Project: Process model discovery and visual analytics
Description
Process discovery is often done automatically using process discovery algorithms, such as the Probabilistic Inductive Miner [2]. However, the quality of the discovered models depends heavily on the quality of the data and how the data is prepared and selected. This is hard to do automatically. On the other hand, the user often has a lot of domain knowledge on the process that cannot be found in the data. We want to investigate how we can, using visual analytics and the combination of data and user’s domain knowledge, enable the user to investigate the data set (event log) characteristics to get build better models using a combination of visualization, interaction, and automated methods. Combination of data and user’s domain knowledge would include, but not be limited to the following dimensions:
- Structure of the event log: Which variations and which activities should we use?
- Process drift: On which period should we discover the process?
Details
- Student
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Susan van Ewijk
- Supervisor
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Stef van den Elzen
- Secondary supervisor
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DSDirk Fahland (PA) + Roeland Scheepens (UiPath)
- External location
- UiPath
- Link
- Thesis