Process Data

Process data analysis focuses on extracting meaningful insights from sequential data representing events or activities within a system, aiming to improve process understanding, prediction, and optimization. Current research emphasizes leveraging machine learning, particularly neural networks (including transformers and attention mechanisms), and generative models to handle noisy, incomplete, or high-dimensional data, often incorporating knowledge graphs or procedural models to enhance accuracy and address data biases. This field is crucial for diverse applications, from improving business processes and automating quality control in manufacturing to personalizing education and enhancing human-AI interaction by incorporating process-level information into decision-making models.