Construction of Fuzzy Sets and Applying Aggregation Operators for Fuzzy Queries

Miroslav Hudec, Frantisek Sudzina

2012

Abstract

Flexible query conditions could use linguistic terms described by fuzzy sets. The question is how to properly construct fuzzy sets for each linguistic term and apply an adequate aggregation function. For construction of fuzzy sets, the lowest value, the highest value of attribute and the distribution of data inside its domain are used. The logarithmic transformation of domains appears to be suitable. This way leads to a balanced distribution of tuples over fuzzy sets. In addition, users’ opinions about linguistic terms as well as current content in database are merged. The second investigated issue is selection of an adequate aggregation operator. Usual t-norm functions as well as compensatory γ – operator have been examined. Finally, the interface for managing these issues has been proposed. A user can obtain an overview about stored data before running a query; that may reduce empty or overabundant answers.

References

  1. Bordogna, G., Psaila, G., 2008. Customizable Flexible Querying for Classical Relational Databases. In: Galindo J. (Ed.), Handbook of Research on Fuzzy Information Processing in Databases (pp. 191-217). IGI Global, London.
  2. Bosc, P., HadjAli, A., Pivert, O., 2008. Empty versus overabundant answers to flexible relational queries. Fuzzy Sets and Systems, 159, 1450-1467.
  3. Bosc, P., Pivert, O., 2000. SQLf query functionality on top of a regular relational database management system. In: Pons, M., Vila, M. A., Kacprzyk, J. (Eds.), Knowledge Management in Fuzzy Databases (pp. 171- 190). Physica-Verlag, Heidelberg.
  4. Branco, A., Evsukoff, A., Ebecken, N., 2005. Generating fuzzy queries from weighted fuzzy classifier rules, In ICDM workshop on Computational Intelligence in Data Mining. IOS Press.
  5. Detyniecki, M., 2001. Fundamentals on Aggregation Operators, In AGOP International Summer School on Aggregation Operators. Asturias.
  6. Dubois, D., Prade, H., 1997. Using fuzzy sets in flexible querying: Why and how? In: Andreasen, T., Christiansen, H., Larsen H.L. (Eds.), Flexible Query Answering Systems (pp. 45-60). Kluwer Academic Publishers, Dordrecht.
  7. Galindo, J., 2008. Introduction and Trends to Fuzzy Logic and Fuzzy Databases, In: Galindo J. (Ed.), Handbook of Research on Fuzzy Information Processing in Databases (pp. 1-33). IGI Global, London.
  8. Gurský, P., Vaneková, V., Pribolová, J., 2008. Fuzzy User Preference Model for Top-k Search. In IEEE World Congress on Computational Intelligence (WCCI). Hong Kong.
  9. Hudec, M., 2009. An approach to fuzzy database querying, analysis and realisation. Computer Science and Information Systems, 6(2), 127-140.
  10. Kacprzyk, J., Zadrozny, S., 2001. Computing with words in intelligent database querying: standalone and internet-based applications. Information Sciences, 134, 71-109.
  11. Kacprzyk, J., Zadrozny, S., 1995. FQUERY for Access: Fuzzy querying for windows-based DBMS, In: Bosc, P., Kacprzyk, J. (Eds.), Fuzziness in Database Management Systems (pp, 415-433). Physica-Verlag, Heidelberg.
  12. Klir, G., Yuan, B., 1995. Fuzzy sets and fuzzy logic, theory and applications, Prentice Hall. New Jersey.
  13. Meier, A., Werro, N., Albrecht, M., Sarakinos, M. 2005. Using a Fuzzy Classification Query Language for Customer Relationship Management. In Conference on Very Large Data Bases. ACM.
  14. Radojevic, D., 2008. Interpolative realization of Boolean algebra as a consistent frame for gradation and/or fuzziness, In: Nikravesh, M., Kacprzyk, J., Zadeh, L.A. (Eds.), Forging New Frontiers: Fuzzy Pioneers II Studies in Fuzziness and Soft Computing (pp. 295- 318). Springer-Verlag, Berlin and Heidelberg.
  15. Siler, W., Buckley, J., 2005. Fuzzy expert systems and fuzzy reasoning, John Wiley & Sons. New Jersey.
  16. Takaci, A., Škrbic, S., 2008. Priority, Weight and Threshold in Fuzzy SQL Systems. Acta Polytechnica Hungarica, 5(1), 59-68.
  17. Tudorie, C., 2008. Qualifying objects in classical relational database querying, In: Galindo J. (Ed.), Handbook of Research on Fuzzy Information Processing in Databases (pp. 218-245). IGI Global, London.
  18. Tudorie, C., 2009. Intelligent interfaces for database fuzzy querying, The annals of “Dunarea de Jos” University of Galati, Fascicle III, 32(2).
  19. Wang, T.C., Lee, H.D., Chen, C.M., 2007. Intelligent Queries based on Fuzzy Set Theory and SQL. In Joint Conference on Information Science, World Scientific.
  20. Werro, N., Meier, A., Mezger, C., Schindler, G., 2005. Concept and Implementation of a Fuzzy Classification Query Language. In International Conference on Data Mining. CSREA Press.
  21. Zadeh, L. A., 1965. Fuzzy sets. Information and Control, 8, 338-353.
  22. Zadrozny, S., Kacprzyk, J., 2009. Issues in the practical use of the OWA operators in fuzzy querying. Journal of Intelligent Information Systems, 33, 307-325.
  23. Zimmermann, H.-J., 2001. Fuzzy Set Theory - and Its Applications, Kluwer Academic Publishers. London.
Download


Paper Citation


in Harvard Style

Hudec M. and Sudzina F. (2012). Construction of Fuzzy Sets and Applying Aggregation Operators for Fuzzy Queries . In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-10-5, pages 253-258. DOI: 10.5220/0003968802530258


in Bibtex Style

@conference{iceis12,
author={Miroslav Hudec and Frantisek Sudzina},
title={Construction of Fuzzy Sets and Applying Aggregation Operators for Fuzzy Queries},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2012},
pages={253-258},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003968802530258},
isbn={978-989-8565-10-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Construction of Fuzzy Sets and Applying Aggregation Operators for Fuzzy Queries
SN - 978-989-8565-10-5
AU - Hudec M.
AU - Sudzina F.
PY - 2012
SP - 253
EP - 258
DO - 10.5220/0003968802530258