Improving Query Evaluation with Approximate Functional Dependency Based Decompositions

  • Chris M. Giannella
  • Mehmet M. Dalkilic
  • Dennis P. Groth
  • Edward L. Robertson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2405)


We investigate how relational restructuring may be used to improve query performance. Our approach parallels recent research extending semantic query optimization (SQO), which uses knowledge about the instance to achieve more efficient query processing. Our approach differs, however, in that the instance does not govern whether the optimization may be applied; rather, the instance governs whether the optimization yields more efficient query processing. It also differs in that it involves an explicit decomposition of the relation instance. We use approximate functional dependencies as the conceptual basis for this decomposition and develop query rewriting techniques to exploit it. We present experimental results leading to a characterization of a well-defined class of queries for which improved processing time is observed.


Functional Dependency Soft Constraint Query Evaluation Query Optimization Relation Symbol 
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.


  1. 1.
    Bell S. Deciding distinctiveness of query results by discovered constraints. Proc. of the 2nd International Conf. on the Practical Application of Constraint Technology, pages 399–417, 1996.Google Scholar
  2. 2.
    Cavallo R. and Pittarelli M. The theory of probabilistic databases. In Proceedings of the 13th International Conference on Very Large Databases (VLDB), pages 71–81, 1987.Google Scholar
  3. 3.
    Chakravarthy U., Grant J., and Minker J. Logic-based approach to semantic query optimization. ACM Transactions on Database Systems, 15(2): 162–207, June 1990.Google Scholar
  4. 4.
    Dalkilic M. and Robertson E. Information dependencies. In Proc. of the Nineteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS), pages 245–253. ACM, 2000.Google Scholar
  5. 5.
    Giannella C., Dalkilic M., Groth D., and Robertson E. Using horizontal-vertical decompositions to improve query evaluation. Technical report, Computer Science, Indiana University, Bloomington, Indiana, USA, 2002.Google Scholar
  6. 6.
    Godfrey P., Grant J., Gryz J., and Minker J. Logics for Databases and Information Systems, pages 265–307. Kluwer Academic Publishers, Boston, MA, 1998.Google Scholar
  7. 7.
    Godfrey P., Gryz J., and Zuzarte C. Exploiting constraint-like data characterizations in query optimization. In Proc. 2001 ACM-SIGMOD Int. Conf. Management of Data, pages 582–592, May 2001.Google Scholar
  8. 8.
    Goodman L. and Kruskal W. Measures of associations for cross classifications. Journal of the American Statistical Association, 49:732–764, 1954.zbMATHCrossRefGoogle Scholar
  9. 9.
    Hammer M. and Zdonik S. Jr. Knowledge-based query processing. In Proc. of the Sixth Intl. Conf. on Very Large Data Bases, pages 137–147, Montreal, Canada, Oct. 1980.Google Scholar
  10. 10.
    Hsu C. and Knoblock C. Using inductive learning to generate rules for semantic query optimization. In Fayyad U. and Piatetsky-Shapiro G., editor, Advances in Knowledge Discovery and Data Mining, 1996.Google Scholar
  11. 11.
    Huhtala Y., Kärkkäinen J., Porkka P., and Toivonen H. Efficient discovery of functional and approximate dependencies using partitions. In Proceedings 14th International Conference on Data Engineering, pages 392–401. IEEE Computer Society Press, February 1998.Google Scholar
  12. 12.
    Jarke J., Clifford J., and Vassiliou Y. An optimizing PROLOG front-end to a relational query system. In Proc. of the ACM SIGMOD Conf., pages 296–306, 1984.Google Scholar
  13. 13.
    Kantola M., Mannila H., Räihä K., and Siirtola H. Discovering functional and inclusion dependencies in relational databases. International Journal of Intelligent Systems, 7:591–607, 1992.zbMATHCrossRefGoogle Scholar
  14. 14.
    King J. QUIST-A system for semantic query optimization in relational databases. In Proc. of the 7th International Conf. on Very Large Data Bases Cannes, pages 510–517, Cannes, France, Sept. 1981. IEEE Computer Society Press.Google Scholar
  15. 15.
    King J. Reasoning about access to knowledge. In Proc. of the Workship on Data Abstraction, Databases, and Conceptual Modelling, pages 138–140. SIGPLAN Notices, Jan. 1981.Google Scholar
  16. 16.
