AB-BPM: Performance-Driven Instance Routing for Business Process Improvement

  • Suhrid SatyalEmail author
  • Ingo Weber
  • Hye-young Paik
  • Claudio Di Ciccio
  • Jan Mendling
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10445)


A fundamental assumption of Business Process Management (BPM) is that redesign delivers new and improved versions of business processes. This assumption, however, does not necessarily hold, and required compensatory action may be delayed until a new round in the BPM life-cycle completes. Current approaches to process redesign face this problem in one way or another, which makes rapid process improvement a central research problem of BPM today. In this paper, we address this problem by integrating concepts from process execution with ideas from DevOps. More specifically, we develop a technique called AB-BPM that offers AB testing for process versions with immediate feedback at runtime. We implemented this technique in such a way that two versions (A and B) are operational in parallel and any new process instance is routed to one of them. The routing decision is made at runtime on the basis of the achieved results for the registered performance metrics of each version. AB-BPM provides for ultimate convergence towards the best performing version, no matter if it is the old or the new version. We demonstrate the efficacy of our technique by conducting an extensive evaluation based on both synthetic and real-life data.


Business Process Management DevOps AB testing Process performance indicators 



The work of Claudio Di Ciccio has received funding from the EU H2020 programme under the MSCA-RISE agreement 645751 (RISE_BPM).


  1. 1.
    Agrawal, S., Goyal, N.: Thompson sampling for contextual bandits with linear payoffs. In: International Conference on Machine Learning, ICML (2013)Google Scholar
  2. 2.
    Alter, S.: Work system theory: overview of core concepts, extensions, and challenges for the future. J. Assoc. Inf. Syst. 14, 72 (2013)Google Scholar
  3. 3.
    Bass, L., Weber, I., Zhu, L.: DevOps: A Software Architect’s Perspective. Addison-Wesley Professional, New York (2015)Google Scholar
  4. 4.
    Burattin, A.: PLG2: multiperspective processes randomization and simulation for online and offline settings. CoRR abs/1506.08415 (2015)Google Scholar
  5. 5.
    Burtini, G., Loeppky, J., Lawrence, R.: A survey of online experiment design with the stochastic multi-armed bandit. CoRR abs/1510.00757 (2015)Google Scholar
  6. 6.
    Cabanillas, C., Di Ciccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 424–432. Springer, Cham (2014). doi: 10.1007/978-3-319-10172-9_31CrossRefGoogle Scholar
  7. 7.
    Chapelle, O., Li, L.: An empirical evaluation of Thompson sampling. In: Neural Information Processing Systems (NIPS) (2011)Google Scholar
  8. 8.
    Crook, T., Frasca, B., Kohavi, R., Longbotham, R.: Seven pitfalls to avoid when running controlled experiments on the web. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1105–1114 (2009)Google Scholar
  9. 9.
    Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Gregory, F.: Cause, effect, efficiency and soft systems models. J. Oper. Res. Soc. 44, 333–344 (1993)CrossRefGoogle Scholar
  11. 11.
    Hammer, M., Champy, J.: Reengineering the Corporation: A Manifesto for Business Revolution. HarperCollins, New York (1993)Google Scholar
  12. 12.
    Holland, C.W.: Breakthrough Business Results with MVT: A Fast, Cost-Free “Secret Weapon” for Boosting Sales, Cutting Expenses, and Improving Any Business Process. Wiley, Hoboken (2005)Google Scholar
  13. 13.
    Jiang, W., Au, T., Tsui, K.L.: A statistical process control approach to business activity monitoring. IIE Trans. 39(3), 235–249 (2007)CrossRefGoogle Scholar
  14. 14.
    Kettinger, W.J., Teng, J.T.C., Guha, S.: Business process change: a study of methodologies, techniques, and tools. MIS Q. 21(1), 55–98 (1997). Scholar
  15. 15.
    Kohavi, R., Longbotham, R., Sommerfield, D., Henne, R.M.: Controlled experiments on the web: survey and practical guide. Data Min. Knowl. Discov. 18(1), 140–181 (2009)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Kohavi, R., Crook, T., Longbotham, R., Frasca, B., Henne, R., Ferres, J.L., Melamed, T.: Online experimentation at Microsoft. In: Workshop on Data Mining Case Studies (2009)Google Scholar
  17. 17.
    Li, L., Chu, W., Langford, J., Schapire, R.E.: A contextual-bandit approach to personalized news article recommendation. In: International Conference on World Wide Web (2010)Google Scholar
  18. 18.
    Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Ōno, T.: Toyota Production System: Beyond Large-scale Production. Productivity Press, Portland (1988)Google Scholar
  20. 20.
    Poelmans, S., Reijers, H.A., Recker, J.: Investigating the success of operational business process management systems. Inf. Tech. Manage. 14(4), 295–314 (2013)CrossRefGoogle Scholar
  21. 21.
    Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning, 1st edn. MIT Press, Cambridge (1998)Google Scholar
  22. 22.
    Teinemaa, I., Leontjeva, A., Masing, K.O.: BPIC 2015: diagnostics of building permit application process in Dutch municipalities. BPI Challenge Report 72 (2015)Google Scholar
  23. 23.
    Thompson, W.R.: On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25(3/4), 285–294 (1933)CrossRefGoogle Scholar
  24. 24.
    Weidlich, M., Ziekow, H., Gal, A., Mendling, J., Weske, M.: Optimizing event pattern matching using business process models. IEEE Trans. Knowl. Data Eng. 26(11), 2759–2773 (2014)CrossRefGoogle Scholar
  25. 25.
    Weidlich, M., Ziekow, H., Mendling, J., Günther, O., Weske, M., Desai, N.: Event-based monitoring of process execution violations. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 182–198. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23059-2_16CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Suhrid Satyal
    • 1
    • 2
    Email author
  • Ingo Weber
    • 1
    • 2
  • Hye-young Paik
    • 2
  • Claudio Di Ciccio
    • 3
  • Jan Mendling
    • 3
  1. 1.Data61CSIROSydneyAustralia
  2. 2.University of New South WalesSydneyAustralia
  3. 3.Vienna University of Economics and BusinessViennaAustria

Personalised recommendations