Generating Event Logs with Workload-Dependent Speeds from Simulation Models
Both simulation and process mining can be used to analyze operational business processes. Simulation is model-driven and very useful because different scenarios can be explored by changing the model’s parameters. Process mining is driven by event data. This allows detailed analysis of the observed behavior showing actual bottlenecks, deviations, and other performance-related problems. Both techniques tend to focus on the control-flow and do not analyze resource behavior in a detailed manner. In this paper, we focus on workload-dependent processing speeds because of the well-known phenomenon that people perform best at a certain stress level. For example, the “Yerkes-Dodson Law of Arousal” states that people will take more time to execute an activity if there is little work to do. This paper shows how workload-dependent processing speeds can be incorporated in a simulation model and learned from event logs. We also show how event logs with workload-dependent behavior can be generated through simulation. Experiments show that it is crucial to incorporate such phenomena. Moreover, we advocate an amalgamation of simulation and process mining techniques to better understand, model, and improve real-life business processes.
- 3.van der Aalst, W.M.P., Nakatumba, J., Rozinat, A., Russell, N.: Business Process Simulation. In: vom Brocke, J., Rosemann, M. (eds.) Handbook on Business Process Management 1: Introduction, Methods and Information Systems. International Handbooks on Information Systems, pp. 313–338. Springer (2010)Google Scholar
- 6.CPN Tools, cpntools.org (2012)Google Scholar
- 10.Montgomery, D.C., Peck, E.A.: Introduction to Linear Regression Analysis. Wiley & Sons (1992)Google Scholar
- 12.Nakatumba, J., Westergaard, M., van der Aalst, W.M.P.: An Infrastructure for Cost-Effective Testing of Operational Support Algorithms Based on Colored Petri Nets. In: Proceedings of the 33rd International Conference on Applications and Theory of Petri Nets. LNCS. Springer (2012)Google Scholar
- 14.ProM (2012), http://www.processmining.org
- 16.Wickens, C.D.: Engineering Psychology and Human Performance. Harper (1992)Google Scholar