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Some approaches for relational databases flexible querying

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Abstract

One of the main objectives of third generation databases is to design database management systems which provide users with more and more functionalities. In such a wide context, various proposals have been made in order to introduce some kind of explicit or implicit flexibility into user queries. In this paper, we propose a classification of the various approaches dealing with imprecise queries. Moreover, we show that the approach based on fuzzy sets is powerful enough to answer a wide range of imprecise queries in an appropriate way and to support the expression of the capabilities available in the other classes of solutions. An outline of an SQL-like language allowing for a variety of imprecise queries is also presented.

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References

  • Bosc, P., Galibourg, M., and Hamon, G. (1988). Fuzzy Querying with SQL: Extensions and Implementation Aspects.Fuzzy Sets and Systems, 28, 333–349.

    Google Scholar 

  • Bosc, P. and Pivert, O. (1991a). Some Algorithms for Evaluating Fuzzy Relational Queries.Lecture Notes in Computer Science, vol. 521, pp. 431–442.

    Google Scholar 

  • Bosc, P. and Pivert, O. (1991b). On the evaluation of simple fuzzy relational queries.Proc. 4th IFSA World Congress, (pp. 9–12), Brussels, Belgium.

  • Bosc, P. and Pivert, O. (1991c). About equivalences in SQLf, a relational language supporting imprecise querying.Proc. Int. Fuzzy Engineering Symp. (309–320), Yokohama, Japan.

  • Bosc, P. and Pivert, O. (1992). Fuzzy Querying in Conventional Databases. In J. Kacprzyk and L. Zadeh (Eds.),Fuzzy Logic for the Management of Uncertainty, New York: Wiley.

    Google Scholar 

  • Bouchon-Meunier, B. and Yao, J. (1992). Linguistic Modifiers and Imprecise Categories.Journal of Intelligent Systems, to appear.

  • Chamberlin, D.D., et al. (1976). SEQUEL2: A Unified Approach to Data Definition, Manipulation and Control.IBM Journal of Research and Development, 20, 560–575.

    Google Scholar 

  • Chang, C.L. (1982). Decision Support in an Imperfect World. Research Report RJ3421, IBM San José, CA.

    Google Scholar 

  • D'Atri, A. and Tarantino, L. (1989). From Browsing to Querying.Data Engineering Bulletin, 12, 47–53.

    Google Scholar 

  • Dubois, D. and Prade, H. (1985). A Review of Fuzzy Set Aggregation Connectives.Information Sciences, 36, 85–121.

    Google Scholar 

  • Dubois, D. and Prade, H. (1986). Weighted Minimum and Maximum Operations in Fuzzy Set Theory.Information Sciences, 39, 205–210.

    Google Scholar 

  • Dyckhoff, H. and Pedrycz, W. (1984). Generalized Means as a Model of Compensative Connectives.Fuzzy Sets and Systems, 14, 143–154.

    Google Scholar 

  • Friedman, J.H., Baskett, F., and Shustek, L.J. (1975). An Algorithm for Finding Nearest Neighbors.IEEE Transactions on Computers, 1001–1006.

  • Gal, A. (1988). Cooperative Responses in Deductive Databases. Technical Report CS-TR-2075, Department of Computer Science, University of Maryland, MD.

    Google Scholar 

  • Guyomard, M. and Siroux, J. (1989). Suggestive and Corrective Answers: A Single Mechanism. In M.M. Taylor, F. Néel and D.G. Bouwhuis (Eds.),The Structure of Multimodal Dialogue, pp. 361–374. Amsterdam: North-Holland.

    Google Scholar 

  • Ichikawa, T. and Hirakawa, M. (1986). ARES: A Relational Database with the Capability of Performing Flexible Interpretion of Queries.IEEE Transactions on Software Engineering, 12, 624–634.

    Google Scholar 

  • Janas, J.M. (1981). On the Feasibility of Informative Answers. In H. Gallaire, J. Minker, and J-M. Nicolas (Eds.),Advances in Database Theory, New York: Plenum Press.

