Class Fundamentals of Process Mining

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the class provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. This class explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis and prediction techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains.  

Not applicable

Fundaments of Process Mining Bridging Data Science to Process Science Introduction to Process Models Process Discovery, Conformance Checking and Enhancement Process Simulation Operational Support (Predictions)

Theoretical concepts are introduced in class, and then they are complemented with real-world examples. For each topic, the students are given a set of exercises that aim to apply the theoretical concepts. Exercises are discussed and solved in class, students are invited to share any doubts they might have.  Support materials and exercises with resolution suggestions will be available on Moodle.  It is believed that continuous assessment, adapted according to the evolution of students, is a good practice. Individual monitoring and availability to clarify doubts, whenever necessary, is essential for the student and his/her performance.

W. Van Der Aalst (2016). Process Mining: Data Science in Action. 2nd ed. Springer-Verlag Berlin Heidelberg.