Mining Event Logs to Assist the Development of Executable Process Variants

Developing process variants has been proven as a principle task to flexibly adapt a business process model to different markets. Contemporary research on variant development has focused on conceptual process models. However, process models do not always exist, even when process logs are available in information systems. Moreover, process logs are often more detailed than process models and reflect more closely to the behavior of the process. In this paper, we propose an activity recommendation approach that takes into account process logs for assisting the development of executable process variants. To this end, we define a notion of neighborhood context for each activity based on logs, which captures order constraints between activities with their occurrence frequency. The similarity of the neighborhood context between activities provides us then with a basis to recommend activities during the process of creating a new process model. The approach has been implemented as a plug-in for ProM. Furthermore, we conducted experiments on a large collection of process logs. The results indicate that our approach is feasible and applicable in real use cases.