Sebastiaan (Bas) van Zelst

Bas is a PhD student within the PA group where his main research is in the area of process mining. More concretely, his research interests focus on analysing event streams without storing (too much) data.

Position: PhD Student
Room: MF 7.108
Tel (internal): 8687
Links: Personal home page
Google scholar page
Scopus page
ORCID page
DBLP page
TU/e employee page

Projects

  • RISE BPM - “Propelling Business Process Management by Research and Innovation Staff Exchange” Description RISE_BPM is the first favourably evaluated project proposal submitted by the University of Münster in cooperation with ERCIS partners within the Horizon 2020 EU funding programme. The RISE_BPM project is aimed at networking world-leading research institutions and corporate innovators to develop new horizons for Read More ...
  • DeLiBiDa - Desire Lines in Big Data Description The goal of process mining is to extract process-related information from event logs, e.g., to automatically discover a process model by observing events recorded by some information system. Despite recent advances in process mining there are still important challenges that need to be addressed. In particular with respect to Read More ...

Publications

  • Repairing outlier behaviour in event logs - Fani Sani, M., van Zelst, S. J., & van der Aalst, W. M. P. (2018). Repairing outlier behaviour in event logs. In W. Abramowicz, & A. Paschke (Eds.), Business Information Systems – 21st International Conference, BIS 2018, Proceedings (pp. 115-131). (Lecture Notes in Business Information Processing; Vol. 320). Cham: Springer. https://doi.org/10.1007/978-3-319-93931-5_9 Abstract One of the Read More ...
  • Computing alignments of event data and process models - van Zelst, S. J., Bolt, A., & van Dongen, B. F. (2018). Computing alignments of event data and process models. In M. Koutny, L. M. Kristensen, & W. Penczek (Eds.), Transactions on Petri Nets and Other Models of Concurrency XIII (pp. 1-26). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Read More ...
  • Process mining with streaming data - van Zelst, S. J. (2019). Process mining with streaming data Eindhoven: Technische Universiteit Eindhoven
  • On the application of sequential pattern mining primitives to process discovery: overview, outlook and opportunity identification - Hassani, M., van Zelst, S. J., & van der Aalst, W. M. P. (2019). On the application of sequential pattern mining primitives to process discovery: overview, outlook and opportunity identification. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(6), [e1315]. DOI: 10.1002/widm.1315 Abstract Sequential pattern mining (SPM) is a well-studied theme in data mining, in Read More ...
  • Online conformance checking: relating event streams to process models using prefix-alignments - van Zelst, S. J., Bolt Irondio, A. J., Hassani, M., van Dongen, B. F., & van der Aalst, W. M. P. (2019). Online conformance checking: relating event streams to process models using prefix-alignments. International Journal of Data Science and Analytics, 8(3), 269-284. DOI: 10.1007/s41060-017-0078-6 Abstract Companies often specify the intended behaviour of their business processes Read More ...
  • Online conformance checking using behavioural patterns - Burattin, Andrea, van Zelst, Sebastiaan J., Armas-Cervantes, Abel, van Dongen, Boudewijn F. & Carmona, Josep (2018). Online conformance checking using behavioural patterns. In Ingo Weber, Jan vom Brocke, Marco Montali & Mathias Weske (Eds.), Business Process Management – 16th International Conference, BPM 2018, Proceedings (pp. 250-267). (Lecture Notes in Computer Science (including subseries Lecture Notes Read More ...
  • Maximizing synchronization for aligning observed and modelled behaviour - Bloemen, Vincent, van Zelst, Sebastiaan J., van der Aalst, Wil M.P., van Dongen, Boudewijn F. & van de Pol, Jaco (2018). Maximizing synchronization for aligning observed and modelled behaviour. In Ingo Weber, Jan vom Brocke, Marco Montali & Mathias Weske (Eds.), Business Process Management – 16th International Conference, BPM 2018, Proceedings (pp. 233-249). (Lecture Notes Read More ...
  • RapidProM : mine your processes and not just your data - van der Aalst, W.M.P., Bolt Iriondo, A.J. & van Zelst, S.J. (2018). RapidProM : mine your processes and not just your data. In R. Klinkenberg & M. Hofmann (Eds.), RapidMiner : Data Mining Use Cases and Business Analytics Applications Chapman & Hall/CRC Press.
  • Repairing outlier behaviour in event logs - Fani Sani, Mohammadreza, van Zelst, Sebastiaan J. & van der Aalst, Wil M.P. (2018). Repairing outlier behaviour in event logs. In W. Abramowicz & A. Paschke (Eds.), Business Information Systems – 21st International Conference, BIS 2018, Proceedings (pp. 115-131). (Lecture Notes in Business Information Processing, No. 320). Cham: Springer. Abstract One of the main challenges Read More ...
  • Filtering spurious events from event streams of business processes - van Zelst, Sebastiaan J., Fani Sani, Mohammadreza, Ostovar, Alireza, Conforti, Raffaele & La Rosa, Marcello (2018). Filtering spurious events from event streams of business processes. Advanced Information Systems Engineering – 30th International Conference, CAiSE 2018, Proceedings (pp. 35-52). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Read More ...

Presentations

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