Research
Bridging process science and data science to solve real-world business problems.
Research Overview
Our lab aims to solve real-world business problems by advancing research in process science, data science, and process mining which bridges two disciplines.
- Process Science × Data Science
- Event-log-driven methodology
- Real-world industrial application
Research Areas
Process Model Discovery
Extracting and constructing a comprehensive model of a business process from event logs — uncovering the actual sequence of activities rather than relying on preconceived or manual models.
- •Development of novel discovery algorithms
- •Optimization of existing algorithms
- •Industrial application
Conformance Checking & Anomaly Detection
Comparing real process executions against reference models to measure adherence, and identifying unusual patterns that deviate from standard behavior — errors, fraud, or inefficiencies.
- •Development of novel checking algorithms
- •Optimization of existing algorithms
- •Industrial application
Predictive Process Monitoring
Using data analysis and machine learning to anticipate future states of business processes — next activity, remaining time, outcome — based on historical and real-time data.
- •Development of novel monitoring algorithms
- •Optimization of existing algorithms
- •Industrial application