Skip to main content

Using Fuzzy Sets in Flexible Querying: Why and How?

  • Chapter
Flexible Query Answering Systems

Abstract

The last few years have witnessed a tremendous increase in the use of computers in more and more domains, the need for managing new kinds of data and for providing new capabilities for storage, access and display of information. In this respect, one may imagine introducing what is often dubbed “uncertainty” ’into databases. This term may refer to two main streams of problems. On the one hand, one wants to store and manipulate incomplete data (i.e., the available information about attribute values may be tainted with imprecision and/or uncertainty for some items) . In that case, the retrieval process will also return results involving some uncertainty (if we are uncertain about the precise value of John’s age, we cannot always be sure that John does (or does not) satisfy a given requirement in the context of a query selecting people on basis of their age). On the other hand, the term “uncertainty” is sometimes (and somewhat misleadingly) used for referring to flexible queries, since one may then consider that there is some ambiguity pertaining to their meaning. In fact, flexible queries are useful for describing preferences and thus for getting an ordered set of answers accordingly.

This is a revised version of the main part of a paper entitled “Using fuzzy sets in database systems: Why and how?” in the Proceedings of the 1996 Workshop on Flexible Query-Answering Systems (FQAS’96) (H. Christiansen, H.L. Larsen, T. Andreasen, eds.), held in Roskilde, Denmark, May 22–24, 1996, pp. 89–103. 45

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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.

Similar content being viewed by others

References

  1. Andrès, V. Filtrage sémantique dans une base de données imprécises et incertaines: Un système souple autorisant la formulation de requêtes composites pondérées. Dissertation, Université P. Sabatier, Toulouse, France.

    Google Scholar 

  2. [Bosc & al, 1996]_Bosc P., Dubois D., Prade H. Fuzzy functional dependencies and redundancy elimination. In:Tech. Report IRIT/96-10-R, IRIT, Univ. P. Sabatier, Toulouse, France, 1996. To appear in J. Amer. Soc. Infor. Syst.

    Google Scholar 

  3. Bosc P., Kacprzyk J. Fuzziness in Database Management Systems, 1995 Physica-Verlag, Heidelberg.

    Google Scholar 

  4. Bosc P., Pivert O. Some approaches for relational databases flexible querying. J. of Intelligent Information Systems, 1, 323–354, 1992.

    Article  Google Scholar 

  5. Bosc P., Pivert O. An approach for a hierarchical aggregation of fuzzy predicates. Proc. 2nd IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE’93), San Francisco, 1231–1236, 1993.

    Google Scholar 

  6. Bosc P., Pivert O. SQLf: A relational database language for fuzzy querying. IEEE Trans. on Fuzzy Systems, 3(1), 1–17, 1995.

    Article  MathSciNet  Google Scholar 

  7. Bosc P., Prade H. An introduction to the fuzzy set and possibility theory-based treatment of soft queries and uncertain or imprecise databases: Uncertainty Management in Information Systems: From Needs to Solutions, (A. Motro, P. Smets, eds.), Kluwer Acad. Pub, 285–324.

    Google Scholar 

  8. Bosc P., Dubois D, Pivert O., Prade H. Flexible queries in relational databases — The example of the devision operator —, Theoretical Computer Science, 171, 1997, 281–302.

    Article  MathSciNet  MATH  Google Scholar 

  9. Buckles B.P., Petry F.E. A fuzzy representation of data for relational databases. Fuzzy Sets and Systems, 5, 213–226, 1982.

    Article  Google Scholar 

  10. Cayrol M., Farreny H., Prade H. Fuzzy pattern matching. Kybernetes, 11, 103–116, 1982.

    Article  Google Scholar 

  11. Chen G.Q., Kerre E.E., Vandenbulcke J. A computational algorithm for the FFD transitive closure and a complete axiomatization of fuzzy functional dependencies. J. of Intelligent Systems, 9(5), 421–440, 1994.

    Article  Google Scholar 

  12. Cubero J.C., Vila M.A. A new definition of fuzzy functional dependency in fuzzy relational databases. J. of Intelligent Systems, 9(5), 441–448, 1994.

    Article  Google Scholar 

  13. Dubois D., Prade H. Possibility TheoryAn Approach to Computerized Processing of Uncertainty, 1988. Plenum Press, New York.

    Google Scholar 

  14. Dubois D., Prade H. Measuring properties of fuzzy sets: A general technique and its use in fuzzy query evaluation. Fuzzy Sets and Systems, 38, 137–152, 1990.

