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Link to original content: https://doi.org/10.1007/3-540-45495-0_3
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Improving Query Evaluation with Approximate Functional Dependency Based Decompositions

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Advances in Databases (BNCOD 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2405))

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Abstract

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.

All authors were supported by NSF Grant IIS-0082407.

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References

  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. 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. 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. 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. 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. 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. 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. Goodman L. and Kruskal W. Measures of associations for cross classifications. Journal of the American Statistical Association, 49:732–764, 1954.

    Article  MATH  Google Scholar 

  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. 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. 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. 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. 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.

    Article  MATH  Google Scholar 

  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. 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. Kivinen J. and Mannila H. Approximate dependency inference from relations. Theoretical Computer Science, 149(1):129–149, 1995.

    Article  MATH  MathSciNet  Google Scholar 

  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. 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. Malvestuto F. Statistical treatment of the information content of a database. Information Systems, 11(3):211–223, 1986.

    Article  MATH  Google Scholar 

  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. Mannila H. and Räihä K. Algorithms for inferring functional dependencies. Data & Knowledge Engineering, 12:83–99, 1994.

    Article  MATH  Google Scholar 

  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. 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. 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. Paulley G. and Larson P. Exploiting uniqueness in query optimization. In Proc. of the 10th ICDE, pages 68–79, 1994.

    Google Scholar 

  26. Piatetsky-Shapiro G. Probabilistic data dependencies. In Machine Discovery Workshop (Aberdeen, Scotland), 1992.

    Google Scholar 

  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. 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. Ramakrishanan R. and Gehrke J. Database Management Systems 2nd Edition. McGraw-Hill Higher Education, Boston, MA, 2000.

    Google Scholar 

  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. 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. 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. 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. 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. 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. 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 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Indiana University Bloomington, IN, 47405, USA

    Chris M. Giannella & Edward L. Robertson

  2. School of Informatics, Indiana Univeristy Bloomington, IN, 47405, USA

    Mehmet M. Dalkilic & Dennis P. Groth

Authors
  1. Chris M. Giannella
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  2. Mehmet M. Dalkilic
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  3. Dennis P. Groth
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  4. Edward L. Robertson
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Editor information

Editors and Affiliations

  1. Department of Information Studies, University of Sheffield, Sheffield, S10 2TN, UK

    Barry Eaglestone

  2. Department of Computer Science, University of Sheffield, Sheffield, S10 2TN, UK

    Siobhán North

  3. Computer Science and Information Systems, Birkbeck College, Malet Street, London, WC1E 7HX, UK

    Alexandra Poulovassilis

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© 2002 Springer-Verlag Berlin Heidelberg

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Giannella, C.M., Dalkilic, M.M., Groth, D.P., Robertson, E.L. (2002). Improving Query Evaluation with Approximate Functional Dependency Based Decompositions. In: Eaglestone, B., North, S., Poulovassilis, A. (eds) Advances in Databases. BNCOD 2002. Lecture Notes in Computer Science, vol 2405. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45495-0_3

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  • DOI: https://doi.org/10.1007/3-540-45495-0_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43905-9

  • Online ISBN: 978-3-540-45495-3

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