Visual Analytics in Process Mining for Supporting Business Process Improvement

Abstract

The increasing amounts of data have affected conceptual modeling as a research field. In this context, process mining involves a set of techniques aimed at extracting a process schema from an event log generated during process execution. While automatic algorithms for process mining and analysis are needed to filter out irrelevant data and to produce preliminary results, visual inspection, domain knowledge, human judgment and creativity are needed for proper interpretation of the results. Moreover, a process discovery on an event log usually results in complicated process models not easily comprehensible by the business user. To this end, visual analytics has the potential to enhance process mining towards the direction of explainability, interpretability and trustworthiness in order to better support human decisions. In this paper we propose an approach for identifying bottlenecks in business processes by analyzing event logs and visualizing the results. In this way, we exploit visual analytics in the process mining context in order to provide explainable and interpretable analytics results for business processes without exposing to the user complex process models that are not easily comprehensible. The proposed approach was applied to a manufacturing business process and the results show that visual analytics in the context of process mining is capable of identifying bottlenecks and other performance-related issues and exposing them to the business user in an intuitive and non-intrusive way.