The Efficiency Analysis of the Multi-level Consensus Determination Method

  • Adrianna Kozierkiewicz-Hetmańska
  • Mateusz Sitarczyk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10448)

Abstract

The task of processing large sets of data which are stored in distributed sources is still a big problem. The determination of a one, consistent version of data could be very time- and cost-consuming. Therefore, the balance between the time of execution and the quality of the integration results is needed. This paper is devoted to a multi-level approach to data integration using the Consensus Theory. The experimental verification of multi-level integration methods has proved that the division of integration task into smaller subproblems gives similar results as the one-level approach, but improves a time performance.

Notes

Acknowledgment

This article is based upon work from COST Action KEYSTONE IC1302, supported by COST (European Cooperation in Science and Technology).

References

  1. 1.
    Arrow, K.J.: Social Choice and Individual Values. Wiley, New York (1963)MATHGoogle Scholar
  2. 2.
    Barthelemy, J.P.: Thresholded consensus for n-trees. J. Classif. 5, 229–236 (1988)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Barthelemy, J.P., Janowitz, M.F.: A formal theory of consensus. SIAM J. Discrete Math. 4, 305–322 (1991)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Barthelemy, J.P., Leclerc, B.: The median procedure for partitions. In: DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 19, pp. 3–33 (1995)Google Scholar
  5. 5.
    Barthelemy, J.P., Monjardet, B.: The median procedure in cluster analysis and social choice theory. Math. Soc. Sci. 1, 235–267 (1981)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Brown, F.N.: Boolean Reasoning. Kluwer Academic Publisher, Dordrecht (1990)CrossRefGoogle Scholar
  7. 7.
    Daniłowicz, C., Nguyen, N.T.: Consensus-based partition in the space of ordered partitions. Pattern Recogn. 21, 269–273 (1988)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Day, W.H.E.: Consensus methods as tools for data analysis. In: Bock, H.H. (ed.) Classification and Related Methods for Data Analysis, pp. 312–324. North-Holland, Amsterdam (1988)Google Scholar
  9. 9.
    Kozierkiewicz-Hetmańska, A.: Analysis of susceptibility to the consensus for a few representations of collective knowledge. Int. J. Softw. Eng. Knowl. Eng. 24(5), 759–775 (2014)CrossRefGoogle Scholar
  10. 10.
    Kozierkiewicz-Hetmańska, A.: Comparison of one-level and two-level consensuses satisfying the 2-optimality criterion. In: Nguyen, N.-T., Hoang, K., Jȩdrzejowicz, P. (eds.) ICCCI 2012. LNCS, vol. 7653, pp. 1–10. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-34630-9_1CrossRefGoogle Scholar
  11. 11.
    Kozierkiewicz-Hetmańska, A., Pietranik, M.: The knowledge increase estimation framework for ontology integration on the concept level. J. Intell. Fuzzy Syst. 32(2), 1161–1172 (2017)CrossRefGoogle Scholar
  12. 12.
    Kozierkiewicz-Hetmańska A., Pietranik, M.: Assessing the quality of a consensus determined using a multi-level approach, In: Proceedings of the 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (2017, to appear)Google Scholar
  13. 13.
    Kozierkiewicz-Hetmańska, A., Pietranik, M., Hnatkowska, B.: The knowledge increase estimation framework for ontology integration on the instance level. In: Nguyen, N.T., Tojo, S., Nguyen, L.M., Trawiński, B. (eds.) ACIIDS 2017. LNCS, vol. 10191, pp. 3–12. Springer, Cham (2017). doi: 10.1007/978-3-319-54472-4_1CrossRefGoogle Scholar
  14. 14.
    Kozierkiewicz-Hetmańska, A., Nguyen, N.T.: A comparison analysis of consensus determining using one and two-level methods. In: Advances in Knowledge-Based and Intelligent Information and Engineering Systems, pp. 159–168 (2012)Google Scholar
  15. 15.
    McMorris, F.R., Powers, R.C.: The median function on weak hierarchies. In: DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 37, pp. 265–269 (1997)Google Scholar
  16. 16.
    Mirkin, B.G.: Problems of group choice, Nauka Moscow (1974)Google Scholar
  17. 17.
    Nguyen, N.T.: Using distance functions to solve representations choice problems. Fundamenta Informaticae 48, 295–314 (2001)MathSciNetGoogle Scholar
  18. 18.
    Nguyen, N.T.: Consensus Choice Methods and their Application to Solving Conflicts in Distributed Systems, Wroclaw University of Technology Press (2002). (in Polish)Google Scholar
  19. 19.
    Nguyen, N.T.: Advanced Methods for Inconsistent Knowledge Management. Springer-Verlag, London (2008)CrossRefGoogle Scholar
  20. 20.
    Pawlak, Z.: Rough Sets-Theoretical Aspects of Reasoning About Data. Kluwer Academic Publisher, Dordrecht (1991)MATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Adrianna Kozierkiewicz-Hetmańska
    • 1
  • Mateusz Sitarczyk
    • 1
  1. 1.Faculty of Computer Science and ManagementWroclaw University of Science and TechnologyWroclawPoland

Personalised recommendations