Rules are objects too: A knowledge model for an active, object-oriented database system

  • Umeshwar Dayal
  • Alejandro P. Buchmann
  • Dennis R. McCarthy
Formalization And Indusion Of Rules
Part of the Lecture Notes in Computer Science book series (LNCS, volume 334)


Event-Condition-Action (ECA) Rules are proposed as a general mechanism for providing active database capabilities in support of applications that require timely response to critical situations. These rules generalize mechanisms such as assertions, triggers, alerters, database procedures, and production rules that have previously been proposed for supporting such DBMS functions as integrity control, access control, derived data management. and inferencing. This paper argues that ECA rules should be thought of as first class objects in an object-oriented data model. It identifies concepts for modelling the components and properties of rule objects: events (database operations, temporal events, abstract signals from arbitrary user processes, and complex events constructed from these primitive ones); conditions (queries over the database): actions (programs in the query language or some programming language); and coupling modes (which describe whether the event, condition, and action components of a rule should be executed in a single transaction or in separate transactions). The paper discusses the association of timing constraints and contingency plans with rules. Finally, it describes operations on rule objects. The emphasis of the paper is on modelling concepts, rather than on specific syntax.


Composite Event Contingency Plan Active Database Rule Firing Primitive Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

5. References

  1. ADIB81.
    M. Adiba. “Derived Relations: A Unified Mechanism for Views, Snapshots, and Distributed Data,” Proceedings 7th International Conference on Very Large Data Bases., 1981.Google Scholar
  2. ALON88.
    R. Alonso, D. Barbara, H. Garcia-Molina, and S. Abad. “Quasi-Copies: Efficient Data Sharing for Information Retrieval Systems.” Advances in Database Technology — EDBT'88, (J.W. Schmidt, S. Ceri, M. Missikoff, eds.), Lecture Notes in Computer Science 303, Springer-Verlag (1988).Google Scholar
  3. BANC86.
    F. Bancilhon and R. Ramakrishnan. “An Amateur's Introduction to Recursive Query Processing Strategies.” Proc. 1986 ACM SIGMOD Conference on Management of Data, pp. 16–52.Google Scholar
  4. BARB85.
    F. Barbic and B. Pernici. “Time Modeling in Office Information Systems.” Proc. 1985 ACM SIGMOD Conference on Management of Data. pp. 51–62.Google Scholar
  5. BLAK86.
    J. Blakely, P. Larson, and F. Tompa. “Efficiently Updating Materialized Views.” Proc. 1986 ACM SIGMOD Conference on Management of Data, pp. 61–71.Google Scholar
  6. BOBR77.
    D. Bobrow and T. Winograd. “An Overview of KRL, A Knowledge Representation Language.” Cognitive Science 1(1), 1977, pp. 3–46.Google Scholar
  7. BOBR83.
    D. Bobrow and M. Stefik. The Loops Manual, Intelligent Systems Laboratory, Xerox Corporation, 1983.Google Scholar
  8. BUCH88.
    A. Buchmann and U. Dayal. “Constraint and Exception Handling for Design, Reliability, and Maintainability.” ASME Symposium ” Engineering Database Management: Emerging Issues, San Francisco, August 1988.Google Scholar
  9. BUNE79.
    P. Buneman and E. Clemons. “Efficiently Monitoring Relational Databases.” ACM Trans. on Database Systems 4, 3 (September 1979), pp. 368–382.Google Scholar
  10. CASA88.
    M.A. Casanova, A.L. Furtado, L. Tuckerman. “Enforcing Inclusion Dependencies and Referential Integrity.” Proceedings 14th International Conference on Very Large Data Bases., 1986, pp. 384–391.Google Scholar
  11. CODA73.
    CODASYL Data Description Language Committee. CODASYL Data Description Language Journal of Development June 1973. NBS Handbook 113 (1973).Google Scholar
  12. DARN87.
    M. Darnovsky, J. Bowman. “TRANSACT-SQL User's Guide.” Document 3231-2.1. Sybase Inc., 1987.Google Scholar
  13. DATE83.
    C.J. Date. An Introduction to Database Systems, Volume II. Addison-Wesley. Reading. Massachusetts, 1983.Google Scholar
  14. DAYA85.
    U. Dayal et al. “PROBE — A Research Project in Knowledge-Oriented Database Systems: Preliminary Analysis.” Technical Report CCA-85-03, Computer Corporation of America. July 1985.Google Scholar
  15. DAYA88a.
    U. Dayal et al. “The HiPAC Project: Combining Active Databases and Timing Constraints.” SIGMOD RECORD 17. No. 1 (March 1988).Google Scholar
  16. DAYA88b.
    U. Dayal et al “HiPAC: a Research Project in Active. Time-Constrained Database Management, Interim Report.” Technical Report CCA-88-02, Computer Corporation of America, June 1988.Google Scholar
  17. ESWA75.
