Optimizing active databases using the split technique (Preliminary Report)

  • Serge Abiteboul
  • Allen Van Gelder
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 646)


A method to perform nonmonotonic relational rule computations is presented, called the split technique, The goal is to avoid redundant computations with rules that can insert and delete sets of tuples specified by the rule body. The method is independent of the control strategy that governs rule firing. Updatable relations are partitioned, as the computation progresses, into blocks of tuples such that tuples within a block are indiscernible from each other based on the computation so far. Results of previous rule firings are remembered as “relational equations” so that a new rule firing does not recompute parts of the result that can be determined from the existing equations. Seminaive evaluation falls out as a special case when all rules specify inserts. The method is amenable to parallelization.


Logic Program Random Graph Access Structure Deductive Database Transition Table 
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.


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  1. [ACV92]
    S. Abiteboul, K. Compton, and V. Vianu. Queries are easier than you thought (probably). In Proc. ACM Symp. on Principles of Database Systems, 1992.Google Scholar
  2. [AV90]
    S. Abiteboul and V. Vianu. Procedural languages for database queries and updates. Journal of Computer and System Sciences, 41:181–229, 1990.Google Scholar
  3. [AV91a]
    S. Abiteboul and V. Vianu. Datalog extensions for database queries and updates. Journal of Computer and System Sciences, 43:62–124, 1991.CrossRefGoogle Scholar
  4. [AV91b]
    S. Abiteboul and V. Vianu. Generic computation and its complexity. In Proc. ACM SIGACT Symp. on the Theory of Computing, pages 209–219, 1991.Google Scholar
  5. [BC79]
    O.P. Buneman and G.K. Clemons. Efficiently monitoring relational databases. ACM Trans. on Database Systems, 4(3):368–382, September 1979.Google Scholar
  6. [BFKM85]
    L. Brownston, R. Farrel, E. Kant, and N. Martin. Programming Expert Systems in OPS5. Addison Wesley, 1985.Google Scholar
  7. [BMSU86]
    F. Bancilhon, D. Maier, Y. Sagiv, and J.D. Ullman. Magic sets and other strange ways to implement logic programs. In Proc. ACM Symp. on Principles of Database Systems, pages 1–15, 1986.Google Scholar
  8. [BR86]
    F. Bancilhon and R. Ramakrishnan. An amateur's introduction to recursive query processing. In Proc. ACM SIGMOD Symp. on the Management of Data, pages 16–52, 1986.Google Scholar
  9. [dMS88]
    C. de Maindreville and E. Simon. Modelling non-deterministic queries and updates in deductive databases. In Proc. of Internat. Conf. on Very Large Databases, Los Angeles, 1988.Google Scholar
  10. [Esw76]
    K.P. Eswaran. Aspects of a trigger subsystem in an integrated data base system. In Proceedings of the 2nd International Conference in Software Engineering, San Francisco, California, pages 243–250, 1976.Google Scholar
  11. [Han89]
    E.H. Hanson. An initial report on the design of ariel: a dbms with an integrated production rule system. In Sigmod Record, 18(3), pages 12–19, 1989.Google Scholar
  12. [HJ91]
    R. Hull and D. Jacob. Language constructs for programming deductive databases. In vldb, 1991.Google Scholar
  13. [IN88]
    T. Imielinski and S. Naqvi. Explicit control of logic programs through rule algebra. In Proc. ACM Symp. on Principles of Database Systems, pages 103–116, 1988.Google Scholar
  14. [Lin90]
    S. Lindell. An analysis of fixed-point queries on binary trees. PhD thesis, UCLA, 1990.Google Scholar
  15. [MD89]
    D. McCarthy and U. Dayal. The architecture of an active database management system. In Proc. ACM SIGMOD Symp. on the Management of Data, pages 215–224, 1989.Google Scholar
  16. [MW88]
    S. Manchanda and D.S. Warren. A logic-based language for database updates. In J. Minker, editor, Foundations of Deductive Databases and Logic Programming, pages 363–394. Morgan-Kaufmann, 1988.Google Scholar
  17. [NK88]
    S. Naqvi and R. Krishnamurthy. Database updates in logic programming. In Proc. ACM Symp. on Principles of Database Systems, page ??, 1988.Google Scholar
  18. [PY92]
    C. H. Papadimitriou and M. Yannakakis. Tie-Breaking Semantics and Structural Totality. In Eleventh ACM Symposium on Principles of Database Systems, pages 16–22, 1992.Google Scholar
  19. [SZ90]
    D. Saccà and C. Zaniolo. Partial models, stable models and non-determinism in logic programs with negation. Technical report, MCC, Austin, TX, January 1990. (Extended abstract appeared in Ninth ACM Symposium on Principles of Database Systems, 1990.).Google Scholar
  20. [SHP88]
    M. Stonebraker, E. Hanson, and S. Potamianos. The POSTGRES rules system. IEEE Transactions on Software Engineering, 14(7):897–907, July 1988.Google Scholar
  21. [VG92]
    A. Van Gelder. The alternating fixpoint of logic programs with negation. Journal of Computer and System Sciences, 1992. (to appear). Abstract in PODS, 1989.Google Scholar
  22. [Vie87]
    L. Vieille. Recursive query processing: the power of logic. Theoretical Computer Science, 69(1):1–53, 1987.Google Scholar
  23. [Wid91]
    J. Widom. Deduction in the Starburst production rule system. Technical report, IBM Almaden Research, 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Serge Abiteboul
    • 1
  • Allen Van Gelder
    • 2
  2. 2.University of California at Santa Cruz and INRIAUSA

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