Enhancing Flexible Querying Using Criterion Trees

  • Guy De Tré
  • Jozo Dujmović
  • Joachim Nielandt
  • Antoon Bronselaer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8132)


Traditional query languages like SQL and OQL use a so-called WHERE clause to extract only those database records that fulfil a specified condition. Conditions can be simple or be composed of conditions that are connected through logical operators. Flexible querying approaches, among others, generalized this concept by allowing more flexible user preferences as well in the specification of the simple conditions (through the use of fuzzy sets), as in the specification of the logical aggregation (through the use of weights). In this paper, we study and propose a new technique to further enhance the use of weights by working with so-called criterion trees. Next to better facilities for specifying flexible queries, criterion trees also allow for a more general aggregation approach. In the paper we illustrate and discuss how LSP basic aggregation operators can be used in criterion trees.


Fuzzy querying criterion trees LSP GCD 


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  1. 1.
    Bosc, P., Lietard, L., Pivert, O.: Sugeno fuzzy integral as a basis for the interpretation of flexible queries involving monotonic aggregates. Information Processing and Management 39(2), 287–306 (2003)zbMATHCrossRefGoogle Scholar
  2. 2.
    Cattell, R.G.G., Barry, D.K. (eds.): The Object Data Standard: ODMG 3.0. Morgan Kaufmann, San Francisco (2000)Google Scholar
  3. 3.
    Codd, E.F.: A Relational Model of Data for Large Shared Data Banks. Communications of the ACM 13(6), 377–387 (1970)zbMATHCrossRefGoogle Scholar
  4. 4.
    Dubois, D., Prade, H.: Using fuzzy sets in flexible querying: why and how? In: Andreasen, T., Christiansen, H., Larsen, H.L. (eds.) Flexible Query Answering Systems. Kluwer Academic Publishers, Dordrecht (1997)Google Scholar
  5. 5.
    Dujmović, J.J.: Preference Logic for System Evaluation. IEEE Transactions on Fuzzy Systems 15(6), 1082–1099 (2007)CrossRefGoogle Scholar
  6. 6.
    Dujmović, J.J., Larsen, H.L.: Generalized conjunction/disjunction. Int. Journal of Approximate Reasoning 46, 423–446 (2007)zbMATHCrossRefGoogle Scholar
  7. 7.
    Dujmović, J.J.: Characteristic Forms of Generalized Conjunction/Disjunction. In: Proc. IEEE World Congress on Computational Intelligence, Hong Kong (2008)Google Scholar
  8. 8.
    Dujmović, J.J., De Tré, G.: Multicriteria Methods and Logic Aggregation in Suitability Maps. Int. Journal of Intelligent Systems 26(10), 971–1001 (2011)CrossRefGoogle Scholar
  9. 9.
    Galindo, J., Medina, J.M., Cubero, J.C., Garcia, M.T.: Relaxing the Universal Quantifier of the Division in Fuzzy Relational Databases. Int. Journal of Intelligent Systems 16(6), 713–742 (2001)zbMATHCrossRefGoogle Scholar
  10. 10.
    ISO/IEC 9075-1:2011: Information technology – Database languages – SQL – Part 1: Framework (SQL/Framework) (2011)Google Scholar
  11. 11.
    Kacprzyk, J.: Ziółkowski, A.: Database queries with fuzzy linguistic quantifiers. IEEE Transactions on Systems, Man and Cybernetics 16, 474–479 (1986)CrossRefGoogle Scholar
  12. 12.
    Klement, E.P., Mesiar, R., Pap, E. (eds.): Triangular Norms. Kluwer Academic Publishers, Boston (2000)zbMATHGoogle Scholar
  13. 13.
    Larsen, H.L.: Efficient Andness-directed Importance Weighted Averaging Operators. Int. Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12(suppl.), 67–82 (2003)CrossRefGoogle Scholar
  14. 14.
    Larsen, H.L.: Importance weighting and andness control in De Morgan dual power means and OWA operators. Fuzzy Sets and Systems 196(1), 17–32 (2012)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Wirth, N.: What Can We Do About the Unnecessary Diversity of Notation for Syntactic Definitions. Communications of the ACM 20(11), 822–823 (1977)CrossRefGoogle Scholar
  16. 16.
    Yager, R.R., Kacprzyk, J.: The Ordered Weighted Averaging Operators: Theory and Applications. Kluwer Academic Publishers, Norwell (1997)CrossRefGoogle Scholar
  17. 17.
    Zadeh, L.A.: A computational approach to fuzzy quantifiers in natural languages. Computational Mathematics Applications 9, 149–184 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Zadrozny, S., De Tré, G., De Caluwe, R., Kacprzyk, J.: An Overview of Fuzzy Approaches to Flexible Database Querying. In: Galindo, J. (ed.) Handbook of Research on Fuzzy Information Processing in Databases, pp. 34–54. IGI Global, Hershey (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guy De Tré
    • 1
  • Jozo Dujmović
    • 2
  • Joachim Nielandt
    • 1
  • Antoon Bronselaer
    • 1
  1. 1.Dept. of Telecommunications and Information ProcessingGhent UniversityGhentBelgium
  2. 2.Dept. of Computer ScienceSan Francisco State UniversitySan FranciscoU.S.A.

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