Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times. (September 2019) - Richter, Florian; Seidl, Thomas
Abstract: Business processes are dynamic and change due to diverse factors. While existing approaches aim to detect drifts in the process structure, Tesseract looks for temporal drifts in activity interim times. This orthogonal view on the process extends the traditional data cube of events – case id, activities and timestamps – by a fourth dimension and improves the operational support by a visualization of temporal drifts in real-time. Insights about temporal deviations lead to an augmented awareness of imminent failures or improved service times. The detection of related structural concept drifts can be improved by early warning, as operation times of critical parts often increase before they catastrophically fail. Highlights: Utilization of event interim times for process analysis. Adaptation of a scalable and noise adapting trend detection method from text mining. Anytime drift detection in events streams for operational support. Visualization for supervised domain expert interaction. Application of Tesseract in the remaining time prediction.