Refactoring business process models with process fragments substitution


Since business processes are important assets of enterprises, thousands of business processes are modeled. After modeling business process models, a problem arises here is how to improve the efficiency of these models? In this paper, we propose a novel approach to refactor business process models with process fragments substitution for efficiency improvement. First, we propose a process model smell for identifying inefficient process fragments in business process models based on a sequence relation matrix and a data dependency matrix. Second, we propose a refactoring technique to replace inefficient process fragments in business process models with efficient process fragments. After refactoring, concurrent execution of business tasks in efficient process fragments can be maximized, so the efficiency of business process models can be improved. Experiments show our approach can improve efficiency of business process models effectively.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15


  1. 1.

    Xu, X., Mo, R., Dai, F., Lin, W., Wan, S., & Dou, W. (2019). Dynamic resource provisioning with fault tolerance for data-intensive meteorological workflows in cloud. IEEE Transactions on Industrial Informatics.

  2. 2.

    Xu, X., Liu, X., Xu, Z., Wang, C., Wan, S., & Yang, X. (2019). Joint optimization of resource utilization and load balance with privacy preservation for edge services in 5G networks. Mobile Networks and Applications.

  3. 3.

    Xu, X., Liu, X., Xu, Z., Dai, F., Zhang, X., & Qi, L. (2019). Trust-oriented IoT service placement for smart cities in edge computing. IEEE Internet of Things Journal.

  4. 4.

    Qi, L., Dou, W., Wang, W., Li, G., Yu, H., & Wan, S. (2018). Dynamic mobile crowdsourcing selection for electricity load forecasting. IEEE Access, 6, 46926–46937.

    Article  Google Scholar 

  5. 5.

    Qi, L., Chen, Y., Yuan, Y., Fu, S., Zhang, X., & Xu, X. (2019). A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems. World Wide Web Journal.

  6. 6.

    Xu, X., Zhang, X., Gao, H., Xue, Y., Qi, L., & Dou, W. (2019). BeCome: Blockchain-enabled computation offloading for IoT in mobile edge computing. IEEE Transactions on Industrial Informatics.

  7. 7.

    Xu, X., He, C., Xu, Z., Qi, L., Wan, S., & Bhuiyan, M. Z. A. (2019). Joint optimization of offloading utility and privacy for edge computing enabled IoT. IEEE Internet of Things Journal.

  8. 8.

    Gong, W., Qi, L., & Xu, Y. (2018). Privacy-aware multidimensional mobile service quality prediction and recommendation in distributed fog environment. Wireless Communications and Mobile Computing, 2018(4), 1–8.

    Google Scholar 

  9. 9.

    Zhang, M., Zhang, N., Li, H., & Gu, Z. (2018). A decomposition-based approach to optimization of TTP-based distributed embedded systems. Journal of Systems Architecture, 91, 53–61.

    Article  Google Scholar 

  10. 10.

    Zhao, Q., Gu, Z., Zeng, H., & Zheng, N. (2018). Schedulability analysis and stack size minimization with preemption thresholds and mixed-criticality scheduling. Journal of Systems Architecture, 83, 57–74.

    Article  Google Scholar 

  11. 11.

    Gu, Z., & Qiu, M. (2018). Embedded artificial intelligence and smart computing. Journal of Systems Architecture, 84, 1.

    Article  Google Scholar 

  12. 12.

    Wan, S., Qi, L., Xu, X., Tong, C., & Gu, Z. (2019). Deep learning models for real-time human activity recognition with smartphones. Mobile Networks and Applications, 25(1), 1–13.

    Google Scholar 

  13. 13.

    Wen, L. J., Wang, J. M., van der Aalst, W. M. P., & Huang, B. Q. (2010). Mining process models with prime invisible tasks. Data & Knowledge Engineering, 69(10), 999–1021.

    Article  Google Scholar 

  14. 14.

    Wan, S., Gu, Z., & Ni, Q. (2019). Cognitive computing and wireless communications on the edge for healthcare service robots. Computer Communications, 149, 99–106.

    Article  Google Scholar 

  15. 15.

    Wan, S., & Goudos, S. (2019). Faster R-CNN for multi-class fruit detection using a robotic vision system. Computer Networks, 168, 107036.

    Article  Google Scholar 

  16. 16.

    Jin, T., Wang, J., Yang, Y., Wen, L., & Li, K. (2016). Refactor business process models with maximized parallelism. IEEE Transactions on Services Computing, 9(3), 456–468.

    Article  Google Scholar 

  17. 17.

    Herbst, J., & Karagiannis, D. (2004). Workflow mining with InWoLvE. Computers in Industry, 53(3), 245–264.

    Article  Google Scholar 

  18. 18.

    Khlif, W., & Ben Abdallah, H. (2015). Integrating semantics and structural information for BPMN model refactoring. Proceedings of IEEE/ACIS 14th international conference on computer and information science (ICIS) (pp. 656–660). Las Vegas, NV: IEEE Computer Society.

  19. 19.

    Fernández-Ropero, M., Pérez-Castillo, R., & Piattini, M. (2012). Refactoring business process models—a systematic review. Information Systems, 37(5), 443–459.

    Article  Google Scholar 

  20. 20.

    Dijkman, R., Gfeller, B., Küster, J., & Völzer, H. (2011). Identifying refactoring opportunities in process model repositories. Information and Software Technology, 53(9), 937–948.

    Article  Google Scholar 

  21. 21.

    Weber, B., Reichert, M., Mendling, J., & Reijers, H. A. (2011). Refactoring large process model repositories. Computers in Industry, 62(5), 467–486.

    Article  Google Scholar 

  22. 22.

    Qi, L., Zhang, X., Li, S., Wan, S., Wen, Y., Gong, W. (2019). Spatial-temporal data-driven service recommendation with privacy-preservation. Information Sciences.

  23. 23.

    van der Aalst, W. M. P. (1998). The application of Petri nets to workflow management. Journal of Circuits, Systems, and Computers, 8(1), 21–66.

    Article  Google Scholar 

  24. 24.

    Mendling, J., Reijers, H. A., & van der Aalst, W. M. P. (2010). Seven process modeling guidelines (7PMG). Information & Software Technology, 52(2), 127–136.

    Article  Google Scholar 

  25. 25.

    Kiepuszewski, B., ter Hofstede, A. H. M., & Bussler, C. (2000). On structured workflow modelling. CAiSE 2000. (Vol. 1789, pp. 431–445). Lecture notes in computer science. Berlin: Springer.

  26. 26.

    Mens, T., & Tourwe, T. (2004). A survey of software refactoring. IEEE Transactions on Software Engineering, 30(2), 126–139.

    Article  Google Scholar 

  27. 27.

    Dingle, N. J., Knottenbelt, W. J., & Suto, T. (2009). PIPE2:a tool for the performance evaluation of generalised stochastic Petri Nets. Measurement and Modeling of Computer Systems, 36(4), 34–39.

    Google Scholar 

  28. 28.

    Mendling, J. (2008). Metrics for process models: empirical foundations of verification, error prediction, and guidelines for correctness. Lecture notes in business information processing (Vol. 6, pp. 103–133). Heidelberg: Springer.

  29. 29.

    Mo, Q., Song, W., Dai, F., Lin, L., & Li, T. (2019). Development of collaborative business processes: A correctness enforcement approach. IEEE Transactions on Services Computing.

  30. 30.

    Leopold, H., Smirnov, S., & Mendling, J. (2010). Refactoring of process model activity labels. In International conference on application of natural language to information systems (NLDB 2010) (pp. 268–276). Berlin: Springer.

  31. 31.

    Leopold, H., Smirnov, S., & Mendling, J. (2012). On the refactoring of activity labels in business process models. Information Systems, 37(5), 443–459.

    Article  Google Scholar 

  32. 32.

    Leopold, H., Eid-Sabbagh, R. H., Mendling, J., Azevedo, L. G., & Baião, F. A. (2013). Detection of naming convention violations in process models for different languages. Decision Support Systems, 56, 310–325.

    Article  Google Scholar 

  33. 33.

    Mendling, J., Reijers, H. A., & Recker, J. (2010). Activity labeling in process modeling: Empirical insights and recommendations. Information Systems, 35(4), 467–482.

    Article  Google Scholar 

  34. 34.

    Cardoso, J. (2005). Control-flow complexity measurement of processes and Weyuker’s properties. In In Proceedings of the 6th international conference on Enformatika (Vol. 8, pp. 213–218). Hungary: IEEE Computer Society.

  35. 35.

    Cardoso, J. (2006). Process control-flow complexity metric: An empirical validation. In Proceedings of IEEE international conference on services computing (IEEE SCC 06) (pp. 167–173). Piscataway: IEEE Computer Society.

  36. 36.

    Polyvyanyy, A., García-Bañuelos, L., & Dumas, M. (2010). Structuring acyclic process models. Information Systems, 37(6), 518–538.

    Article  Google Scholar 

  37. 37.

    Polyvyanyy, A., García-Bañuelos, L., Fahland, D., & Weske, M. (2011). Maximal structuring of acyclic process models. The Computer Journal, 57(1), 12–35.

    Article  Google Scholar 

  38. 38.

    Mendling, J., Reijers, & H. A., Cardoso, J. (2007). What makes process models understandable? In Proceedings of the 5th international conference on business process management (BPM 2007) (pp. 48–63). Berlin: Springer.

  39. 39.

    Vanderfeesten, I., Reijers, H. A., Mendling, J., van der Aalst, W. M. P., & Cardoso, J. (2008). On a quest for good process models: the cross-connectivity metric. In Advanced information systems engineering (CAiSE 2008). Lecture notes in computer science (Vol. 5074, pp. 480–494). Berlin: Springer.

  40. 40.

    Mendling, J., Verbeek, H., van Dongen, B., van der Aalst, W. M. P., & Neumann, G. (2008). Detection and prediction of errors in EPCs of the SAP reference model. Data & Knowledge Engineering, 64(1), 312–329.

    Article  Google Scholar 

  41. 41.

    Stefanie, R. M., & Weber, B. (2008). On the formal semantics of change patterns in process-aware information systems. In Proceedings of the 27th international conference on conceptual modeling (ER2008) (Vol. 5231, pp. 279–293). Berlin: Springer.

  42. 42.

    Weber, B., Reichert, M., & Stefanie, R. M. (2008). Change patterns and change support features enhancing flexibility in process-aware information systems. Data and Knowledge Engineering, 66(3), 438–466.

    Article  Google Scholar 

Download references


This work was supported in part by the Project of National Natural Science Foundation of China under Grant No. 61702442, 61862065, and 61662085, the Application Basic Research Project in Yunnan Province Grant No. 2018FB105.

Author information



Corresponding author

Correspondence to Fei Dai.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dai, F., Mo, Q., Li, T. et al. Refactoring business process models with process fragments substitution. Wireless Netw (2020).

Download citation


  • Business process model
  • Refactoring
  • Process fragment substitution
  • Efficiency improvement
  • Petri Nets