Optimizing business processes is vital for achieving organizational strategic goals. This project explores advanced process knowledge using machine learning and computational intelligence. Ongoing works focus on process discovery, quality metrics, concept drift, interpretability and explainability, prediction and prescription, bias and fairness, LLMs, trace clustering, graph embeddings, sequencial association rules, semantic parsing, plain language, and OCEL, aiming at process and organizational improvement across both private and public organizations, including governmental entities.