Efficient Detection of Minimal Failing Subqueries in a Fuzzy Querying Context

  • Olivier Pivert
  • Grégory Smits
  • Allel Hadjali
  • Hélène Jaudoin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6909)


This paper deals with conjunctive fuzzy queries that yield an empty or unsatisfactory answer set. We propose a cooperative answering approach which efficiently retrieves the minimal failing subqueries of the initial query (which can then be used to explain the failure). The detection of the minimal failing subqueries relies on a prior step of fuzzy cardinalities computation. The main advantage of this strategy is to imply a single scan of the database. Moreover, the storage of such knowledge about the data distributions easily fits in memory.


Fuzzy Relation Satisfaction Degree Conjunctive Query Initial Query Predicate Price 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    McSherry, D.: Retrieval failure and recovery in recommender systems. Artif. Intell. Rev. 24(3-4) (2005)Google Scholar
  2. 2.
    Zadeh, L.A.: Fuzzy sets. Information and control 8(3), 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Dubois, D., Prade, H.: Fundamentals of fuzzy sets. The Handbooks of Fuzzy Sets, vol. 7. Kluwer Academic Pub., Netherlands (2000)zbMATHGoogle Scholar
  4. 4.
    Bosc, P., Pivert, O.: SQLf: a relational database language for fuzzy querying. IEEE Transactions on Fuzzy Systems 3(1), 1–17 (1995)CrossRefGoogle Scholar
  5. 5.
    Bosc, P., Buckles, B., Petry, F., Pivert, O.: Fuzzy databases. In: Bezdek, J., Dubois, D., Prade, H. (eds.) Fuzzy Sets in Approximate Reasoning and Information Systems. The Handbook of Fuzzy Sets Series, pp. 403–468. Kluwer Academic Publishers, Dordrecht (1999)CrossRefGoogle Scholar
  6. 6.
    Godfrey, P.: Minimization in cooperative response to failing database queries. Int. J. Cooperative Inf. Syst. 6(2), 95–149 (1997)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Jannach, D.: Techniques for fast query relaxation in content-based recommender systems. In: Freksa, C., Kohlhase, M., Schill, K. (eds.) KI 2006. LNCS (LNAI), vol. 4314, pp. 49–63. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    McSherry, D.: Incremental relaxation of unsuccessful queries. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 331–345. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Bosc, P., Hadjali, A., Pivert, O.: Incremental controlled relaxation of failing flexible queries. Journal of Intelligent Information Systems 33(3), 261–283 (2009)CrossRefGoogle Scholar
  10. 10.
    Zadeh, L.: A computational approach to fuzzy quantifiers in natural languages. Computing and Mathematics with Applications 9, 149–183 (1983)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Olivier Pivert
    • 1
  • Grégory Smits
    • 2
  • Allel Hadjali
    • 1
  • Hélène Jaudoin
    • 1
  1. 1.Technopole AnticipaIrisa – Enssat, University of Rennes 1Lannion CedexFrance
  2. 2.Irisa – IUT LannionUniversity of Rennes 1Lannion CedexFrance

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