Introduction to process mining
Xixi Lu Utrecht University
Event data are being unceasingly recorded during process executions, for example, when a patient is following a healthcare treatment trajectory, when sales orders are placed online and delivered, or when a mortgage loan application is submitted. Event data store information about how these processes are executed in real life and who is executing which tasks and when.
As a Data Science discipline, Process Mining equips data scientists with a set of powerful techniques and tools to analyze event data, uncover process behaviors, and drive process improvement.
In this course, we will focus on:
- Process Discovery, the automatic learning of process models using event data. We will study a few well-known discovery algorithms, such as the directly-follows-graph miner and the inductive miner, which enable us to automatically derive process models from event data.
- The event-data preprocessing techniques (such as creating views, filtering event logs, event abstraction, and label refinements) and their effect on the discovered models. We will study these techniques and use them to enhance the quality and accuracy of the discovered models.
In the practical sessions, you will have the opportunity to apply your knowledge using popular process mining tools and libraries. Through hands-on exercises with real-life event data, you will gain experience, improving your skills in preprocessing event data and discovering process models effectively.
About the lecturer:Dr. ir. Xixi Lu is an Assistant Professor at Utrecht University’s Information and Computing Sciences department. She received her Ph.D. in Computer Science at the Eindhoven University of Technology, the Netherlands, in 2018, where her dissertation was honored with the Best PhD Thesis Award by the IEEE Task Force on Process Mining. She has published in more than 40 peer-reviewed conferences (CAiSE, BPM, ICPM) and journals (InfoSys, BISE, DKE, TSC). She served as a senior PC member of several international conferences, including BPM and ICPM. She also served as a program chair of the Sixth International Conference on Process Mining (ICPM’24). Her research interests sit at the intersection of Process Mining and Artificial Intelligence. In particular, she focuses on designing methods and algorithms to analyze, predict, and optimize complex processes using event data, with applications spanning healthcare and auditing.
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