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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

The lemniscate knowledge flow model

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DOI: http://dx.doi.org/10.15439/2017F357

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 12171220 ()

Full text

Abstract. Knowledge is seen as one of the main resources for organizations providing knowledge-intensive services. Therefore, sharing and reusing are the main goals of modern knowledge management (KM) approach, driven by information and communication technologies (ICT). However, one can ask for the details in order to provide means and tools to design and deploy environment able to fulfil these two goals. We observed that occurred interactions on knowledge resources can be reduced to a directional flow, and further described by distinguished internal phases. In our research we put forward two research questions: (1) what are the main entities in the knowledge flow supported by ICT? and (2) what are the main phases of the knowledge flow? In this paper we introduce the generic leminiscate knowledge flow model, which grounded on the recognized theory, learned principles and gathered practices, provides foundations to solve the above problem.

References

  1. K. Marciniak, and M. L. Owoc, "Systemy klasy business intelligence w jednostkach sektora publicznego-wstępne studium badań,” Studia Ekonomiczne, vol. 199, 2014, pp. 166–175.
  2. M. Hernes, "Using Cognitive Agents for Unstructured Knowledge Management in a Business Organization’s Integrated Information System”, Intelligent Information and Database Systems, Springer, Berlin 2016, pp. 344–353.
  3. G. Kayakutlu, and E. Laurent-Mercier, "From knowledge workerknowledge cultivator-effective dynamics", IEEE 2012, pp. 1149–1153.
  4. M. Hernes, and A. Bytniewski, “Towards Big Management” [in:] Advanced Topics in Intelligent Information and Database Systems, to Springer 2017, pp. 197–209.
  5. M. L. Owoc, and P. Weichbroth, “A Framework for Web Usage Mining Based on Multi-Agent and Expert System”. AITM2011, Wrocław 2011, pp. 139–151.
  6. T. Sitek, and P. Weichbroth, “Ekonometryczne szacowanie parametrów jako metoda przetwarzania wstępnego w systemach agentowych”, PWNT, Gdańsk 2009, pp. 303–313.
  7. P. Weichbroth, and M. Owoc, “Wartościowanie wiedzy o ścieżkach nawigacji użytkowników portali internetowych”, Technologie wiedzy w zarządzaniu publicznym. Wydawnictwo UE w Katowicach, Katowice 2014, pp. 326–337.
  8. M. Owoc, and P. Weichbroth, "Toward knowledge-grid model for academic purposes”. AI4KM2015, Buenos Aires 2015, pp. 5–9.
  9. M. L. Owoc, and P. Weichbroth, “Transformacje wiedzy sieciowej. Podstawy ontologiczne”. In: Wiedza w kreowaniu przedsiębiorczości, K. Perechuda, I. Chomiak-Orsa (Eds.), Wydawnictwo Politechniki Częstochowskiej, Częstochowa 2014. pp. 165–177.
  10. M. Owoc, and K. Marciniak, "Knowledge management as foundation of smart university", IEEE, 2013, pp.1267–1272.
  11. K. Marciniak, and M. L. Owoc, "Usability of Knowledge Grid in Smart City Concepts”, ICEIS (3), 2013, pp. 341–346.
  12. K. Marciniak, and M. L. Owoc, "Applying of knowledge grid models in smart city concepts”, Uniwersytet Ekonomiczny we Wrocławiu, Wrocław 2013, pp. 238–244.
  13. M. Alsqour, and M. L. Owoc, "Benefits of knowledge acquisition systems for management. An empirical study", IEEE, 2015, pp. 1691–1698.
  14. M. L. Owoc, "The Role of Data Warehouse as a Source of Knowledge Acquisition in Decision-Making. An Empirical Study”. In: AI for Knowledge Management. Springer 2014, pp. 21–42.
  15. M. Alsqour, M. L. Owoc, and A. S. Ahmed, "Data warehouse as a source of knowledge acquisition. An empirical study", IEEE, 2014, pp. 1421–1430.
  16. P. Kapłański, and P. Weichbroth, "Cognitum Ontorion: Knowledge Representation and Reasoning System”, IEEE 2015, pp. 169–176.
  17. A. Seganti, P. Kapłański, and P. Zarzycki, "Collaborative Editing of Ontologies Using Fluent Editor and Ontorion”, Ontology Engineering, Springer, 2015, pp. 45–55.
  18. P. Kapłański, "Controlled English interface for knowledge bases”, Studia Informatica, vol. 32(2A), 2011, pp. 485–494.
  19. M. H. Zack, “Rethinking the knowledge-based organization”. MIT Sloan Management Review, vol. 44(4), 2003, pp. 67–72.
  20. P. Weichbroth, and M. L. Owoc, “Web User Navigation Patterns Discovery as Knowledge Validation challenge", AI4KM 2012, France 2012, pp. 33–39.
  21. E. Mercier-Laurent, J. Jakubczyc, and M. L. Owoc, “What is Knowledge Management?”, Prace Naukowe Akademii Ekonomicznej we Wrocławiu, vol. 815, 1999, pp. 9–21.
  22. J. Fazlagić, M. Sikorski, and A. Sala, „Portale intranetowe. Zarządzanie wiedzą, kapitał intelektualny, korzyści dla pracowników i dla organizacji”. Politechnika Gdańska, Gdańsk 2014.
  23. P. Dominiak, and K. Leja, „Czy uniwersytet potrzebuje strategii?”. CBPNiSW, Uniwersytet Warszawski, 2000, pp. 26–42.
  24. D. J. Teece, “Research directions for knowledge management. California management review, vol. 40(3), 1998, pp. 289–292.
  25. M. L. Owoc, M. Ochmanska, and T. Gladysz, "On principles of knowledge validation”, Validation and Verification of Knowledge Based Systems, Springer, 1999, pp. 25–35.
  26. M. L. Owoc, "Wartościowanie wiedzy w inteligentnych systemach wspomagających zarządzanie”, Prace Naukowe Akademii Ekonomicznej we Wrocławiu. vol. 100 (1047), Wrocław 2004.
  27. N. Rizun, and Y. Taranenko, “Simulation models of human decision-making processes”, Management Dynamics in the Knowledge Economy. College of Management, vol. 2(2), 2014, pp. 241–264.
  28. P. Weichbroth, “Facing the Brainstorming Theory. A Case of Requirements Elicitation”. Studia Ekonomiczne. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach, 6 (296), pp. 151–162.