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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.
Kurssprache: English
English
Advanced, Beginner, Enterprise Computing
Kursinformationen
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.
Lernmaterial
Organization:
This section provides an overview of the organizational aspects of the course.Week 1:
In the first week, we will cover the fundamentals of process mining in eleven videos. We think it is important to start a course on process mining with the most important input: data.Week 2:
In the second week, we will cover advanced concepts of process mining in 10 videos. We will discuss quality metrics for process mining, conformance checking, enhancement techniques, and practical aspects.
Für diesen Kurs einschreiben
Lernende
Bewertungen
Der Kurs wurde mit durchschnittlich 4.71 Sternen bei 63 abgegebenen Stimmen bewertet.
Anforderungen für Leistungsnachweise
- Den Leistungsnachweis erhält, wer in der Summe aller benoteten Aufgaben mindestens 50% der Höchstpunktzahl erreicht hat.
- Die Teilnahmebestätigung erhält, wer auf mindestens 50% der Kursunterlagen zugegriffen hat.
- Einen Open Badge erhalten Sie, indem Sie den Kurs abschließen.
Mehr Informationen finden Sie in den Richtlinien für Leistungsnachweise.
Dieser Kurs wird angeboten von
Professor Mathias Weske leitet das Fachgebiet Business Process Technology am Hasso-Plattner-Instituts der Digital Engineering Fakultät der Universität Potsdam. Uns interessieren praktische Probleme im Geschäftsprozessmanagement; wir adressieren diese durch formale Methoden und bauen nützliche Prototypen. Seine Forschungsschwerpunkte sind prozessorientierte Informationssysteme, Process Mining und Event Processing. Das BPT-Forschungsteam verfügt über eine gute Bilanz bei der Entwicklung von Prototypen, was erhebliche Auswirkungen auf Forschung und Praxis hatte, einschließlich Projekte wie Oryx und jBPT. Dr. Weske ist Autor des ersten Lehrbuchs zum Geschäftsprozessmanagement und hielt 2013 den ersten groß angelegten öffentlichen Online-Kurs zu diesem Thema ab. Mathias Weske ist Gründungsmitglied der BPM-Konferenzreihe und seit September 2017 Vorsitzender des Lenkungsausschusses.
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.