Real World Process

Real-world process analysis focuses on understanding and optimizing complex processes using data-driven techniques. Current research emphasizes integrating domain expertise with automated process discovery methods, often leveraging large language models and agent-based simulation to capture nuanced behaviors beyond traditional workflow models. This work is significant for improving process efficiency, detecting anomalies (including subtle, gradual drifts), and enabling more effective process redesign across diverse domains, from manufacturing to service industries. The development of robust, interpretable models and the integration of human knowledge remain key challenges and active research areas.