ProcessExplorer: Interactive Visual Exploration of Event Logs with Analysis Guidance
Alexander Seeliger, Maximilian Ratzke, Timo Nolle, Max Mühlhäuser · TUbilio (Technical University of Darmstadt) · 2019
Process analysts use process mining techniques to obtain fact-based knowledge from event logs about how business processes are actually executed in organizations. Often process discovery is the first step in their analytical workflow. However, when working with large amount of data and complex processes, exploring as-is process models to obtain interesting and insightful knowledge can be challenging. We propose ProcessExplorer, an interactive visual recommendation system for process discovery to ease event log exploration. ProcessExplorer automatically analyzes the event log to obtain promising subsets of cases, evaluates interesting process performance indicators, and recommends those that are most interesting and insightful. Our system uses multi-perspective trace clustering to identify candidate cases of interest and a deviation-based approach to assess the interestingness of process performance indicators. We implemented ProcessExplorer as a standalone desktop application that allows to explore any process and any event log. Our demo shows how the workflow of analysts is supported by the system through suggesting subset and insights recommendations.