    Kivinen J. and Mannila H. Approximate dependency inference from relations. Theoretical Computer Science, 149(1):129–149, 1995.zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Lee T. An information-theoretic analysis of relational databases-part I: Data dependencies and information metric. IEEE Transactions on Software Engineering, SE-13(10):1049–1061, October 1987.Google Scholar
  18. 18.
    Lopes S., Petit J., and Lakhal L. Efficient discovery of functional dependencies and Armstrong relations. In Lecture Notes in Computer Science 1777 (first appeared in the Proceedings of the Seventh International Conference on Extending Database Technology (EDBT)), pages 350–364, 2000.Google Scholar
  19. 19.
    Malvestuto F. Statistical treatment of the information content of a database. Information Systems, 11(3):211–223, 1986.zbMATHCrossRefGoogle Scholar
  20. 20.
    Mannila H. and Räihä K. Dependency inference. In Proceedings of the 13th International Conference on Very Large Databases (VLDB), pages 155–158, 1987.Google Scholar
  21. 21.
    Mannila H. and Räihä K. Algorithms for inferring functional dependencies. Data & Knowledge Engineering, 12:83–99, 1994.zbMATHCrossRefGoogle Scholar
  22. 22.
    Nambiar K. Some analytic tools for the design of relational database systems. In Proceedings of the 6th International Conference on Very Large Databases (VLDB), pages 417–428, 1980.Google Scholar
  23. 23.
    Novelli N., Cicchetti R. FUN: An efficient algorithm for mining functional and embedded dependencies. In Lecture Notes in Computer Science 1973 (Proceedings of the 8th International Conference on Database Theory (ICDT)), pages 189–203, 2001.Google Scholar
  24. 24.
    Paulley G. Exploiting Functional Dependence in Query Optimization. PhD thesis, Dept. of Computer Science, University of Waterloo, Waterloo, Ontario, Canada, Sept. 2000.Google Scholar
  25. 25.
    Paulley G. and Larson P. Exploiting uniqueness in query optimization. In Proc. of the 10th ICDE, pages 68–79, 1994.Google Scholar
  26. 26.
    Piatetsky-Shapiro G. Probabilistic data dependencies. In Machine Discovery Workshop (Aberdeen, Scotland), 1992.Google Scholar
  27. 27.
    Pirahesh H., Hellerstein J., and Hasan W. Extensible/rule based query rewrite optimization in starburst. In Proc. 1992 ACM-SIGMOD Int. Conf. Management of Data, pages 39–48, May 1992.Google Scholar
  28. 28.
    Pirahesh H., Leung T.Y., and Hasan W. A rule engine for query transformation in starburst and IBM DB2 C/S DBMS. In Proc. 13th Int. Conf. on Data Engineering (ICDE), pages 391–400, April 1997.Google Scholar
  29. 29.
    Ramakrishanan R. and Gehrke J. Database Management Systems 2nd Edition. McGraw-Hill Higher Education, Boston, MA, 2000.Google Scholar
  30. 30.
    Sagiv Y. Quadratic algorithms for minimizing join in restricted relational expressions. SIAM Journal of Computing, 12(2):316–328, May 1983.Google Scholar
  31. 31.
    Shekhar S., Hamidzadeh B., Kohli A., and Coyle M. Learning transformation rules for semantic query optimization: a data-driven approach. IEEE Transactions on Knowledge and Data Engineering, 5(6):950–964, December 1993.Google Scholar
  32. 32.
    Shenoy S. and Ozsoyglu Z. A system for semantic query optimization. In Proc. 1987 ACM-SIGMOD Int. Conf. Management of Data, pages 181–195, May 1987.Google Scholar
  33. 33.
    Shenoy S. and Ozsoyglu Z. Design and implementation of a semantic query optimizer. IEEE Transactions on Knowledge and Data Engineering, 1(3):344–361, Sept. 1989.Google Scholar
  34. 34.
    Sun W. and Yu C. Semantic query optimization for tree and chain queries. IEEE Transactions on Knowledge and Data Engineering, 6(1):136–151, February 1994.Google Scholar
  35. 35.
    Wyss C., Giannella C., and Robertson E. FastFDs: A heuristic-driven, depth-first algorithm for mining functional dependencies from relation instances. In Lecture Notes in Computer Science 2114 (Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery), pages 101–110, 2001.Google Scholar
  36. 36.
    Yu C. and Sun W. Automatic knowledge acquisition and maintenance for semantic query optimization. IEEE Transactions on Knowledge and Data Engineering, 1(3):362–374, September 1989.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Chris M. Giannella
    • 1
  • Mehmet M. Dalkilic
    • 2
  • Dennis P. Groth
    • 2
  • Edward L. Robertson
    • 1
  1. 1.Department of Computer ScienceIndiana University BloomingtonUSA
  2. 2.School of InformaticsIndiana Univeristy BloomingtonUSA

Personalised recommendations