Process mining and simulation

So far we have seen how workflow models can be defined in YAWL and how the YAWL workflow engine allows a model to drive business processes. Once such a model becomes operational, the Engine automatically logs activity, keeping track of task start and completion events, their time of occurrence, the resources involved, and so on (cf. Chap. 9, Sect. 9.7). These logs are a valuable source of information about the way a business process actually performs in practice, and can be used as a basis for operational decision making. Process mining is a technology that uses event logs (i.e., recorded actual behaviors) to analyze workflows. This is a valuable outcome in its own right, because such dynamically captured information can alert us to practical problems with the workflow model, such as hotspots or bottlenecks, that cannot be identified by mere inspection of the static model alone. Further, the information extracted through process mining can be used to calibrate simulations of the workflow’s potential future behaviors. In effect, this gives us a fast forward capability, which allows future activity to be predicted from the current system state and explored for various anticipated scenarios. In this chapter, we explain how YAWL’s logs can be mined, and how the information extracted from them can be used to calibrate simulations of expected future behavior. This is done using two additional tools: the process mining tool ProM and the modeling and simulation package CPN Tools