LTAT.05.025 Business Process Mining
Process Mining is a discipline at the intersection of Data Mining and Business Process Management, providing techniques to discover, analyze, and enhance processes using event data recorded by enterprise systems. These data-driven methods form the foundation for (evidence-based) process improvement by offering insights into how processes are executed and enabling forecasting techniques such as business process simulation, among other capabilities.
This course will provide you with an introduction to process mining techniques, including techniques for automated process discovery, conformance checking, performance analysis, business rules mining, and predictive process monitoring. In order to develop a mastery of these techniques, we will introduce specific tools for process mining, including Apromore and various Python libraries, and we will apply these tools to answer business questions using real-life datasets.
Course Outcomes
Upon completion of this course, you will be able to:
- Analyze business process event logs using process mining techniques.
- Identify a suitable combination of process mining techniques to address a given business question.
- Use a process mining tool at an intermediate level.
- Explain how to use process mining techniques in conjunction with other data mining and business process analysis techniques to improve a business process.
Important information
- Instructors
- David Chapela de la Campa (david [dot] chapela [ät] ut [dot] ee)
- Attendance
- Lectures and Practicals will take place on Mondays 14:15-15:45 and 16:15-17:45, respectively (room 2048).
- In-person attendance is preferred (attendance over Zoom could be arranged if necessary).
- Lecture and practice materials can be found through the navigation panel on the left and in the Moodle course.
- Homeworks are submitted and graded through Moodle.
- For questions, please use the Moodle forum.