    Google Scholar 

  • Kacprzyk, J. and Ziolkowski, A. (1986). Database Queries with Fuzzy Linguistic Quantifiers.IEEE Transactions on Systems, Man and Cybernetics, 16, 474–478.

    Google Scholar 

  • Kaplan, J. (1982). Cooperative Responses from a Portable Natural Language Database Query System. In M. Brady (Ed.),Computational Models of Discourse, Cambridge, MA: MIT Press.

    Google Scholar 

  • Kunii, T.L. (1976). Dataplan: An Interface Generator for Database Semantics.Information Sciences, 10, 279–298.

    Google Scholar 

  • Lacroix, M. and Lavency P. (1987). Preferences: putting more knowledge into queries.Proc. 13rd VLDB Conf. (pp. 217–225), Brighton, Great Britain.

  • Lakoff, G. (1973). Hedges: A Study in Meaning Criteria and the Logic of Fuzzy Concepts.Journal of Philosophical Logic, 2, 458–508.

    Google Scholar 

  • Motro, A. (1986). BAROQUE: A Browser for Relational Databases.ACM Transactions on Office Information Systems, 4, 164–181.

    Google Scholar 

  • Motro, A. (1988). VAGUE: A User Interface to Relational Databases That Permits Vague Queries.ACM Transactions on Office Information Systems, 6, 187–214.

    Google Scholar 

  • Motro, A. (1989). A Trio of Database User Interfaces for Handling Vague Retrieval Requests.Data Engineering Bulletin, 12, 54–63.

    Google Scholar 

  • Rivest, R.L. (1976). Partial Match Retrieval Algorithms.SIAM Journal of Computing, 5, 19–50.

    Google Scholar 

  • Tahani, V. (1977). A Conceptual Framework for Fuzzy Query Processing; A Step toward Very Intelligent Database Systems.Information Processing and Management, 13, 289–303.

    Google Scholar 

  • Yager, R.R. (1988). On Ordered Weighted Averaging Aggregation Operators in Multicriteria Decisionmaking.IEEE Transactions on Systems, Man and Cybernetics, 18, 183–190.

    Google Scholar 

  • Yager, R.R. (1991a). Connectives and Quantifiers in Fuzzy Sets.Fuzzy Sets and Systems, 40, 39–75.

    Google Scholar 

  • Yager, R.R. (1991b). Fuzzy Quotient Operators for fuzzy relational databases.Proc. Int. Fuzzy Engineering Symp., (pp. 289–296), Yokohama, Japan.

  • Yager, R.R. (1991c). Non-Monotonic Set Theoretic Operations.Fuzzy Sets and Systems, 42, 173–190.

    Google Scholar 

  • Yong-Yi, C. (1981). An Approach to Fuzzy Operators.BUSEFAL, 9, 59–65.

    Google Scholar 

  • Zadeh, L.A. (1965). Fuzzy Sets.Information and Control, 8, 338–353.

    Google Scholar 

  • Zadeh, L.A. (1972). A Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges.Journal of Cybernetics, 2, 4–34.

    Google Scholar 

  • Zadeh, L.A. (1983). A Computational Approach to Fuzzy Quantifiers in Natural Languages.Computer Mathematics with Applications, 9, 149–183.

    Google Scholar 

  • Zimmermann, H.J. and Zysno, P. (1980). Latent Connectives in Human Decision Making.Fuzzy Sets and Systems, 4, 37–51.

    Google Scholar 

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Authors and Affiliations

  1. ENSSAT/IRISA, BP 447, 22305, Lannion Cedex, France

    Patrick Bosc & Olivier Pivert

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  1. Patrick Bosc
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  2. Olivier Pivert
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Bosc, P., Pivert, O. Some approaches for relational databases flexible querying. J Intell Inf Syst 1, 323–354 (1992). https://doi.org/10.1007/BF00962923

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