Bachelor Thesis - Localized Conformance and Performance Analysis Based on Event Data: Diagnosing Individual Places
Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). Various process modeling notations are available and many can be expressed in terms of Petri nets (after some abstraction). Hence a process model can be viewed as a set of places. Most approach try to analyze the performance of the process as a whole. The idea of this bachelor project would be to take a process model and analyze individual places using event data. Conformance and performance can be analyzed very locally and reveal trends, etc. The goal is to develop a ProM plug-in taking a process model and event logs as input. Real-life data sets will be provided to test the approach.
Good programming skills and knowledge of basic computer science concepts
- Coursera - Process Mining - Data Science in Action
- Process Mining
- IEEE 1849-2016 XES Standard
- ProM Tools
- Springer Book - Process Mining - Data Science in Action
Prof. Wil van der Aalst
For more Information
Send an e-mail to Prof. Wil van der Aalst. Make sure to include some information about your background and scores for completed courses.