Business Architecture Flexibility as a Result of Knowledge-Intensive Process Management

  • Alexander Gromoff
  • Yulia Bilinkis
  • Nikolay KazantsevEmail author
Original Article


In 2016 a survey was conducted among Russian companies to discover the most common problems associated with flexibility of business process management. A gap between strict process formalization demands and unpredictable nature of many knowledge-intensive operations was identified. The article suggests an approach to facilitate process management via combined context-aware set of methods. Firstly, the key terms are selected to serve as special cause indicators of variation in a process instance based on risk profiles. Afterward a cloud service is called, which automatically analyzes semantic annotation of the concrete process instance. Risk detection service identifies potential operational risks and in case of unexpected process execution complexities notifies users. Finally, expert search service calls for an expert in an organization automatically to create expert community. This novel approach could be used for knowledge-intensive business sectors (such as Research and Development) or in any organization interested in increasing its agility in changing business environment.


Business agility Business flexibility Business process management Knowledge-intensive process management Business architecture Knowledge-intensive industries 



This work/article is an output of a research project implemented as part of the Research Program of the Faculty of Business and Management at National Research University—Higher School of Economics (116-02-0005).


  1. Aranda-Corral, G. A., Borrego-Díaz, J., Galán-Páez, J., & Jiménez-Mavillard, A. (2014). Emergent concepts on knowledge intensive processes. In International conference on computational collective intelligence (pp. 282–291). Berlin: Springer.Google Scholar
  2. Di Ciccio, C., Marrella, A., & Russo, A. (2012). Knowledge-intensive processes: An overview of contemporary approaches⋆. Knowledge-Intensive Business Processes, 33–47.Google Scholar
  3. Dyer, L. D., & Ericksen, J. (2008). Complexity-based agile enterprises: Putting self-organizing emergence to work (CAHRS Working Paper #08-01) (pp. 2–42). Ithaca, NY: Cornell University. Accessed 12 Jan 2017.
  4. Filimonova, E., Kazantsev, N., & Zueva, A. (2016). Entropy-based approach for semi-structured processes enhancement. Accessed 12 Jan 2017.
  5. Fleischmann, A. (2009). What is s-bpm? In International conference on subject-oriented business process management (pp. 85–106). Berlin: Springer.Google Scholar
  6. Fleischmann, A., & Stary, C. (2012). Whom to talk to? A stakeholder perspective on business process development. Universal Access in the Information Society, 11(2), 125–150.CrossRefGoogle Scholar
  7. Gonçalves, P., das Dores, W. J., & Benevenuto, F. (2015). Medindo sentimentos no Twitter por meio de uma escala psicométrica/Measuring sentiments on Twitter by means of a psychometric scale. Revista Electronica de Sistemas de Informaçao, 14(2), 1.CrossRefGoogle Scholar
  8. Gregus M., & Kryvinska N. (2015). Service orientation of enterprises—aspects, dimensions, technologies. Comenius University in Bratislava, ISBN: 9788022339780.Google Scholar
  9. Gromoff, A., Chebotarev, V., Evina, K., & Stavenko, Y. (2011). An approach to agility in enterprise innovation. In International conference on subject-oriented business process management (pp. 271–280). Berlin: Springer.Google Scholar
  10. Gromoff, A., Kazantsev, N., Kozhevnikov, D., Ponfilenok, M., & Stavenko, Y. (2012). Newer approach to create flexible business architecture of modern enterprise. Global Journal of Flexible Systems Management, 13(4), 207–215.CrossRefGoogle Scholar
  11. Gromoff, A., Kazantsev, N., Schumsky, L., & Konovalov, N. (2014). Business transformation based on cloud services. In Services computing (SCC), 2014 IEEE international conference on (pp. 844–845). IEEE.Google Scholar
  12. Gromov, A. I., Kazantsev, N., & Zueva, A. (2016). Applying extended DMAIC methodology to optimize weakly structured business processes. Бизнec-инфopмaтикa, 3(37), 72–79.Google Scholar
  13. Head, M. R., Sailer, A., Shaikh, H., & Viswanathan, M. (2009). Taking it management services to a cloud. In 2009 IEEE international conference on cloud computing (pp. 175–182). IEEE.Google Scholar
  14. Kaczor, S., & Kryvinska, N. (2013). It is all about services-fundamentals, drivers, and business models. Journal of Service Science Research, 5(2), 125–154.CrossRefGoogle Scholar
  15. Kolodziej, J., & Xhafa, F. (2011). Supporting situated computing with intelligent multi-agent systems. International Journal of Space-Based and Situated Computing, 1(1), 30–42.CrossRefGoogle Scholar
  16. Kopetzky, R., Günther, M., Kryvinska, N., Mladenow, A., Strauss, C., & Stummer, C. (2013). Strategic management of disruptive technologies: A practical framework in the context of voice services and of computing towards the cloud. International Journal of Grid and Utility Computing, 4(1), 47–59.CrossRefGoogle Scholar
  17. Kryvinska, N. (2012). Building consistent formal specification for the service enterprise agility foundation. Journal of Service Science Research, 4(2), 235–269.CrossRefGoogle Scholar
  18. Kryvinska, N., & Gregus, M. (2014). SOA and its business value in requirements, features, practices and methodologies. Comenius University in Bratislava, ISBN: 9788022337649.Google Scholar
  19. Marin M. A., Hauder M., & Matthes F. (2015). Case management: An evaluation of existing approaches for knowledge-intensive processes, business. In Process management workshops, Volume 256 of the series Lecture notes in business information processing (pp. 5–16).Google Scholar
  20. Mudambi, R. (2008). Location, control and innovation in knowledge-intensive industries. Journal of Economic Geography, 8(5), 699–725.CrossRefGoogle Scholar
  21. Papazoglou, M. P., & Van Den Heuvel, W. J. (2007). Service oriented architectures: Approaches, technologies and research issues. The VLDB Journal, 16(3), 389–415.CrossRefGoogle Scholar
  22. Pesic, M., & Van der Aalst, W. M. (2006). A declarative approach for flexible business processes management. In International conference on business process management (pp. 169–180). Berlin: Springer.Google Scholar
  23. Ploesser, K., Peleg, M., Soffer, P., Rosemann, M., & Recker, J. C. (2009). Learning from context to improve business processes. BPTrends, 6(1), 1–7.Google Scholar
  24. Ren, C., Wang, W., Dong, J., Ding, H., Shao, B., & Wang, Q. (2008). Towards a flexible business process modeling and simulation environment. In 2008 Winter simulation conference (pp. 1694–1701). IEEE.Google Scholar
  25. Romanov, D., Ponfilenok, M., & Kazantsev, N. (2013). Potential innovations (new ideas/trends) detection in information network. International Journal of Future Computer and Communication, 2(1), 63.CrossRefGoogle Scholar
  26. Rosemann, M. (2014). Proposals for future BPM research directions. In Asia-Pacific conference on business process management (pp. 1–15). Berlin: Springer.Google Scholar
  27. Scheer, A. W., Kruppke, H., Jost, W., & Kindermann, H. (Eds.). (2006). Agility by ARIS business process management: Yearbook business process excellence 2006/2007 (Vol. 243). Berlin: Springer.Google Scholar
  28. Scheithauer, G., & Hellmann, S. (2012). Analysis and documentation of knowledge-intensive processes. In International conference on business process management (pp. 3–11). Berlin: Springer.Google Scholar
  29. Stavenko, Y., Kazantsev, N., & Gromoff, A. (2013). Business process model reasoning: From workflow to case management. Procedia Technology, 9, 806–811.CrossRefGoogle Scholar
  30. Tenschert, J., & Lenz, R. (2015). Supporting knowledge work by speech-act based templates for micro processes. In International conference on business process management (pp. 78–89). Berlin: Springer.Google Scholar
  31. van der Aalst, W. (2016). Process mining: The missing link. In Process mining (pp. 25–52). Berlin: Springer.Google Scholar
  32. van der Aalst, W. M., La Rosa, M., & Santoro, F. M. (2016). Don’t forget to improve the process!. Berlin: Springer. doi: 10.1007/s12599-015-0409-x.Google Scholar
  33. Weber, I. M. (2009). Semantic methods for execution-level business process modeling. In Modeling support through process verification and service composition (Vol. 40). Berlin: Springer.Google Scholar

Copyright information

© Global Institute of Flexible Systems Management 2017

Authors and Affiliations

  • Alexander Gromoff
    • 1
  • Yulia Bilinkis
    • 1
  • Nikolay Kazantsev
    • 1
    Email author
  1. 1.BPM Group, Faculty of Business and Management, Business Informatics SchoolNational Research University “Higher School of Economics” (NRU HSE)MoscowRussia

Personalised recommendations