Process mining is widely used in organizations to improve the understanding of business processes, based on data. Therefore, process mining is also called “data science for business processes”. While process mining has gone mainstream, there are many underlying concepts and techniques, and these are complex. The goal of this online course is to provide a general understanding of the concepts and techniques behind process mining. The course will be most valuable for domain experts, whose business processes are investigated, and for professionals in IT and in business consulting. We aim at providing a common understanding and a common language that facilitates communication between all stakeholders involved in process mining projects.
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.
Mathias Weske教授是德國波茨坦大學數字工程學院Hasso Plattner研究所業務流程技術研究小組的主席。該研究小組旨在通過形式化方法和有用的原型來解決業務流程管理中的實際問題。他的研究專注於面向流程的信息系統，流程挖掘和事件處理的工程。 BPT研究小組在工程原型方面擁有良好的記錄，這對研究和實踐產生了重大影響，包括Oryx和jBPT等項目。 Weske博士是第一本有關業務流程管理的教科書的作者，並於2013年舉辦了有關該主題的第一本大規模的在線公開課程。 BPM會議系列，以及自2017年9月以來擔任指導委員會主席。
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.