Decision point analysis of time series data in process-aware information systems

The majority of process mining techniques focuses on control flow. Decision Point Analysis (DPA) exploits additional data attachments within log files to determine attributes decisive for branching of process paths within discovered process models. DPA considers only single attribute values. However, in many applications, the process environment provides additional data in form of consecutive measurement values such as blood pressure or container temperature. We introduce the DPATimeSeries method as an iterative process for exploiting time series data by combining process mining and data mining techniques. The method also offers different approaches for incorporating time series data into log files in order to enable existing process mining techniques to be applied. Finally, we provide the simulation environment DPATimeSeries Sim to produce log files and time series data. The DPATimeSeries method is evaluated based on an application scenario from the logistics domain.