Role-Driven Context-Based Decision Support: Approach, Implementation and Lessons Learned

  • Alexander Smirnov
  • Tatiana Levashova
  • Nikolay Shilov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 553)


Today, companies have to deeply transform both their product development structure and the structure of their business processes. Context-based decision support has shown its efficient applicability in this area. However, implementation of such complex changes in large companies faces many difficulties. The paper describes the methodology of context-based decision support. The context specifies domain knowledge describing the task to be solved and its situation. It is produced based on the knowledge extracted from a common ontology. The context usage is facilitated via role-based knowledge management. The major steps of the approach implementation in collaboration with an industrial partner are described. The observations made during the implementation of the approach addressing problems related to the implementation itself and generic principles that helped to overcome the problems are discussed.


Context management Decision support Role Ontology Lesson 



The research was supported partly by projects funded by grants #14-07-00345, #15-07-08092, #14-07-00427, #15-07-08391, #15-07-09229, #14-07-00378 of the Russian Foundation for Basic Research and by Government of Russian Federation, Grant 074-U01.


  1. 1.
    Gunasekaran, A., Lai, K., Cheng, T.: Responsive supply chain: a competitive strategy in a networked economy. Omega 36, 549–564 (2008)CrossRefGoogle Scholar
  2. 2.
    Gunasekaran, A., Ngai, N.: Build-to-order supply chain management: literature review and framework for development. J. Oper. Manage. 23(5), 423–451 (2005)CrossRefGoogle Scholar
  3. 3.
    Christopher, M., Towill, D.: An integrated model for the design of agile supply chains. Int. J. Phys. Distrib. Oper. Manage. 31, 235–244 (2001)CrossRefGoogle Scholar
  4. 4.
    Blomqvist, E.: The use of semantic web technologies for decision support–a survey. Semant. Web 5(3), 177–201 (2014)Google Scholar
  5. 5.
    Smirnov, A., Pashkin, M., Levashova, T., Chilov, N.: Fusion-based knowledge logistics for intelligent decision support in network-centric environment. In: Klir, G.J. (ed.) Int. J. Gen. Syst. 34(6), 673–690. Taylor & Francis (2005)Google Scholar
  6. 6.
    Oluikpe, P.: Developing a corporate knowledge management strategy. J. Knowl. Manage. 16(6), 862–878 (2012)CrossRefGoogle Scholar
  7. 7.
    Oroszi, A., Jung, T., Smirnov, A., Shilov, N., Kashevnik, A.: Ontology-driven codification for discrete and modular products. Int. J. Prod. Dev. 8(2), 162–177 (2009)CrossRefGoogle Scholar
  8. 8.
    Smirnov, A., Kashevnik, A., Teslya, N., Shilov, N., Oroszi, A., Sinko, M., Humpf, M., Arneving, J.: Knowledge management for complex product development. In: Bernard, A., Rivest, L., Dutta, D. (eds.) PLM 2013. IFIP AICT, vol. 409, pp. 110–119. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Smirnov, A., Shilov, N., Kashevnik, A., Jung, T., Sinko, M., Oroszi, A.: Ontology-driven product configuration: industrial use case. In: Proceedings of International Conference on Knowledge Management and Information Sharing (KMIS 2011), pp. 38–47 (2011)Google Scholar
  10. 10.
    Smirnov, A., Shilov, N.: Role-driven knowledge management implementation: lessons learned. In: Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS 2014), pp. 36–43 (2014)Google Scholar
  11. 11.
    Botkin, J.: Smart Business: How Knowledge Communities Can Revolutionize Your Company. Free Press, New York (1999)Google Scholar
  12. 12.
  13. 13.
    Web services explained. services_explained.html
  14. 14.
  15. 15.
