Advertisement

Abstract

The fuzzy queries represent a solution to enhance the bipolar behavior of the classical querying in relational databases (RDB) in order to deal with the empty answer problem. This problem is defined by the execution of queries that do not return any answer. So, the fuzzy queries provide the user with some alternative data when there is no response satisfies his or her query. The aim of this paper is to present a solution to the empty answer problem based on cooperative answers and the proximity measure for improving fuzzy querying in RDB. The proximity measure is based on the use of the Hausdorff distance as a similarity measure between a failing query (query with the empty answer) and some other successful ones whose answers are not empty. Our idea is to assign the closest query’s answers to the one that failed as cooperative answers. We propose four gradual operators based on usual ones between predicates for enhancing fuzzy queries. The experimental results show that our approach is a promising way for improving fuzzy queries in relational databases.

Keywords

Fuzzy queries Cooperative answers Empty answer problem Proximity measure Gradual operators 

References

  1. 1.
    Pivert, O., Jaudoin, H., Brando, C., Hadjali, A.: A method based on query caching and predicate substitution for the treatment of failing database queries. In: Bichindaritz, I., Montani, S. (eds.) Case-Based Reasoning. Research and Development. LNCS, vol. 6176, pp. 436–450. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-14274-1_32CrossRefGoogle Scholar
  2. 2.
    Tahani, V.: A conceptual framework for fuzzy query processing—a step toward very intelligent database systems. Inf. Process. Manage. 13(5), 289–303 (1977).  https://doi.org/10.1016/0306-4573(77)90018-8CrossRefzbMATHGoogle Scholar
  3. 3.
    Marín, N.: Intelligent Fuzzy Information Systems: Beyond the Relational Data Model. World Scientific (2007)Google Scholar
  4. 4.
    Bowman, D., Ortega, R.E., Linden, G., Spiegel, J.R.: Identifying the items most relevant to a current query based on items selected in connection with similar queries. Google Patents (2001)Google Scholar
  5. 5.
    Muiño, D.P.: Measuring and repairing inconsistency in knowledge bases with graded truth. Fuzzy Sets Syst. 197, 108–122 (2012).  https://doi.org/10.1016/j.fss.2011.10.01MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Blanco, I.J., Martin-Bautista, M.J., Pons, O., Vila, M.A.: A tuple-oriented algorithm for deduction in a fuzzy relational database. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 11(1), 47–66 (2003).  https://doi.org/10.1142/s0218488503002260MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Tamani, N., Liétard, L., Rocacher, D.: Bipolar SQLf: a flexible querying language for relational databases. In: Christiansen, H., De Tré, G., Yazici, A., Zadrozny, S., Andreasen, T., Larsen, H.L. (eds.) Flexible Query Answering Systems. FQAS 2011. LNCS, vol. 7022, pp. 472–484. Springer, Berlin, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-24764-4_41CrossRefGoogle Scholar
  8. 8.
    Kacprzyk, J., Zadrożny, S., Ziołkowski, A.: FQUERY III+: a “human-consistent” database querying system based on fuzzy logic with linguistic quantifiers. Inf. Syst. 14(6), 443–453 (1989).  https://doi.org/10.1016/0306-4379(89)90012-4CrossRefGoogle Scholar
  9. 9.
    Fredj, I.B., Ouni, K.: Fuzzy k-nearest neighbors applied to phoneme recognition. In: 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, pp. 422–426. Tunisia (2016).  https://doi.org/10.1109/setit.2016.7939907
  10. 10.
    Nevzorova, O., Mukhamedshin, D., Galieva, A., Gataullin, R., Nevzorova, O., Gataullin, R.: Corpus management system: semantic aspects of representation and processing of search queries. In: 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, pp. 285–290. Tunisia (2016).  https://doi.org/10.1109/setit.2016.7939881
  11. 11.
    Toujani, R., Akaichi, J.: Fuzzy sentiment classification in social network Facebook’ statuses mining. In: 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, pp. 393–397. Tunisia (2016).  https://doi.org/10.1109/setit.2016.7939902
  12. 12.
    Liao, H., Xu, Z., Zeng, X.-J.: Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Inf. Sci. 271, 125–142 (2014).  https://doi.org/10.1016/j.ins.2014.02.125MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Bhatia, S., Majumdar, D., Mitra, P.: Query suggestions in the absence of query logs. In: 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 795–804. ACM, (2011).  https://doi.org/10.1145/2009916.2010023
