A Tool for Business Processes Diagnostics

Recorded event data of processes inside organizations is a valuable source for providing insights and information using process mining. Most techniques analyze process executions at detailed levels, e.g., process instances, which may result in missing insights. Techniques at detailed levels using detailed event data should be complemented by techniques at aggregated levels. We designed and developed a standalone tool for diagnostics in event data of business processes based on both detailed and aggregated data and techniques. The data-driven framework first analyzes the event data of processes for possible compliance and performance problems, e.g., bottlenecks in processes. The results are used for aggregating the event data per window of time, i.e., extracting features in the time series format. The tool is able to uncover hidden insights in an explainable manner using time series analysis. The focus of the tool is to provide a data-driven business process analysis at different levels while reducing the dependencies on the user's domain knowledge for interpretation and feature engineering steps. The tool is applied to both real-world and synthetic event data.