control, and others. In addition, a database system with rule processing capabilities provides a useful platform for large and efficient knowledge-base and expert systems. Database systems with production rules are referred to as active database systems, and the field of active database systems has indeed been active. This paper summarizes current work in active database systems, and suggests future research directions. Topics covered include database rule languages, rule processing semantics, and implementation issues.

    • Copyright © Cambridge University Press 19931993Cambridge University Press
References (46)
  • About this article
    Cite this article
    Eric N. Hanson, Jennifer Widom. 1993. An overview of production rules in database systems. The Knowledge Engineering Review. 8:126 doi: 10.1017/S0269888900000126
    Eric N. Hanson, Jennifer Widom. 1993. An overview of production rules in database systems. The Knowledge Engineering Review. 8:126 doi: 10.1017/S0269888900000126
  • Catalog

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return