  14. 14.
    Bosc, P., HadjAli, A., Pivert, O.: Weakening of fuzzy relational queries: an absolute proximity relation-based approach. Mathw. Soft Comput. 14(1), 35–55 (2007)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Caha, J., Dvorský, J.: Querying on fuzzy surfaces with vague queries. In: Pan, J.-S., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds.) Hybrid Artificial Intelligent Systems. HAIS 2013. LNCS, vol. 8073, pp. 548–557. Springer Berlin Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40846-5_55Google Scholar
  16. 16.
    Ioannidis, Y.E., Poosala, V.: Histogram-based approximation of set-valued query-answers. In: 25th International Conference on Very Large Data Bases, pp. 174–185. USA, (1999)Google Scholar
  17. 17.
    Cormode, G., Garofalakis, M.: Sketching streams through the net: distributed approximate query tracking. In: 31st International Conference on Very Large Data Bases, pp. 13–24. ACM, Norway (2005)Google Scholar
  18. 18.
    Nitsche, M., Nürnberger, A.: Vague query formulation by design. In: EuroHCIR, pp. 83–86. (2012)Google Scholar
  19. 19.
    Perera, K.S., Hahmann, M., Lehner, W., Pedersen, T.B., Thomsen, C.: Modeling large time series for efficient approximate query processing. In: Liu, A., Ishikawa, Y., Qian, T., Nutanong, S., Cheema, M.A. (eds.) Database Systems for Advanced Applications DASFAA 2015. LNCS, vol. 9052, pp. 190–204. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-22324-7_16CrossRefGoogle Scholar
  20. 20.
    Smits, G., Pivert, O., Hadjali, A.: Fuzzy cardinalities as a basis to cooperative answering. In: Pivert, O., Zadrożny, S. (eds.) Flexible Approaches in Data, Information and Knowledge Management. Studies in Computational Intelligence. LNCS, vol. 497, pp. 261–289. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-00954-4_12Google Scholar
  21. 21.
    Aggoune, A., Bouramoul, A., Kholladi, M.K.: Approximate flexible queries using Hausdorff distance. In: 2nd International Symposium on Modelling and Implementation of Complex Systems. Constantine, Algeria (2012)Google Scholar
  22. 22.
    Yager, R.R., Filev, D.P.: Approximate clustering via the mountain method. IEEE Trans. Syst. Man Cybern. 24(8), 1279–1284 (1994).  https://doi.org/10.1109/21.299710CrossRefGoogle Scholar
  23. 23.
    Chu, S.-C., Roddick, J.F., Pan, J.-S.: An incremental multi-centroid, multi-run sampling scheme for k-medoids-based algorithms. WIT Transactions on Information and Communication Technologies 28 (2002)Google Scholar
  24. 24.
    Sujatha, K., Keerthana, P., Priya, S.S., Kaavya, E., Vinod, B.: Fuzzy based multiple dictionary bag of words for image classification. Procedia Eng. 38, 2196–2206 (2012).  https://doi.org/10.1016/j.proeng.2012.06.264CrossRefGoogle Scholar
  25. 25.
    Kowalczyk-Niewiadomy, A., Pelikant, A.: Processing imprecise database queries by fuzzy clustering algorithms. In: Position Papers of the 2015 Federated Conference on Computer Science and Information Systems, pp. 31–38 (2015).  https://doi.org/10.15439/2015f1
  26. 26.
    Bosc, P., Pivert, O.: On four noncommutative fuzzy connectives and their axiomatization. Fuzzy Sets Syst. 202, 42–60 (2012).  https://doi.org/10.1016/j.fss.2011.11.005MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Benferhat, S., Grant, J.: Scalable Uncertainty Management, vol. 6929. Springer-Verlag Berlin Heidelberg. LNAI, USA (2011).  https://doi.org/10.1007/978-3-642-23963-2zbMATHGoogle Scholar
  28. 28.
    Chaudhuri, B.B., Rosenfeld, A.: A modified Hausdorff distance between fuzzy sets. Inf. Sci. 118(1–4), 159–171 (1999).  https://doi.org/10.1016/S0020-0255(99)00037-7MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Fell, J.M.: A Hausdorff topology for the closed subsets of a locally compact non-Hausdorff space. Proc. Am. Math. Soc. 13(3), 472–476 (1962).  https://doi.org/10.2307/2034964MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Aggoune, A., Bouramoul, A., Kholladi, M.K.: A New semantic proximity measure for fuzzy query optimization in relational databases. In: 1st International Conference on Pattern Analysis and Intelligent Systems (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science, LabSTIC LaboratoryUniversity of 8th May 1945GuelmaAlgeria

Personalised recommendations