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Assessing the Influence of Conflict Profile Properties on the Quality of Consensus

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Intelligent Information and Database Systems (ACIIDS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12033))

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Abstract

Asserting a high quality of data integration results frequently involves broadening a number of merged data sources. But does more always mean more? In this paper we apply a consensus theory, originating from the collective intelligence field, and investigate which parameters describing a collective affects the quality of its consensus, which can be treated as an output of the data integration, most prominently. Eventually, we identified, either analytically or experimentally, adjusting which properties of the conflict profile (input data) asserts exceeding expected integration quality. In other words-which properties have the biggest influence and which are insignificant.

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References

  1. Bogart, K.P.: Preference structures I: distances between transitive preference relations. J. Math. Sociol. 3(1), 49–67 (1973)

    Article  MathSciNet  Google Scholar 

  2. Duong, T.H., Nguyen, N.T., Nguyen, D.C., Nguyen, T.P.T., Selamat, A.: Trust-based consensus for collaborative ontology building. Cybern. Syst. 45(2), 146–164 (2014)

    Article  Google Scholar 

  3. Duong, T.H., Nguyen, N.T., Truong, H.B., Nguyen, V.H.: A collaborative algorithm for semantic video annotation using a consensus-based social network analysis. Expert Syst. Appl. 42(1), 246–258 (2015)

    Article  Google Scholar 

  4. Green, B.: Testing and quantifying collective intelligence. In: Proceedings of the Collective Intelligence Conference (2015)

    Google Scholar 

  5. Izsak, R.: Working together: committee selection and the supermodular degree. In: Sukthankar, G., Rodriguez-Aguilar, J.A. (eds.) AAMAS 2017. LNCS (LNAI), vol. 10642, pp. 103–115. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71682-4_7

    Chapter  Google Scholar 

  6. Jha, D.K., Chattopadhyay, P., Sarkar, S., Ray, A.: Path planning in GPS-denied environments via collective intelligence of distributed sensor networks. Int. J. Control 89(5), 984–999 (2016)

    Article  MathSciNet  Google Scholar 

  7. Kemeny, J.G.: Mathematics without numbers. Daedalus 88, 577–591 (1959)

    Google Scholar 

  8. Kozierkiewicz, A.: The analysis of expert opinions’ consensus quality. Inf. Fusion 34, 80–86 (2017)

    Article  Google Scholar 

  9. Kozierkiewicz, A.: Analysis of susceptibility to the consensus for a few representations of collective knowledge. Int. J. Software Eng. Knowl. Eng. 24(5), 759–775 (2014)

    Article  Google Scholar 

  10. Lewoniewski, W., Węcel, K., Abramowicz, W.: Relative quality and popularity evaluation of multilingual wikipedia articles. Informatics 4(4), 43 (2017)

    Article  Google Scholar 

  11. McHugh, K.A., Yammarino, F.J., Dionne, S.D., Serban, A., Sayama, H., Chatterjee, S.: Collective decision making, leadership, and collective intelligence: tests with agent-based simulations and a field study. Leadersh. Quart. 27(2), 218–241 (2016)

    Article  Google Scholar 

  12. Nguyen, N.T.: Consensus Choice Methods and their Application to Solving Conflicts in Distributed Systems. Wroclaw University of Technology Press, Wroclaw (2002). (in Polish)

    Google Scholar 

  13. Nguyen, N.T.: Advanced Methods for Inconsistent Knowledge Management. Springer, London (2008). https://doi.org/10.1007/978-1-84628-889-0

    Book  MATH  Google Scholar 

  14. Pretorius, A., Parry, D.A.: Human decision making and artificial intelligence: a comparison in the domain of sports prediction. In: Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists, p. 32. ACM (2016)

    Google Scholar 

  15. Nguyen, V.D., Merayo, M.G.: Intelligent collective: some issues with collective cardinality. J. Inf. Telecommun. 1(2), 127–140 (2017)

    Google Scholar 

  16. Wagner, C., Suh, A.: The wisdom of crowds: impact of collective size and expertise transfer on collective performance. In: 47th Hawaii International Conference on System Sciences (HICSS), pp. 594–603. IEEE (2014)

    Google Scholar 

  17. Woolley, A.W., Aggarwal, I., Malone, T.W.: Collective intelligence and group performance. Curr. Dir. Psychol. Sci. 24(6), 420–424 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Adrianna Kozierkiewicz, Marcin Pietranik & Mateusz Sitarczyk

Authors
  1. Adrianna Kozierkiewicz
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  2. Marcin Pietranik
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  3. Mateusz Sitarczyk
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Corresponding author

Correspondence to Marcin Pietranik .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kietikul Jearanaitanakij

  3. Faculty of Computer Science and Information, University Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  4. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

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Kozierkiewicz, A., Pietranik, M., Sitarczyk, M. (2020). Assessing the Influence of Conflict Profile Properties on the Quality of Consensus. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12033. Springer, Cham. https://doi.org/10.1007/978-3-030-41964-6_3

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  • DOI: https://doi.org/10.1007/978-3-030-41964-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41963-9

  • Online ISBN: 978-3-030-41964-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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