Skip to main content
SpringerLink
Log in

FUN: An Efficient Algorithm for Mining Functional and Embedded Dependencies

  • Conference paper
  • First Online:
Database Theory — ICDT 2001 (ICDT 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1973))

Included in the following conference series:

Abstract

Discovering functional dependencies from existing databases is an important technique strongly required in database design and administration tools. Investigated for long years, such an issue has been recently addressed with a data mining viewpoint, in a novel and more efficient way by following from principles of level-wise algorithms. In this paper, we propose a new characterization of minimal functional dependencies which provides a formal framework simpler than previous proposals. The algorithm, defined for enforcing our approach has been implemented and experimented. It is more efficient (in whatever configuration of original data) than the best operational solution (according to our knowledge): the algorithm Tane. Moreover, our approach also performs (without additional execution time) the mining of embedded functional dependencies, i.e. dependencies holding for a subset of the attribute set initially considered (e.g. for materialized views widely used in particular for managing data warehouses).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A.I. Verkamo. Fast Discovery of Association Rules. Advances in Knowledge Discovery and Data Mining, pages 307–328, 1996.

    Google Scholar 

  2. R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules. In Proc. VLDB’94, pages 487–499, Santiago, Chile, September 1994.

    Google Scholar 

  3. W.W. Armstrong. Dependency Structures of Database Relationships. In Proc. IFIP Conf., pages 580–583, Amsterdam, The Netherlands, 1974. North-Holland.

    Google Scholar 

  4. W.W. Armstrong and C. Delobel. Decompositions and Functional Dependencies in Relations. ACM TODS, 5(4):404–430, Dec 1980.

    Article  MATH  Google Scholar 

  5. C. Beeri and P.A. Bernstein. Computational Problems Related to the Design of Normal Form Relational Schemas. ACM TODS, 4(1):30–59, 1979.

    Article  Google Scholar 

  6. C. Beeri, M. Dowd, R. Fagin, and R. Statman. On the Structure of Armstrong Relations for Functional Dependencies. Journal of the ACM, 31(1):30–46, 1984.

    Article  MATH  MathSciNet  Google Scholar 

  7. G. Birkhoff. Lattices Theory. Coll. Pub. XXV, vol. 25, 3rd edition, 1967.

    Google Scholar 

  8. D. Bitton, J. Millman, and S. Torgersen. A Feasability and Performance Study of Dependency Inference. In Proc. ICDE’89, pages 635–641, 1989.

    Google Scholar 

  9. S. Chaudhuri. Data Mining and Database Systems: Where is the Intersection? Data Engineering Bulletin, 21(1):4–8, 1998.

    Google Scholar 

  10. R.H.L. Chiang, T.M. Barron, and V.C. Storey. Reverse Engineering of Relational Databases: Extraction of an EER Model from a Relational Database. DKE, 10(12):107–142, 1994.

    Article  Google Scholar 

  11. E.F. Codd. Further Normalization of the Data Base Model. Technical Report 909, IBM, 1971.

    Google Scholar 

  12. S.S. Cosmadakis, P.C. Kanellakis, and N. Spyratos. Partition Semantics for Relations. Journal of Computer and System Sciences, 33(2):203–233, 1986.

    Article  MATH  MathSciNet  Google Scholar 

  13. P.C. Fisher, J.H. Hou, and D.M. Tsou. Succinctness in Dependency Systems. TCS, 24:323–329, 1983.

    Article  Google Scholar 

  14. G. Gottlob. Computing Covers for Embedded Functional Dependencies. In Proc. ACM-SIGACT-SIGMOD-SIGART’87, pages 58–69, San Diego, US, 1987.

    Google Scholar 

  15. G. Gottlob and L. Libkin. Investigations on Armstrong Relations, Dependency Inference, and Excluded Functional Dependencies. Acta Cybernetica, 9(4):385–402, 1990.

    MATH  MathSciNet  Google Scholar 

  16. J. Gryz. Query Folding with Inclusion Dependencies. In Proc. ICDE’98, pages 126–133, Orlando, US, Feb 1998.

    Google Scholar 

  17. Y. Huhtala, J. Karkkainen, P. Porkka, and H. Toivonen. Efficient Discovery of Functional and Appproximate Dependencies. In Proc. ICDE’98, pages 392–401, Orlando, US, Feb 1998.

    Google Scholar 

  18. Y. Huhtala, J. Karkkainen, P. Porkka, and H. Toivonen. TANE: An Efficient Algorithm for Discovering Functional and Approximate Dependencies. The Computer Journal, 42(2):100–111, 1999.

    Article  MATH  Google Scholar 

  19. M. Kantola, H. Mannila, K.R. Räihä, and H. Siirtola. Discovering Functional and Inclusion Dependencies in Relational Databases. International Journal of Intelligent Systems, 7:591–607, 1992.

