Building an Knowledge Base of a Company Based on the Analysis of Employee’s Behavior

  • M. V. VinogradovaEmail author
  • A. S. Larionov
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 260)


The approach is proposed for building the enterprise knowledge base utilizing technologies of cyber-physical systems operating in virtual space. Integration of the enterprise information system (IS) with the knowledge base is considered as the cyber-physical system; employees act as physical objects. The knowledge base model has been developed, whose articles are matched with the objects and business processes of the main information system, based on a three-level semantic network. The algorithm for determining priority materials for writing into the knowledge base driven by the analysis of the user actions track was built. The user actions track is formed according to statistical data obtained from the event logs of the knowledge base and the IS using the sequential analysis to identify patterns of typical operations. The formal model of the interaction process of employees with the knowledge base was built, taking into account the activities of its formation and use. Simulation of the interaction process with the knowledge base was carried out, the results of which confirmed the effectiveness of the proposed approach. With an increase in the number of users, the cost of seeding the knowledge base, taking into account the relevance of its materials, pays its way due to reducing the employees idle time. The proposed models and algorithms contribute to reducing the cost of developing the enterprise knowledge base and increasing its utilization efficiency.


Knowledge management Enterprise knowledge base Semantic networks Analysis of user behavior Labor cost estimation Interaction simulation Sequential pattern 


  1. 1.
    Alavi, M., Leidner, D.E.: Knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Q. 107–136 (2001)Google Scholar
  2. 2.
    Gorlacheva, E.N., Gudkov, A.G., Omelchenko, I.N., Drogovoz, P.A., Koznov, D.V.: Knowledge management capability impact on enterprise performance in russian high-tech sector. In: 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), IEEE, pp. 1–9 (2018)Google Scholar
  3. 3.
    Migdadi, M.M., Abu Zaid, M.K.S.: An empirical investigation of knowledge management competence for enterprise resource planning systems success: insights from Jordan. Int. J. Prod. Res. 54(18), 5480–5498 (2016)CrossRefGoogle Scholar
  4. 4.
    Mohapatra, S., Agrawal, A., & Satpathy, A.: Designing knowledge management strategy. In Designing Knowledge Management-Enabled Business Strategies, pp. 55–88. Springer, Cham (2016)Google Scholar
  5. 5.
    Massaro, M., Handley, K., Bagnoli, C., Dumay, J.: Knowledge management in small and medium enterprises: a structured literature review. J. Knowl. Manag. 20(2), 258–291 (2016)CrossRefGoogle Scholar
  6. 6.
    Sadiku, M.N., Wang, Y., Cui, S., Musa, S.M.: Cyber-physical systems: a literature review. Eur. Sci. J. ESJ 13(36), 52 (2017)Google Scholar
  7. 7.
    Zanni, A. (2015). Cyber-physical systems and smart cities. IBM Big data and analytics, 20Google Scholar
  8. 8.
    Cassandras, C.G.: Smart cities as cyber-physical social systems. Engineering 2(2), 156–158 (2016)CrossRefGoogle Scholar
  9. 9.
    Yu, X., Pan, A., Tang, L.A., Li, Z., Han, J.: Geo-friends recommendation in gps-based cyber-physical social network. In: 2011 International Conference on Advances in Social Networks Analysis and Mining, IEEE, pp. 361–368, July 2011Google Scholar
  10. 10.
    Chernova, V.Y., Tretyakova, O.V., Vlasov, A.I.: Brand Marketing Trends in Russian Social Media. (2018). Accessed 20 Apr 2019
  11. 11.
    Popolov, D., Callaghan, M., Luker, P.: Conversation space: visualising multi-threaded conversation. In: Proceedings of the Working Conference on Advanced Visual Interfaces, ACM, pp. 246–249, May 2000Google Scholar
  12. 12.
    Vu, H.Q., Li, G., Law, R., Zhang, Y.: Travel diaries analysis by sequential rule mining. J. Travel Res. 57(3), 399–413 (2018)CrossRefGoogle Scholar
  13. 13.
    C-Rarus: 1C: Enterprise Platform. Business Automation, Consulting and Support. Accessed 20 Apr 2019
  14. 14.
    Vazhdaev, A.N., Chernysheva, T.Y., Lisacheva, E.I.: Software selection based on analysis and forecasting methods, practised in 1C. In: IOP Conference Series: Materials Science and Engineering, vol. 91, no. 1, p. 012067. IOP Publishing (2015)Google Scholar
  15. 15.
    Semantic MediaWiki (2019) Semantic MediaWiki. Accessed 20 Apr 2019
  16. 16.
    C-Rarus: Objects of Configuration at 1C: Enterprise 8. Accessed 20 Apr 2019
  17. 17.
    Clifton, B.: Advanced Web Metrics with Google Analytics. John Wiley & Sons (2012)Google Scholar
  18. 18.
    Aggarwal, C.C.: Data Mining: The Textbook. Springer (2015)Google Scholar
  19. 19.
    Fedotova, A.V., Tabakov, V.V., Ovsyannikov, M.V., Bruening, J.: Ontological modeling for industrial enterprise engineering. In: International Conference on Intelligent Information Technologies for Industry, pp. 182–189. Springer, Cham, September 2018Google Scholar
  20. 20.
    Chernenkiy, V., Gapanyuk, Y., Terekhov, V., Revunkov, G., Kaganov, Y.: The hybrid intelligent information system approach as the basis for cognitive architecture. Procedia Comput. Sci. 145, 143–152 (2018)CrossRefGoogle Scholar
  21. 21.
    Kanev, A., Cunningham, S., Valery, T.: Application of formal grammar in text mining and construction of an ontology. In: 2017 Internet Technologies and Applications (ITA), IEEE, pp. 53–57, September 2017Google Scholar
  22. 22.
    Chernenkiy, V.M., Gapanyuk, Y.E., Kaganov, Y.T., Dunin, I.V., Lyaskovsky, M.A., Larionov, V.S.: Storing Metagraph Model in Relational, Document-Oriented, and Graph Databases (2018)Google Scholar
  23. 23.
    Alfimtsev, A.N., Loktev, D.A., Loktev, A.A.: Comparison of development methodologies for systems of intellectual interaction. In: Proceedings of Moscow State University of Civil Engineering, no. 5, pp. 200–208 (2013)Google Scholar
  24. 24.
    Barbara, D., Kamath, C. (eds.) Proceedings of the 2003 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics (2003)Google Scholar
  25. 25.
    Cook, J.E., Du, Z., Liu, C., Wolf, A.L.: Discovering models of behavior for concurrent workflows. Comput. Ind. 53(3), 297–319 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Bauman Moscow State Technical UniversityMoscowRussian Federation

Personalised recommendations