Week 1 introduces the main concepts in process mining, using a sample business process. We explore the data generated during the execution of this process and we transform data items to events that tell us about the execution of process activities. Process discovery shows us how business processes are actually executed. After week 1 you will have a good understanding of fundamental concepts in process mining, including event log generation and process discovery.
Week 2 focuses on process mining techniques beyond process discovery. First, we explore how data about the process execution can be used to detect undesired behavior and potential compliance issues. Second, we take a look at how process mining can help to understand more detailed aspects of the process execution. This includes understanding what decisions have been made in the process and why and what factors determine the overall completion time of a process. After week 2 you will have an overview and a good understanding of the potential of process mining beyond process discovery.
Find out more in the certificate guidelines.
Professor Dr. Mathias Weske is chair of the business process technology research group at Hasso Plattner Institute at the Digital Engineering Faculty, University of Potsdam, Germany. The research group aims at addressing real-world problems in business process management with formal approaches and engineering useful prototypes. His research focuses on the engineering of process oriented information systems, process mining, and event processing. The BPT research group has a track record in engineered prototypes with a significant impact on research and practice, including projects like Oryx and jBPT. Dr. Weske is author of the first textbook on business process management and he held the first massive open online course on the topic in 2013. He is on the Editorial Board of Springer’s Computing journal, and he is a founding member of the steering committee of the BPM conference series and, since September 2017, chair of the steering committee.
Prof. Dr. Henrik Leopold is Associate Professor for Data Science and Business Intelligence at the Kühne Logistics University (KLU) in Hamburg, Germany and senior researcher at the Hasso Plattner Institute (HPI) at the Digital Engineering Faculty, University of Potsdam, Germany. His research focuses on developing novel techniques for process mining and analysis based on technology from the areas of machine learning and natural language processing. The results of his research have been published in leading journals, such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Software Engineering, and Information Systems. He is also serving as a Programm Committee member for several major conferences in the areas of business process management and information systems engineering, such as BPM, CAiSE, and ICPM.