Decision Point Analysis of Time Series Data in Process-Aware Information Systems

Abstract

Abstract. The majority of process mining techniques focuses on con-trol flow. Decision Point Analysis (DPA) exploits additional data at-tachments 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 en-vironment 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 se-ries 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 DPATimeSeriesSim to produce log files and time series data. The DPATimeSeries method is evaluated based on an application scenario from the logistics domain.

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