    Smirnov, A., Levashova, T., Shilov, N.: Knowledge sharing in flexible production networks: a context-based approach. In: Graves, A., Stone, G., Miemczyk, J. (eds.) Int. J. Automot. Technol. Manage. (IJATM) 9(1), 87–109. Inderscience Publishers (2009)Google Scholar
  16. 16.
    Bradfield, D.J., Gao, J.X., Soltan, H.: A metaknowledge approach to facilitate knowledge sharing in the global product development process. Comput. Aided Des. Appl. 4(1–4), 519–528 (2007)CrossRefGoogle Scholar
  17. 17.
    Chan, E.C.K., Yu, K.M.: A framework of ontology-enabled product knowledge management. Int. J. Prod. Dev. 4(3–4), 241–254 (2007)CrossRefGoogle Scholar
  18. 18.
    Patil, L., Dutta, D., Sriram, R.: Ontology-based exchange of product data semantics. IEEE Trans. Autom. Sci. Eng. 2(3), 213–225 (2005)CrossRefGoogle Scholar
  19. 19.
    Uschold, M., Grüninger, M.: Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11(2), 93–155 (1996)CrossRefGoogle Scholar
  20. 20.
    Zimmermann, A., Lorenz, A., Oppermann, R.: An operational definition of context. In: Kokinov, B., Richardson, D.C., Roth-Berghofer, T.R., Vieu, L. (eds.) CONTEXT 2007. LNCS (LNAI), vol. 4635, pp. 558–571. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Balandin, S., Boldyrev, S., Oliver, I.J., Turenko, T., Smirnov, A.V., Shilov, N.G., Kashevnik, A.M.: Method and Apparatus for Ontology Matching, US Patent 2012/0078595 A1 (2012)Google Scholar
  22. 22.
    Baumgaertel, H.: Distributed constraint processing for production logistics. IEEE Intell. Syst. 15(1), 40–48 (2000)CrossRefGoogle Scholar
  23. 23.
    Tsang, J.P.: Constraint propagation issues in automated design. In: Gottlob, G., Nejdl, W. (eds.) Expert Systems in Engineering Principles and Applications. LNCS, vol. 462, pp. 135–151. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  24. 24.
    Neches, R., Fikes, R., Finin, T., Gruber, T., Patil, R., Senator, T., Swartout, W.: Enabling technology for knowledge sharing. AI Mag. 12(3), 36–56 (1991)Google Scholar
  25. 25.
    Smirnov, A., Levashova, T., Shilov, N.: Semantic-oriented support of interoperability between production information systems. Int. J. Prod. Dev. 4(3/4), 225–240 (2007)CrossRefGoogle Scholar
  26. 26.
    Simon, H.A.: Making management decisions: the role of intuition and emotion. Acad. Manage. Executive 1, 57–64 (1987)CrossRefGoogle Scholar
  27. 27.
    Lundqvist, M.: Information demand and use: improving information flow within small-scale business contexts. Licentiate thesis, Department of Computer and Information Science, Linköping University, Linköping, Sweden (2007)Google Scholar
  28. 28.
    Persson, A., Stirna, J.: PoEM 2009. LNBIP, vol. 39. Springer, Heidelberg (2009)CrossRefzbMATHGoogle Scholar
  29. 29.
    Fox, M.S., Barbuceanu, M. Gruninger, M.: An organisation ontology for enterprise modelling: preliminary concepts for linking structure and behavior. In: Proceedings of the Fourth Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 71–81 (1995)Google Scholar
  30. 30.
    Tarasov, V., Sandkuhl, K.: On the role of competence models for business and IT alignment in network organizations. In: Abramowicz, W., Maciaszek, L., Węcel, K. (eds.) BIS Workshops 2011 and BIS 2011. LNBIP, vol. 97, pp. 208–219. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  31. 31.
    VDMA, German Engineering Federation.
  32. 32.
    Smirnov, A., Sandkuhl, K., Shilov, N., Kashevnik, A.: “Product-process-machine” system modeling: approach and industrial case studies. In: Grabis, J., Kirikova, M., Zdravkovic, J., Stirna, J. (eds.) PoEM 2013. LNBIP, vol. 165, pp. 251–265. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alexander Smirnov
    • 1
    • 2
  • Tatiana Levashova
    • 1
  • Nikolay Shilov
    • 1
  1. 1.SPIIRASSt. PetersburgRussia
  2. 2.ITMO UniversitySt. PetersburgRussia

Personalised recommendations