    Article  MATH  Google Scholar 

  20. J. Kivinen and H. Mannila. Approximate Dependency Inference from Relations. TCS, 149(1):129–149, 1995.

    Article  MATH  MathSciNet  Google Scholar 

  21. M. Levene. A Lattice View of Functional Dependencies in Incomplete Relations. Acta Cyberbernetica, 12:181–207, 1995.

    MATH  MathSciNet  Google Scholar 

  22. M. Levene and G. Loizou. Axiomatisation of Functional Dependencies in Incomplete Relations. TCS, 206(1–2):283–300, 1998.

    Article  MATH  MathSciNet  Google Scholar 

  23. M. Levene and G. Loizou. Database Design for Incomplete Relations. ACM TODS, 24(1):80–125, 1999.

    Article  MathSciNet  Google Scholar 

  24. M. Levene and G. Loizou. A Guided Tour of Relational Databases and Beyond. Springer-Verlag, London, 1999.

    Google Scholar 

  25. S. Lopes, J.M. Petit, and L. Lakhal. Efficient Discovery of Functional Dependencies and Armstrong Relations. In Proc. EDBT’00, pages 350–364, 2000.

    Google Scholar 

  26. H. Mannila and K.J. Räihä. Design by Example: An Application of Armstrong Relations. Journal of Computer and System Sciences, 33(2):126–141, Oct 1986.

    Article  MATH  MathSciNet  Google Scholar 

  27. H. Mannila and K.J. Räihä. On the Complexity of Inferring Functional Dependencies. Discrete Applied Mathematics, 40:237–243, 1992.

    Article  MATH  MathSciNet  Google Scholar 

  28. H. Mannila and K.J. Räihä. Algorithms for Inferring Functional Dependencies from Relations. DKE, 12(1):83–99, 1994.

    Article  MATH  Google Scholar 

  29. H. Mannila and K.J. Räihä. The Design of Relational Databases. Addison Wesley, 1994.

    Google Scholar 

  30. H. Mannila and H. Toivonen. Levelwise Search and Borders of Theories in Knowledge Discovery. Data Mining and Knowledge Discovery, 1(3):241–258, 1997.

    Article  Google Scholar 

  31. V.M. Markowitz and J.A. Makowsky. Identifying Extended Entity-Relationship Object Structure in Relational Schemas. IEEE Transactions on Software Engineering, 16(8):777–790, August 1990.

    Article  Google Scholar 

  32. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering Frequent Closed Itemsets for Association Rules. In Proc. ICDT’99, LNCS, Vol. 1540, Springer Verlag, pages 398–416, Jan 1999.

    Google Scholar 

  33. J.M. Petit, F. Toumani, J.F. Boulicaut, and J. Kouloumdjian. Towards the Reverse Engineering of Denormalized Relational Databases. In Proc. ICDE’96, pages 218–227, Feb 1996.

    Google Scholar 

  34. X. Qian. Query Folding. In Proc. ICDE’96, pages 48–55, Feb 1996.

    Google Scholar 

  35. I. Savnik and P.A. Flach. Bottom-up Induction of Functional Dependencies from Relations. In Proc. AAAI’93, pages 174–185, 1993.

    Google Scholar 

  36. A.M. Silva and M.A. Melkanoff. A Method for Helping Discover the Dependencies of a Relation, pages 115–133. Plenum. Advances in Data Base Theory, 1981.

    Google Scholar 

  37. N. Spyratos. The Partition Model: a Deductive Database Model. ACM TODS, 12(1):1–37, 1987.

    Article  Google Scholar 

  38. Z. Tari, J. Stokes, and S. Spaccapietra. Object Normal Forms and Dependency Constraints for Object-Oriented Schemata. ACM TODS, 22(4):513–569, Dec 1997.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LIM, CNRS FRE-2246, Université de la Méditerranée, Case 901 163 Avenue de Luminy, F-13288, Marseille Cedex 9, France

    Noël Novelli & Rosine Cicchetti

Authors
  1. Noël Novelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rosine Cicchetti
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Limburg University (LUC), 3590, Diepenbeek, Belgium

    Jan Van den Bussche

  2. Department of Computer Science and Engineering, University of California, 92093-0114, La Jolla, CA, USA

    Victor Vianu

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Novelli, N., Cicchetti, R. (2001). FUN: An Efficient Algorithm for Mining Functional and Embedded Dependencies. In: Van den Bussche, J., Vianu, V. (eds) Database Theory — ICDT 2001. ICDT 2001. Lecture Notes in Computer Science, vol 1973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44503-X_13

Download citation

  • DOI: https://doi.org/10.1007/3-540-44503-X_13

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41456-8

  • Online ISBN: 978-3-540-44503-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation