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Abstract. It is vital to use accurate models for the analysis, design, and/or control of business processes. Unfortunately, there are often important discrepancies between reality and models. In earlier work, we have shown that simulation models are often based on incorrect assumptions and one example is the speed at which people work. The "Yerkes-Dodson Law of Arousal" suggests that a worker that is under time pressure may become more efficient and thus finish tasks faster. However, if the pressure is too high, then the worker's performance may degrade. Traditionally, it was difficult to investigate such phenomena and few analysis tools (e.g., simulation packages) support workload-dependent behavior. Fortunately, more and more activities are being recorded and modern process mining techniques provide detailed insights in the way that people really work. This paper uses a new process mining plug-in that has been added to ProM to explore the effect of workload on service times. Based on historic data and by using regression analysis, the relationship between workload and services time is investigated. This information can be used for various types of analysis and decision making, including more realistic forms of simulation.

Abstract. 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.

Abstract.Operational support is a specific type of process mining that assists users while process instances are being executed. Examples are predicting the remaining processing time of a running insurance claim and recommending the action that minimizes the treatment costs of a particular patient. Whereas it is easy to evaluate prediction techniques using cross validation, the evaluation of recommendation techniques is challenging as the recommender influences the execution of the process. It is therefore impossible to simply use historic event data. Therefore, we present an approach where we use a colored Petri net model of user behavior to drive a real workflow system and real implementations of operational support, thereby providing a way of evaluating algorithms for operational support before implementation and a costly test using real users. In this paper, we evaluate algorithms for operational support using different user models. We have implemented our approach using Access/CPN 2.0.

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