Advertisement

Introduction to Concept Modifiers in Fuzzy Soft Sets for Efficient Query Processing

  • Chandrasekhar Uddagiri
  • Neelu KhareEmail author
Conference paper
  • 127 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1054)

Abstract

This paper presents hedges also known as concept modifiers on fuzzy soft sets. Hedges allow close ties to natural language and also allow query processing on the latest data repositories like data warehouses, big data and cloud data. Data repositories may require complex linguistic queries and at the same time efficient query processing also, while the requirements are vague and uncertain in natural languages. SQL is not able to handle such complex queries. This requires an additional application layer for computing hedges and defuzzification process. Hence, the presented framework scores a lot in terms of efficiency as well as the ability to handle linguistic queries along with aggregate operators.

Keywords

Fuzzy soft sets Hedges Linguistic variable Concept modifiers SQL 

References

  1. 1.
    Ibrahim, A.: Enhanced fuzzy system for student’s academic evaluation using linguistic hedges. IEEE (2017). 978-1-5090-6034-4/17/$31.00Google Scholar
  2. 2.
    Le, V.H., Tran, D.K.: Extending fuzzy logics with many hedges. Fuzzy Sets Syst.  https://doi.org/10.1016/j.fss.2018.01.014,  https://doi.org/10.1016/j.fss.2018.01.0140165-0114/ ©2018 Elsevier
  3. 3.
    Zadeh, L.A.: A fuzzy-set-theoretic interpretation of linguistic hedges. J. Cybern. 2 (1972)Google Scholar
  4. 4.
    Zadeh, L.A.: A theory of approximate reasoning. In: Yager, R.R., Ovchinnikov, S., Tong, R.M., Nguyen, H.T. (eds.) Fuzzy Sets and Applications: Selected Papers by L.A. Zadeh, pp. 367–411. Wiley, New York (1987)Google Scholar
  5. 5.
    Zadeh, L.A.: The concept of linguistic variable and its application to approximate reasoning (I). Inf. Sci. 8, 199–249 (1975)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Zadeh, L.A.: The concept of linguistic variable and its application to approximate reasoning (II). Inf. Sci. 8, 310–357 (1975)MathSciNetGoogle Scholar
  7. 7.
    Zadeh, L.A.: The concept of linguistic variable and its application to approximate reasoning (III). Inf. Sci. 9, 43–80 (1975)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Bellman, R.E., Zadeh, L.A.: Local and fuzzy logics. In: Klir, G.J., Yuan, B. (eds.) Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by L.A. Zadeh, pp. 283–335. World Scientific, Singapore (1996)Google Scholar
  9. 9.
    Huynh, V.N., Ho, T.B., Nakamori, Y.: A parametric representation of linguistic hedges in Zadeh’s fuzzy logic. Int. J. Approximate Reasoning 30, 203–223 (2002)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Molodtsov, D.A.: Soft set theory—first results. Comput. Math Appl. 37(4), 19–31 (1999)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Balamurugan, V., Senthamarai Kannan, K.: A framework for computing linguistic hedges in fuzzy queries. IJDMS 2(1) (2010)Google Scholar
  12. 12.
    Chandrasekhar, U., Mathur, S.: Decision making using fuzzy soft set inference system. In: Smart innovations, Systems and Technologies. Springer Book Series (ISBCC-16), pp. 445–457Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.CSE DepartmentBVRITHHyderabadIndia
  2. 2.SITEVellore Institute of TechnologyVelloreIndia

Personalised recommendations