Clustering-based Aggregations for Prediction in Event Streams
Spenrath, Y., Hassani, M., & Dongen, B. F. V. (2022). Clustering-based Aggregations for Prediction in Event Streams. CoRR, abs/2210.09738. https://doi.org/10.48550/arXiv.2210.09738
Scientific
Extracting and Pre-Processing Event Logs
Fahland, D. (2022). Extracting and Pre-Processing Event Logs. CoRR, abs/2211.04338. https://doi.org/10.48550/arXiv.2211.04338
Uncovering Complex Relations in Patient Pathways based on Statistics: the Impact of Clinical Actions
Koorn, J. J., Lu, X., Mannhardt, F., Leopold, H., & Reijers, H. A. (2022). Uncovering Complex Relations in Patient Pathways based on Statistics: the Impact of Clinical Actions. https://doi.org/10.24251/HICSS.2022.503 Abstract Process mining is a family of techniques that can aid healthcare organizations in improving their processes. Most existing process mining techniques do not provide insights Read More …
A Framework for Efficient Memory Utilization in Online Conformance Checking
Zaman, R., Hassani, M., & van Dongen, B. F. (2021). A Framework for Efficient Memory Utilization in Online Conformance Checking. arXiv.org. https://arxiv.org/pdf/2112.13640.pdf Abstract Conformance checking (CC) techniques of the process mining field gauge the conformance of the sequence of events in a case with respect to a business process model, which simply put is an Read More …
What Averages Do Not Tell – Predicting Real Life Processes with Sequential Deep Learning
Ketykó, I., Mannhardt, F., Hassani, M., & van Dongen, B. F. (2021). What Averages Do Not Tell – Predicting Real Life Processes with Sequential Deep Learning. CoRR, abs/2110.10225. https://arxiv.org/abs/2110.10225 Abstract Deep Learning is proven to be an effective tool for modeling sequential data as shown by the success in Natural Language, Computer Vision and Signal Read More …
Augmented Business Process Management Systems: A Research Manifesto
Dumas, M., Fournier, F., Limonad, L., Marrella, A., Montali, M., Rehse, J-R., Accorsi, R., Calvanese, D., Giacomo, G. D., Fahland, D., Gal, A., Rosa, M. L., Völzer, H., & Weber, I. (2022). Augmented Business Process Management Systems: A Research Manifesto. CoRR, abs/2201.12855. https://dblp.org/db/journals/corr/corr2201.html#abs-2201-12855
BPIC’2018: Mining Concept Drift in Performance Spectra of Processes
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
Using graph data structures for event logs
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 …
Log skeletons: a classification approach to process discovery
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 …
Framework for process discovery from sensor data
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 …
Trust and Privacy in Process Analytics
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
Multi-perspective process mining
Mannhardt, F. (2018). Multi-perspective process mining. Eindhoven: Technische Universiteit Eindhoven. ((Co-)promot.: Hajo Reijers, Wil van der Aalst & Massimiliano de Leoni).
Using behavioral context in process mining : exploration, preprocessing and analysis of event data
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)
Learning high-level process models from event data
Kalenkova, A.A. (2018). Learning high-level process models from event data. Eindhoven: Technische Universiteit Eindhoven. ((Co-)promot.: Wil van der Aalst & Irina Lomazova).