    Article  MathSciNet  MATH  Google Scholar 

  15. Dubois D., Prade H. Semantics of quotient operators in fuzzy relational databases. Fuzzy Sets and Systems, 78, 89–93, 1996.

    Article  MathSciNet  Google Scholar 

  16. Dubois D., Prade H. Testemale C., Weighted fuzzy pattern matching. Fuzzy Sets and Systems, 28, 313–331, 1988.

    Article  MathSciNet  MATH  Google Scholar 

  17. Kacprzyk J., Ziolkowski A. Data base queries with fuzzy linguistic quantifiers. IEEE Trans. on Systems, Man and Cybernetics, 16(3), 474–478.

    Google Scholar 

  18. Lacroix M., Lavency P. Preferences: Putting more knowledge into queries. Proc. of the 13rd Inter. Conf. on Very Large Data Bases, Brighton, UK, 217–225, 1987.

    Google Scholar 

  19. Petry F.E. Fuzzy Databases: Principles and Applications. Kluwer Acad. Pub., Dord. 1996

    Google Scholar 

  20. Prade H., Testemale C. Generalizing database relational algebra for the treatment of incomplete/uncertain information and vague queries. Information Sciences, 34, 115–143, 1984.

    Article  MathSciNet  MATH  Google Scholar 

  21. 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(2), 129–166, 1998.

    Article  Google Scholar 

  22. Sanchez E. Fuzzy logic and neural networks in Artificial Intelligence and Pattern Recognition. SPIE, vol 1569, 1569, Stochastic and Neural Methods in Signal Processing, Image Processing and Computer Vision, 474–483, 1991.

    Google Scholar 

  23. Tahani V. A conceptual framework for fuzzy query processing. A step toward very intelligent database systems. Information Processing Management, 13, 289–303, 1977.

    Article  MATH  Google Scholar 

  24. Umano M. FREEDOM-0: A fuzzy database system. In: Fuzzy Information and Decision Processes (M.M. Gupta, E. Sanchez, eds.), North-Holland, 339–347, 1982.

    Google Scholar 

  25. Vandenberghe R., Van Schooten A., De Caluwe R., Kerre E.E. Some practical aspects of fuzzy database techniques: An example, Information Systems, 14, 465–472, 1989.

    Article  Google Scholar 

  26. Wu X.D., Mahlen P. Fuzzy interpretation of induction results. Proc. of the Inter. Conf. on Knowledge Discovery & Data Mining (U.M. Fayyad, R. Uthurusamy, eds.), Montréal, Canada, Aug. 20–21, 325–330, 1995.

    Google Scholar 

  27. Yager R.P. General multiple objective decision making and linguistically quantified statements. Int. J. of Man-Machine Studies, 21, 389–400, 1984.

    Article  MATH  Google Scholar 

  28. Yager R.P. On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans, on Systems, Man and Cybernetics, 18, 183–190, 1988.

    Article  MathSciNet  MATH  Google Scholar 

  29. Yager R.P. Fuzzy quotient operators for fuzzy relational data bases. In: Fuzzy Engineering toward Human Friendly Systems, Vol. 1 (Proc. Inter. Fuzzy Engineering Symp. (IFES’91), Yokohama, Japan, Nov. 13–15, 1991) (T. Terano, M. Sugeno, M. Mukaidono, K. Shigemasu, eds.), Available from IOS Press, Amsterdam, 289–296.

    Google Scholar 

  30. Yager R.P. Database discovery using fuzzy sets. Tech. Report #MII-1601, Machine Intelligence Institute, lona College, New Rohelle, NY. 1996.

    Google Scholar 

  31. Zadeh L. A. Fuzzy sets. Information and Control, 8, 338–353, 1965.

    Article  MathSciNet  MATH  Google Scholar 

  32. Zemankova M., Kandel A. Fuzzy Relational Databases A Key to Expert Systems. Interdisciplinary Systems Research, Verlag TV, Rheinland, 1984.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer Science+Business Media New York

About this chapter

Cite this chapter

Dubois, D., Prade, H. (1997). Using Fuzzy Sets in Flexible Querying: Why and How?. In: Andreasen, T., Christiansen, H., Larsen, H.L. (eds) Flexible Query Answering Systems. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6075-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-6075-3_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7783-2

  • Online ISBN: 978-1-4615-6075-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics