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An Efficient Preprocessing Transformation for Functional Dependencies Sets Based on the Substitution Paradigm

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Current Topics in Artificial Intelligence (TTIA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3040))

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Abstract

Functional Dependency is a fundamental notion of the Relational Model. Since the introduction of this successful theoretical framework in the 70’s, there have been several works focussed on their automated treatment. The pioneer line of this area was the use of Functional Dependencies Logics. Unfortunately, this line has presented several limitations, most of them caused by the crucial role of the transitivity paradigm in the axiomatic system. In [11] we introduce a new Functional Dependencies Logic which does not use the transitivity rule. This logic uses a new substitution rule and the design of its axiomatic system has been guided by the notion of optimality.

In this paper we show the advantages of such a logic. We introduce a pre-processing transformation which removes redundancy of a given set of Functional Dependencies and allows a more efficient further treatment by other well known indirect algorithms. Besides that, we carry out an empirical study to prove the practical benefits of our approach.

This work has been partially supported by the Cycit project 1109-2000.

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References

  1. Torgersen, S.: Automatic design of relational databases, Ph. D. Thesis. TR 89-1038 (1989)

    Google Scholar 

  2. Lee, M.L., Ling, T.W., Low, W.L.: Designing functional dependencies for XML. In: EDTB 2002 Proceedings 2287. LNCS, pp. 124–141.

    Google Scholar 

  3. Armstrong, W.W.: Dependency structures of data base relationships. In: Proc. IFIP Congress, pp. 580–583. North Holland, Amsterdam (1974)

    Google Scholar 

  4. Atzeni And, P., Antonellis, V.D.: Relational Database Theory. The Benjamin/ Cummings Publishing Company Inc. (1993)

    Google Scholar 

  5. Ausiello, G., D’Atri, A., Sacca, G.: Graph Algorithms for Functional Dependenciy Manipulation. J. ACM 30(4) (1983)

    Google Scholar 

  6. Bell, D.A.: From data properties to evidence. IEEE Transactions on Knowledge and Data Engireering 5(6), 965–968 (1993)

    Article  Google Scholar 

  7. Bell And, D.A., Guan, J.W.: Computational methods for rought classifications and discovery. J. American Society for Information Sciences. Special issue on Data Minig (2004)

    Google Scholar 

  8. Buneman, P., Fan, W., SimÉÉ, J.R.O.M., Weinstein, S.: Constraints for semistructured data and XML. SIGMOD Record (ACM Special Interest Group on Management of Data) 30(1), 47–54 (2001)

    Google Scholar 

  9. Choobineh And, J., Venkatraman, S.S.: A methodology and tool for derivation of functional dependencies from business forms. Information Systems 17(3), 269–282 (1992)

    Article  Google Scholar 

  10. Codd, E.F.: The Relational Model for Database Management: Version 2. Addison Wesley, Reading (1990)

    Google Scholar 

  11. Cordero, P., Enciso, M., Guzmán, I.P.D., Mora, A.: SLFD Logic: Elimination of data redundancy in Knowledge Representation. In: Garijo, F.J., Riquelme, J.-C., Toro, M. (eds.) IBERAMIA 2002. LNCS (LNAI), vol. 2527, pp. 141–150. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  12. De Miguel, A., Piattini, M., Marcos, E.: Diseño de Bases de Datos Relacionales. In: Ra-ma, ed. (1999)

    Google Scholar 

  13. Diederich And, J., Milton, J.: New Methods and Fast Algorithms for Database Normalization. ACM Transactions on Database Systems 13(3), 339–365 (1988)

    Article  Google Scholar 

  14. Fagin, R.: Functional Dependencies in a Relational Database and Propositional Logic, IBM. Journal of research and development 21(6), 534–544 (1977)

    MATH  MathSciNet  Google Scholar 

  15. Flach And, P.A., Savnik, I.: Database dependency discovery: A machine learning approach. AI communications 12, 139–160 (1999)

    MathSciNet  Google Scholar 

  16. Guan And, J.W., Bell, D.A.: Rough computational methods for information systems. Artificial Intelligence 105, 77–103 (1998)

    Article  Google Scholar 

  17. Huhtala, Y., KÄRkÄInen, J., Porkka, P., Toivonen, H.: TANE: An efficient algorithm for discovering functional and approximate dependencies. The computer Journal 42(2) (1999)

    Google Scholar 

  18. Ibaraki, T., Kogan, A., Makino, K.: Functional dependencies in Horn theories. Artificial Intelligence 108(1-2), 1–30 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  19. Ibaraki, T., Kogan, A., Makino, K.: On functional dependencies in q-Horn theories. Artificial Intelligence 131, 171–187 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  20. Lopes, S., Petit, J.-M., Toumani, F.: Discovering interesting inclusion dependencies: application to logical database tuning. Information Systems 27(1), 1–19 (2002)

    Article  MATH  Google Scholar 

  21. Mannila And, H., Raiha, K.-J.: Algorithms for Inferring Functional Dependencies from Relations. Data and Knowledge Engineering 12, 83–99 (1994)

    Article  Google Scholar 

  22. Orre, R., Lansner, A., Bate, A., Lindquist, M.: Bayesian neural networks with confidence estimations applied to data mining. Computational Statistics & Data Analysis 34, 473–493 (2000)

    Article  MATH  Google Scholar 

  23. Paredaens, J., De Bra, P., Gyssens, M., Van Gucht, D.V.: The Structure of the Relational Database Model. In: EATCS Monographs on Theoretical Computer Science (1989)

    Google Scholar 

  24. Pawlak, Z.: Rough Set: theoretical aspects of reasoning about data. Kluwer, Dordercht (1991)

    MATH  Google Scholar 

  25. Ullman, J.D.: Database and Knowledge-Base Systems. Computer Science Press, Rockville (1988)

    Google Scholar 

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Authors and Affiliations

  1. E.T.S.I. Informática, Universidad de Málaga, 29071, Málaga, Spain

    Ángel Mora, Manuel Enciso, Pablo Cordero & Inmaculada Pérez de Guzmán

Authors
  1. Ángel Mora
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  2. Manuel Enciso
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  3. Pablo Cordero
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  4. Inmaculada Pérez de Guzmán
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Editor information

Editors and Affiliations

  1. Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga,  

    Ricardo Conejo

  2. Facultad de Informática, Dept. Lenguajes y Sistemas Informáticos Codigo, EPV/EHU, Postal 649, 20080, San Sebastian, Spain

    Maite Urretavizcaya

  3. Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Campus de Teatinos s/n, 29079, Málaga, SPAIN

    José-Luis Pérez-de-la-Cruz

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Mora, Á., Enciso, M., Cordero, P., Pérez de Guzmán, I. (2004). An Efficient Preprocessing Transformation for Functional Dependencies Sets Based on the Substitution Paradigm. In: Conejo, R., Urretavizcaya, M., Pérez-de-la-Cruz, JL. (eds) Current Topics in Artificial Intelligence. TTIA 2003. Lecture Notes in Computer Science(), vol 3040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25945-9_14

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  • DOI: https://doi.org/10.1007/978-3-540-25945-9_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22218-7

  • Online ISBN: 978-3-540-25945-9

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