Denisov, V. V., Belkina, E., & Fahland, D. (2018). BPIC’2018: Mining Concept Drift in Performance Spectra of Processes. (BPI Challenge 2018). https://doi.org/10.4121/uuid:3301445f-95e8-4ff0-981f1f204972
Esser, S., & Fahland, D. (2019). Using graph data structures for event logs. https://doi.org/10.5281/zenodo.3333831 Abstract Process mining as described in by Wil van der Aalst in is a combination of data mining and business process management to a new discipline. The general purpose of process mining is to derive process insights from event data captured Read More …
Verbeek, H. M. W., & Medeiros de Carvalho, R. (2018). Log skeletons: a classification approach to process discovery. arXiv.org. http://arxiv.org/abs/1806.08247 Abstract To test the effectiveness of process discovery algorithms, a Process Discovery Contest (PDC) has been set up. This PDC uses a classification approach to measure this effectiveness: The better the discovered model can classify Read More …
Koschmider, A., Janssen, D., & Mannhardt, F. (2020). Framework for process discovery from sensor data. CEUR Workshop Proceedings, 2628, 32-38. Abstract Process mining can give valuable insights into how real-life activities are performed when extracting meaningful activities instances from raw sensor events. However, in many cases, the event data generated during the execution of a Read More …
Mannhardt, F., Koschmider, A., Biermann, L., Lange, J., Tschorsch, F., & Wynn, M. (2020). Trust and Privacy in Process Analytics. Enterprise Modelling and Information Systems Architectures (EMISAJ), 15(8). https://doi.org/10.18417/emisa.15.8
Mannhardt, F. (2018). Multi-perspective process mining. Eindhoven: Technische Universiteit Eindhoven. ((Co-)promot.: Hajo Reijers, Wil van der Aalst & Massimiliano de Leoni).
Lu, X. (2018). Using behavioral context in process mining : exploration, preprocessing and analysis of event data. Eindhoven: Technische Universiteit Eindhoven. ((Co-)promot.: Wil van der Aalst, Dirk Fahland & Nicola Zannone)
Kalenkova, A.A. (2018). Learning high-level process models from event data. Eindhoven: Technische Universiteit Eindhoven. ((Co-)promot.: Wil van der Aalst & Irina Lomazova).
