Tariq, H., & Hassani, M. (2023). Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions. arXiv.org. https://doi.org/10.48550/arXiv.2309.13662
Fahland, D., Montali, M., Lebherz, J., van der Aalst, W. M. P., van Asseldonk, M., Blank, P., Bosmans, L., Brenscheidt, M., Di Ciccio, C., Delgado, A., Calegari, D., Peeperkorn, J., Verbeek, E., Vugs, L., & Wynn, M. T. (2024). Towards a Simple and Extensible Standard for Object-Centric Event Data (OCED) – Core Model, Design Space, Read More …
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 …
