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Flexible Bipolar Querying of Uncertain Data Using an Ontology

  • Patrice Buche
  • Sébastien Destercke
  • Valérie Guillard
  • Ollivier Haemmerlé
  • Rallou Thomopoulos
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 497)

Abstract

In this chapter, we propose an approach to query a database where the user preferences can be bipolar (i.e., express both constraints and wishes about the desired result) and the data stored in the database can be uncertain. Query results are then completely ordered with respect to these bipolar preferences, giving priority to constraints over wishes. Furthermore, we consider user preferences expressed on a domain of values which is not “flat”, but contains values that are more specific than others according to the “kind of” relation. These preferences are represented by specific fuzzy sets, called “Hierarchical Fuzzy Sets” and defined over a simple ontology. We propose a use of “Hierarchical Fuzzy Sets” for query enlargement purposes. The approach is illustrated on a real-world problem concerning the selection of optimal packaging material for fresh fruits and vegetables.

Notes

Acknowledgments

The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/ 2007-2013) under the grant agreement FP7-265669-EcoBioCAP project.

References

  1. 1.
    Benferhat, S., Dubois, D., Kaci, S., Prade, H.: Bipolar possibility theory in preference modelling: representation, fusion and optimal solutions. Inf. Fusion 7, 135–150 (2006)CrossRefGoogle Scholar
  2. 2.
    Bordogna, G., Pasi, G.: A fuzzy object oriented data model managing vague and uncertain information. Int. J. Intell. Syst. 14(6), SCI 3495 (1999)Google Scholar
  3. 3.
    Bosc, P., Lietard, L., Pivert, O.: Fuzzy theory techniques and applications in data-base management systems. In: Bosc, P., Kacprzyk, J. (eds.) Fuzziness in Database Management Systems, pp. 666–671. Academic Press, New York (1999)Google Scholar
  4. 4.
    Bosc, P., Lietard, L., Pivert, O.: Soft querying, a new feature for database management system. In: Proceedings DEXA’94 (Database and EXpert system Application), Lecture Notes in Computer Science, vol. 856, pp. 631–640. Springer-Verlag (1994)Google Scholar
  5. 5.
    Bosc, P., Pivert, O., Mokhtari, A., Lietard, L.: Extending relational algebra to handle bipolarity. In: Proceedings of the 2010 ACM Symposium on Applied Computing (SAC), pp. 1718–1722. Sierre, Switzerland, ACM, 22–26 March 2010Google Scholar
  6. 6.
    Bosc, P., Pivert, O.: About bipolar division operators. In: Flexible Query Answering Systems, 8th International Conference, FQAS 2009, Roskilde, Denmark, October 26–28, 2009. Proceedings. Lecture Notes in Computer Science, vol. 5822, pp. 572–582. Springer (2009)Google Scholar
  7. 7.
    Bosc, P., Pivert, O.: SQLf: a relational database language for fuzzy querying. IEEE Trans. Fuzzy Syst. 3(1), 1–17 (1995)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Bruno, N., Chaudhuri, S., Gravano, L.: Top-k selection queries over relational databases: Mapping strategies and performance evaluation. ACM Trans. Database Syst. 27(2), 153–187 (2002)CrossRefGoogle Scholar
  9. 9.
    Buche, P., Dibie-Barthelemy, J., Ibanescu, L., Soler, L.: Fuzzy web data tables integration guided by an ontological and terminological resource. IEEE Trans. Knowl. Data Eng. 24(4), 805–819 (2011)Google Scholar
  10. 10.
    Buche, P., Haemmerlé, O.: Towards a unified querying system of both structured and semi-structured imprecise data using fuzzy views. In: Proceedings of the 8th International Conference on Conceptual Structures, Lecture Notes in Artificial Intelligence, vol. 1867. pp. 207–220. Darmstadt, Germany, Springer-Verlag (August 2000)Google Scholar
  11. 11.
    Cacioppo, J.T., Gardner, W.L., Berntson, G.G.: Beyond bipolar conceptualizations and measures: the case of attitudes & evaluative space. Pers. Soc. Psychol. Rev. 1, 3–25 (1997)CrossRefGoogle Scholar
  12. 12.
    Charles, F., Sanchez, J., Gontard, N.: Modeling of active modified atmosphere packaging of endives exposed to several postharvest temperatures. J. Food Sci. 8, 443–448 (2005)CrossRefMATHGoogle Scholar
  13. 13.
    Chomicki, J.: Preference formulas in relational queries. ACM Trans. Database Syst. 28(4), 427–466 (2003)CrossRefGoogle Scholar
  14. 14.
    Destercke, S., Dubois, D., Chojnacki, E.: Unifying practical uncertainty representations: I generalized p-boxes. Int. J. Approximate Reasoning 49(3), 664–677 (2008)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Destercke, S., Dubois, D., Chojnacki, E.: Unifying practical uncertainty representations: II clouds. Int. J. Approximate Reasoning 49(3), 649–663 (2008)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Destercke, S., Buche, P., Guillard, V.: A flexible bipolar querying approach with imprecise data and guaranteed results. Fuzzy Sets Syst. 169(1), 51–64 (2011)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Destercke, S., Guillard, V.: Interval analysis on non-linear monotonic systems as an efficient tool to optimise fresh food packaging. Comput. Electron. Agric. 79(2), 116–124 (2011)CrossRefGoogle Scholar
  18. 18.
    Dubois, D., Prade, H.: Bipolarity in flexible querying. In: Flexible Query Answering Systems, 5th International Conference, FQAS 2002, Copenhagen, Denmark, October 27–29, 2002, Proceedings. Lecture Notes in Computer Science, vol. 2522, pp. 174–182. Springer (2002)Google Scholar
  19. 19.
    Dubois, D., Prade, H.: Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press, New York (1988)Google Scholar
  20. 20.
    Dubois, D., Prade, H.: Tolerant fuzzy pattern matching: an introduction. In: Bosc, P., Kacprzyk, J. (eds.) Fuzziness in Database Management Systems. Physica-Verlag (1995)Google Scholar
  21. 21.
    Dubois, D., Prade, H.: An overview of the asymmetric bipolar representation of positive and negative information in possibility theory. Fuzzy Sets Syst. 160, 1355–1366 (2009)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Kießling, W., Köstler, G.: Preference SQL—design, implementation, experiences. In: VLDB Proceedings of 28th International Conference on Very Large Data Bases. pp. 990–1001. Morgan Kaufmann, Hong Kong, China, 20–23 August 2002Google Scholar
  23. 23.
    Klement, E., Mesiar, R., Pap, E.: Triangular Norms. Kluwer Academic Publisher, Dordrecht (2000)Google Scholar
  24. 24.
    Labreuche, C.: A general framework for explaining the results of a multi-attribute preference model. Artif. Intell. 175(7–8), 1410–1448 (2011)MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Mahajan, P., Oliveira, F., Montanez, J., Frias, J.: Development of user-friendly software for design of modified atmosphere packaging for fresh and fresh-cut produce. Innovative Food Sci. Emerg. Technol. 8, 84–92 (2007)CrossRefGoogle Scholar
  26. 26.
    Prade, H.: Lipski’s approach to incomplete information data bases restated and generalized in the setting of Zadeh’s possibility theory. Inf. Syst. 9(1), 27–42 (1984)MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Prade, H., Testemale, C.: Generalizing database relational algebra for the treatment of incomplete or uncertain information and vague queries. Inf. Sci. 34, 115–143 (1984)MathSciNetCrossRefMATHGoogle Scholar
  28. 28.
    Thomopoulos, R., Buche, P., Haemmerlé, O.: Fuzzy sets defined on a hierarchical domain. IEEE Trans. Knowl. Data Eng. 18(10), 1397–1410 (2006)CrossRefGoogle Scholar
  29. 29.
    Tré, G.D., Caluwe, R.D.: A generalized object-oriented database model. In: Bordogna, G. Pasi, G. (eds.) Recent Research Issues on the Management of Fuzziness in Databases. Studies in Fuzziness and Soft computing, vol. 53, pp. 155–182. Physica-Verlag, Heidelberg, Germany (2000)Google Scholar
  30. 30.
    Tré, G.D., Zadrozny, S., Matthé, T., Kacprzyk, J., Bronselaer, A.: Dealing with positive and negative query criteria in fuzzy database querying. In: Flexible Query Answering Systems, 8th International Conference, FQAS 2009, Proceedings. Lecture Notes in Computer Science, vol. 5822, pp. 593–604. Roskilde, Denmark, Springer, 26–28 October 2009Google Scholar
  31. 31.
    Tré, G.D., Zadrozny, S., Bronselaer, A.: Handling bipolarity in elementary queries to possibilistic databases. IEEE Trans. Fuzzy Syst. 18, 599–612 (2010)CrossRefGoogle Scholar
  32. 32.
    Yager, R.: On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans. Syst. Man Cybern. 18, 183–190 (1988)Google Scholar
  33. 33.
    Zadeh, L.: The concept of a linguistic variable and its application to approximate reasoning-i. Inf. Sci. 8, 199–249 (1975)MathSciNetCrossRefMATHGoogle Scholar
  34. 34.
    Zadeh, L.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Patrice Buche
    • 1
    • 2
  • Sébastien Destercke
    • 3
  • Valérie Guillard
    • 4
  • Ollivier Haemmerlé
    • 5
  • Rallou Thomopoulos
    • 1
    • 6
  1. 1.INRA IATE Montpellier Cedex 02France
  2. 2.LIRMM/CNRS-UM2/INRIA GRAPHIKMontpellierFrance
  3. 3.CNRS HEUDYASICCentre de recherches de RoyallieuCompiegne CedexFrance
  4. 4.UM2 IATEMontpellierFrance
  5. 5.IRIT-MelodiUniversité Toulouse le MirailToulouse Cedex 9France
  6. 6.LIRMM/CNRS-UM2/INRIA GRAPHIKMontpellierFrance

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