Eva Klijn

Position: PhD TA
Room: MF 7.062
Tel (internal): 3173
Links: Courses
Presentations
Projects
Publications
External links: TU/e page

Recent courses

  • 2AMI20 Advanced Process Mining - Understanding and predicting behavior of people and machines in a shared setting (task, project, factory, process, organization) is central to Data Science and Artificial Intelligence. Actions of people and machines can be recorded as discrete events in event sequences (logs), event databases (tables, graphs), and real-time event streams. Learning behavioral models of discrete event data Read More ...
  • 2IAB0 Data analytics for engineers - Learning goals Students gain insight in basic techniques for processing large amounts of data in an efficient, reliable, and consistent way. Students develop skills in understanding, interpreting, and documenting data and information in the context of realistic scenarios. Students get understanding of the data life cycle and develop skills for structuring their solutions of practical Read More ...

Recent presentations

Recent projects

Recent publications

  • Classifying and Detecting Task Executions and Routines in Processes Using Event Graphs - Klijn, E. L., Mannhardt, F., & Fahland, D. (2021). Classifying and Detecting Task Executions and Routines in Processes Using Event Graphs. In BPM (Forum) (pp. 212-229) https://doi.org/10.5281/zenodo.5091610, https://doi.org/10.1007/978-3-030-85440-9_13 Abstract Business process management organizes work into several interrelated “units of work”, fundamentally conceptualized as a task. The classical concept of a task as a single step Read More ...
  • Identifying and reducing errors in remaining time prediction due to inter-case dynamics - Klijn, E. L., & Fahland, D. (2020). Identifying and reducing errors in remaining time prediction due to inter-case dynamics. In B. van Dongen, M. Montali, & M. T. Wynn (Eds.), Proceedings – 2020 2nd International Conference on Process Mining, ICPM 2020 (pp. 25-32). [9229927] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICPM49681.2020.00015 Abstract Remaining time prediction Read More ...

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