    K. P. Eswaran and D. D. Chamberlain. “Functional Specifications of a Subsystem for Data Base Integrity.” Proc. 1st International Conference on Very Large Data Bases (September 1975).Google Scholar
  18. ESWA76.
    K. P. Eswaran. “Specifications, Implementations, and Interactions of a Trigger Subsystem in an Integrated Data Base System.” IBM Research Report RJ1820 (August 1976).Google Scholar
  19. FORG77.
    C.L. Forgy and J. McDermott. “OPS — A Domain-Independent Production System Language.” Proc. Fifth International Conf. on Artificial Intelligence, Cambridge, Massachusetts (1977).Google Scholar
  20. HANS87.
    E. Hanson. “A Performance Analysis of View Materialization Strategies.” Proceedings of the 1987 ACM SIGMOD Conference on Management of Data, (May 1987), pp 440–453.Google Scholar
  21. HEW173.
    C. Hewitt, P. Bishop, and R. Steiger. “A universal modular ACTOR formalism for artificial intelligence.” Proc. 3rd International Joint Conference on Artificial Intelligence. 1973, pp235–245. 1975.Google Scholar
  22. HUDS86.
    S. Hudson and R. King. “CACTIS: A Database System for Specifying Functionally-Defined Data.” Proc. 1st International Workshop on Object-Oriented Database Systems (September 1986), pp.26–37.Google Scholar
  23. HSU88.
    M. Hsu, R. Ladin, and D. McCarthy. “An Execution Model for Active Data Base Management Systems.” Proc. 3rd International Conference on Data and Knowledge Bases (June 1988).Google Scholar
  24. KEE85.
    Intellicorp. KEE Software Development System User's Manual (1985).Google Scholar
  25. KOEN81.
    S. Koenig and R. Paige. “A Transformational Framework for the Automatic Control of Derived Data.” Proc. 7th International Conference on Very Large Data Bases (September 1981), pp. 306–318.Google Scholar
  26. KOTZ88.
    A Kotz, K. Dittrich, and J. Mulle. “Supporting Semantic Rules by a Generalized Event/Trigger Mechanism.” Proc. International Conference on Extending Database Technology (March 1988).Google Scholar
  27. LIND86.
    B. Lindsay, L. Haas, and C. Mohan. “A Snapshot Differential Refresh Algorithm.” Proc. 1986 ACM SIGMOD Conference on Management of Data, pp. 53–60.Google Scholar
  28. MANO86a.
    F. Manola and U. Dayal. “PDM: An Object-Oriented Data Model.” Proc. 1st International Workshop on Object-Oriented Database Systems (September 1986).Google Scholar
  29. MANO86b.
    F. Manola and J. Orenstein. “Toward a general Spatial Data Model for an Object-Oriented DBMS.” Proc. 12th International Conference on Very Large Data Bases, Kyoto, Japan, August 1986.Google Scholar
  30. MINS75.
    M. Minsky. “A Framework for Representing Knowledge.” in The Psychology of Computer Vision (P. Winston, ed.). McGraw-Hill: New York (1975).Google Scholar
  31. MORG83.
    M. Morgenstern. “Active Databases as a paradigm for Enhanced Computing Environments.” Proceedings 9th International Conference on Very Large Data Bases, 1983, pp. 34–42.Google Scholar
  32. RASC88.
    L. Raschid, S.W. Su. “A Transaction-Oriented Mechanism to Control Processing in a Knowledge Base Management System.” Proceedings 2nd International Conference on Expert Database System. 1988, pp. 163–174.Google Scholar
  33. ROSE88.
    A. Rosenthal and U. Chakravarthy. “Anatomy of a Modular Multiple Query Optimizer”, VLDB88. Los Angeles. Sept. 1988.Google Scholar
  34. ROUS82.
    N. Roussopoulos. “View Indexing in Relational Databases.” ACM Trans. on Database Systems 7. No. 2, pp. 258–290 (June 1982).Google Scholar
  35. SELL88.
    T. Sellis and N. Roussopoulos. “Deep Compilation of Large Rule Bases.” Proc. 2nd International Conference on Expert Database Systems (April 1988).Google Scholar
  36. STON75.
    M. Stonebraker. “Implementation of Integrity Constraints and Views by Query Modification.” Proc. 1975 ACM SIGMOD Conference on Management of Data (May 1975).Google Scholar
  37. STON82.
    M Stonebraker et al. “A Rules System for a Relational Data Base Management System.” Proc. 2nd International Conference on Databases, Jerusalem, June 1982.Google Scholar
  38. STON85.
    M. Stonebraker, “Triggers and Inference In Database Systems.” On Knowledge Base Management Systems, Brodie and Mylopoulos (Eds.), Springer-Verlag (1986).Google Scholar
  39. STON86.
    M. Stronebraker, E. Hanson, and C.-H. Hong. “The Design of the POSTGRES Rules System.” in The POSTGRES Papers (M. Stonebraker and L.A. Rowe, eds.), Memorandum No. UCB/ERL/M86/85, Electronics Research Laboratory, University of California, Berkeley, California (November 1986).Google Scholar
  40. ZLOO82.
    M. Zloof. “Office-by-example: a business language that unifies data and word processing and electronic mail.” IBM Systems Journal 21, No. 3, pp. 272–304 (1982).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Umeshwar Dayal
    • 1
  • Alejandro P. Buchmann
    • 1
  • Dennis R. McCarthy
    • 1
  1. 1.Computer Corporation of AmericaCambridge

Personalised recommendations