Skip to main content
SpringerLink
Log in

Fuzzy Functional Dependencies in Multiargument Relationships

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6113))

Included in the following conference series:

Abstract

A multiargument relationship may be formally presented using the relational notation: R(X 1, X 2, ... , X n ), where R is the name of the relationship, and attributes X i denote keys of entity sets which participate in it. The dependencies between all n attributes describe the integrity constraints and must not be infringed. They constitute a restriction for relationships of fewer attributes. In the paper an analysis of fuzzy functional dependencies between attributes of R is presented. Attribute vales are given by means of possibility distributions.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Motro, A.: Imprecision and uncertainty in database systems. In: Bosc, P., Kacprzyk, J. (eds.) Studies in Fuzziness: Fuzziness in Database Management Systems, pp. 3–22. Physica Verlag, Heidelberg (1995)

    Google Scholar 

  2. Dey, D., Sarkar, S.: Generalized Normal Forms for Probabilistic Relational Data. IEEE Transactions on Knowledge and Data Engineering 14(3), 485–497 (2002)

    Article  Google Scholar 

  3. Motro, A.: Accommodating Imprecision in Database Systems: Issues and Solutions. SIGMOD RECORD 19(4), 69–74 (1990)

    Article  Google Scholar 

  4. Petry, F.: Fuzzy Databases: Principles and Applications. Kluwer Academic Publishers, Boston (1996)

    MATH  Google Scholar 

  5. Fedrizzi, M., Kacprzyk, J.: A brief introduction to fuzzy sets. In: Bosc, P., Kacprzyk, J. (eds.) Studies in Fuzziness: Fuzziness in Database Management Systems, pp. 59–67. Physica Verlag, Heidelberg (1995)

    Google Scholar 

  6. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  7. Galindo, J., Urrutia, A., Piattini, M.: Fuzzy Databases. Modeling, Design and Implementation. Idea Group Publishing, London (2005)

    Google Scholar 

  8. van Gyseghem, N., de Caluwe, R.: Imprecision and uncertainty in UFO database model. Journal of the American Society for Information Science 49(3), 236–252 (1998)

    Article  Google Scholar 

  9. Bordogna, G., Pasi, G., Lucarella, D.: A Fuzzy Object-Oriented Data Model for Managing Vague and Uncertain Information. International Journal of Intelligent Systems 14, 623–651 (1999)

    Article  Google Scholar 

  10. Ma, Z.M., Zhang, W.J., Ma, W.Y.: Extending object-oriented databases for fuzzy information modeling. Information Systems 29, 421–435 (2004)

    Article  Google Scholar 

  11. Yazici, A., George, R.: Fuzzy Database Modeling. Physica-Verlag, Heidelberg (1999)

    MATH  Google Scholar 

  12. Buckles, B.P., Petry, F.E.: A Fuzzy Representation of Data for Relational Database. Fuzzy Sets and Systems 7, 213–226 (1982)

    Article  MATH  Google Scholar 

  13. Prade, H., Testemale, C.: Generalizing Database Relational Algebra for the Treatment of Incomplete or Uncertain Information and Vague Queries. Information Science 34, 115–143 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  14. Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1, 3–28 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  15. Dullea, J., Song, I., Lamprou, I.: An Analysis of Structural Validity in Entity-Relationship Modeling. Data and Knowledge Engineering 47, 167–205 (2001)

    Article  Google Scholar 

  16. Jones, T., Song, I.: Analysis of Binary/Ternary Relationships Cardinality Combinations in Entity-Relationship Modeling. Data and Knowledge Engineering 19, 39–64 (1996)

    Article  MATH  Google Scholar 

  17. Raju, K.V.S.V.N., Majumdar, A.K.: Fuzzy Functional Dependencies and Lossless Join Decomposition of Fuzzy Relational Database Systems. ACM Trans. On Database Systems 13, 120–166 (1988)

    Article  Google Scholar 

  18. Shenoi, S., Melton, A., Fan, L.T.: Functional dependencies and normal forms in the fuzzy relational database model. Information Sciences 60, 1–28 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  19. Cubero, J.C., Vila, M.A.: A new definitions of fuzzy functional dependency in fuzzy relational databases. International Journal for Intelligent Systems 9, 441–448 (1994)

    Article  Google Scholar 

  20. Sozat, M.I., Yazici, A.: A Complete Approximation for Fuzzy Functional and Multi-valued Dependencies in Fuzzy Database Relations. Fuzzy Sets and Systems 117(2), 161–181 (2001)

    Article  MathSciNet  Google Scholar 

  21. Tyagi, B.K., Sharfuddin, A., Dutta, R.N., Devendra, K.T.: A complete axiomatization of fuzzy functional dependencies using fuzzy function. Fuzzy Sets and Systems 151, 363–379 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  22. Chen, G.: Fuzzy Logic in Data Modeling - semantics, constraints and database design. Kluwer Academic Publishers, Boston (1998)

    MATH  Google Scholar 

  23. Saiedian, H., Spencer, K.: An Efficient Algorithm to Compute the Candidate Key of a Relational Database Schema. The Computer Journal 39, 124–132 (1996)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Information Technology, Technical University of Łódź, Wólczańska 215, 93-005, Łódź, Poland

    Krzysztof Myszkorowski

Authors
  1. Krzysztof Myszkorowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź,, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics,, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA,  

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Myszkorowski, K. (2010). Fuzzy Functional Dependencies in Multiargument Relationships. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13208-7_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13207-0

  • Online ISBN: 978-3-642-13208-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation