Adaptable Cross-organizational Unstructured Business Processes via Dynamic Rule-based Semantic Network


Rapid online adaptation to the new business requirements can improve innovation level and market competency of collaborative organizations. Complex and unstructured processes are provision e-services in collaborative networks through web service inter-connections, which unanticipated changes made it hard to manage them. In a cross-organizational domain, when partners deal with unexpected changes, the received requests are represented as complex theories, and business process adaptation will be more complicated in the occurrence of concept drifts. Rapid prediction and adaptation to new situations need concept drift detection and novel class prediction mechanism for the overall collaborative network processes in both data and control flows. In this article, a new approach to the online reflection of distributed rule concept drifts of collaborative network reference processes is introduced. The solution to the data-informed adaptation of unstructured process employed managing rule concept drifts and cross-organizational processes restructuring via the distributed model with minor updates. The method could react to the new events via an ensemble prediction mechanism. Its architecture has major components for monitoring, dynamic distributed rule reconfiguration, and partners side components. The paper suggests a service-oriented semantic network of distributed rules for e-services provision, substitution, and replacement via management of choreographed web-services. The approach has been validated and verified with real data belonging to the healthcare domain. The results prove the adaptation mechanism efficiency of the daily changes.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. Barthe-Delanoë, A.-M., Truptil, S., Benaben, F., & Pingaud, H. (2014). Event-driven agility of interoperability during the Run-time of collaborative processes. Decision Support Systems, 59, 171–179.

  2. Bastida, L., Nieto, F.J., Tola, R. (2008). Context-aware service composition: a methodology and a case study. In 2nd international workshop on Systems development in SOA environments, pp. 19-24.

  3. Bernal, M., Jose, F., Falcarin, P., Morisio, M., Dai, J. (2010). Dynamic context-aware business process: a rule-based approach supported by pattern identification. in Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 470-474.

  4. Brzeziński, D. (2015). Block-based and online ensembles for concept-drifting data streams. Poznan University of Technology, Doctoral dissertation.

  5. Brzezinski, D., & Piernik, M. (2015). Structural XML Classification in Concept Drifting Data Streams. New Generation Computing, 33(4), 345–366.

    Article  Google Scholar 

  6. Cognini, R, Corradini, F., Gnesi, S., Polini, A., Re, B. (2014). Research challenges in business process adaptability. In Proceedings of the 29th Annual ACM Symposium on Applied Computing - SAC ‘14, pp. 1049 – 1054.

  7. Courbis, C., Finkelstein, A. (2005). Towards aspect weaving applications. In In Software Engineering, 2005. ICSE 2005. Proceedings. 27th International Conference on, pp. 69-77.

  8. de Leoni, M., van der Aalst, W. M. P., & Dees, M. (2016). A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Information Systems, 56, 235–257.

    Article  Google Scholar 

  9. Delias, P., Grigori, D., Mouhoub, M.L., Tsoukias, A. (2015). Discovering Characteristics that Affect Process Control Flow. Decision Support Systems IV-Information and Knowledge Management in Decision Processes, pp. 51-63.

  10. Döhring, M., Reijers, H. A., & Smirnov, S. (2014). Configuration vs. adaptation for business process variant maintenance: an empirical study. Information Systems, 39, 108–133.

    Article  Google Scholar 

  11. Domingos, P., Hulten, G. (2000). Mining high-speed data streams. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80.

  12. Engel, R., van der Aalst, W.M.P., Zapletal, M., Pichler, C., Werthner, H. (2012). Mining inter-organizational business process models from edi messages: A case study from the automotive sector. In Advanced Information Systems Engineering, pp. 222-237.

  13. Harmon, P. (2016). The state of business process management. BpTrends.

  14. Hermosillo, G. (2012). Towards creating context-aware dynamically-adaptable business processes using complex event processing. Université des Sciences et Technologie de Lille-Lille I, PhD Thesis.

  15. Hofmann, M., Betke, H., Sackmann, S. (2015). Automated Analysis and Adaptation of Disaster Response Processes with Place-Related Restrictions. In ISCRAM, pp. 266-276.

  16. Isazadeh, A., Pedrycz, W., & Mahan, F. (2014). ECA rule learning in dynamic environments. Expert Systems with Applications, 41(17), 7847–7857.

    Article  Google Scholar 

  17. Janssoone, T., Clavel, C., Bailly, K., Richard, G. (2016). Using temporal association rules for the synthesis of embodied conversational agents with a specific stance. In International Conference on Intelligent Virtual Agents, pp. 175-189.

  18. Kalibatiene, D., & Vasilecas, O. (2010). Ontology axioms for the implementation of business rules. Technological and Economic Development of Economy, 16(3), 471–486.

    Article  Google Scholar 

  19. Kozlenkov, A. (2005). The World Wide Web Consortium.

  20. Lankhorst, M. M. (2004). Enterprise architecture modelling—the issue of integration. Advanced Engineering Informatics, 18(4), 205–216.

    Article  Google Scholar 

  21. Mendling, J., & Hafner, M. (2008). From WS-CDL choreography to BPEL process orchestration. Journal of Enterprise Information Management, 21(5), 525–542.

    Article  Google Scholar 

  22. Nishida, K., Yamauchi, K., Omori, T. (2005). ACE: Adaptive classifiers-ensemble system for concept-drifting environments. In 6th International Workshop on Multiple Classifier Systems, volume 3541 of Lecture Notes in Computer Science, pp. 176–185.

  23. Papamarkos, G., Poulovassilis, A., Wood, P.T. (2003). Event-condition-action rule languages for the semantic web. In First International Conference on Semantic Web and Databases, pp. 294-312.

  24. Patiniotakis, I., Papageorgiou, N., Verginadis, Y., Apostolou, D., Mentzas, G. (2012). An aspect oriented approach for implementing situational driven adaptation of bpmn2. 0 workflows. In International Conference on Business Process Ma, pp. 414-425.

  25. Rozinat, A., van der Aalst, W.M.P. (2006a). Decision mining in ProM. International Conference on Business Process Management, pp. 420-425.

  26. Rozinat, A., van der Aalst, W.M.P. (2006b). Decision mining in ProM. In International Conference on Business Process Management, pp. 420-425.

  27. Sánchez, M., Villalobos, J. (2008). A flexible architecture to build workflows using aspect-oriented concepts. In AOSD workshop on Aspect-oriented modeling, pp. 25-30.

  28. Sprovieri, D., Diaz, D., Mazo, R., Hinkelmann, K. (2016). Run-time planning of case-based busi- ness processes. In IEEE 10th International Conference on Research Challenges in Information Science(RCIS), Grenoble, pp. 1-6.

  29. Tsai, C.-J., Lee, C.-I., & Yang, W.-P. (2009). Mining decision rules on data streams in the presence of concept drifts. Expert Systems with Applications, 36(2), 1164–1178.

    Article  Google Scholar 

  30. Tsourveloudis, N. C., & Valavanis, K. P. (2002). On the measurement of enterprise agility. Journal of Intelligent and Robotic Systems, 33(3), 329–342.

    Article  Google Scholar 

  31. ur Rahman, S.S., Lodhi, A., Aoumeur, N., Rautenstrauch, C., Saake, G. (2009). Intra-service adaptability for eca-centric web services using contract and aspect. In The IADIS International Conference Information Systems.

  32. Van Der Aalst, W., et al (2011) Process mining manifesto. Business Process Management, 169-194.

  33. Weber, B., Reichert, M., & Rinderle-Ma, S. (2008). Change patterns and change support features–enhancing flexibility in process-aware information systems. Data & Knowledge Engineering, 66, 438–466.

    Article  Google Scholar 

  34. Yiannis, D.A.V., Barthe-Delanoe A.-M., Benaben, F. (2013). Addressing agility in collaborative processes: a comparative study. In 7th IEEE, International Conference on Digital Ecosystems and Technologies (DEST), pp. 120-125.

  35. Zico Kolter, J., & Maloof, M. A. (2007). Dynamic weighted majority: An ensemble method for drifting concepts. Machine Learning Research, 8, 2755–2790.

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Ehsan Alirezaei.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Alirezaei, E., Parsa, S. Adaptable Cross-organizational Unstructured Business Processes via Dynamic Rule-based Semantic Network. Inf Syst Front 22, 771–787 (2020).

Download citation


  • Unstructured processes
  • Business process adaptation
  • Collaborative network
  • Ensemble prediction
  • Choreographed web-services