A Method for Analyzing Time Series Data in Process Mining: Application and Extension of Decision Point Analysis

Abstract

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 DPATS method as an iterative process for exploiting time series data by combining process and data mining techniques. The latter ranges from visual mining to temporal data mining techniques such as dynamic time warping and response feature analysis. 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 DPATSSim to produce log files and time series data. The DPATS method is evaluated based on application scenarios from the logistics and medical domain.