Sebastiaan (Bas) van Zelst does not work here anymore.
Seidel, A., Weske, M., Montali, M., Rivkin, A., Reichert, M., van der Werf, J. M. E. M., van der Aalst, W. M. P., Breitmayer, M., Liss, L., van Detten, J. N., Jalali, A., Khayatbashi, S., König, M., Lichtenstein, T., Rinderle-Ma, S., Weber, B., Soffer, P., Rossi, L., Calegari Garcia, D., Delgado, A., Dijkman, R., Winkler, Read More …
Bloemen, V., van Zelst, S., van der Aalst, W., van Dongen, B., & van de Pol, J. (Accepted/In press). Aligning observed and modelled behaviour by maximizing synchronous moves and using milestones. Information Systems, 103, [101456]. https://doi.org/10.1016/j.is.2019.101456 Abstract Given a process model and an event log, conformance checking aims to relate the two together, e.g. to Read More …
van Zelst, S. J., Mannhardt, F., de Leoni, M., & Koschmider, A. (2020). Event abstraction in process mining: literature review and taxonomy. Granular Computing, XX(XX). https://doi.org/10.1007/s41066-020-00226-2 Abstract The execution of processes in companies generates traces of event data, stored in the underlying information system(s), capturing the actual execution of the process. Analyzing event data, i.e., Read More …
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 …
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 …
van Zelst, S. J. (2019). Process mining with streaming data Eindhoven: Technische Universiteit Eindhoven
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 …
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 …
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 …
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 …
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.
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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 …
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 …
Tax, N., van Zelst, S.J. & Teinemaa, I. (2018). An experimental evaluation of the generalizing capabilities of process discovery techniques and black-box sequence models. In Palash Bera, Jens Gulden, Iris Reinhartz-Berger, Wided Guédria, Sérgio Guerreiro & Rainer Schmidt (Eds.), Enterprise, Business-Process and Information Systems Modeling (pp. 165-180). (Lecture Notes in Business Information Processing). Dordrecht: Springer Read More …
Sani, Mohammadreza Fani, van Zelst, Sebastiaan J. & van der Aalst, Wil M.P. (2018). Improving process discovery results by filtering outliers using conditional behavioural probabilities. Business Process Management Workshops – BPM 2017 International Workshops, Revised Papers (pp. 216-229). (Lecture Notes in Business Information Processing, No. 308). Berlin: Springer. Abstract Process discovery, one of the key Read More …
van Zelst, S.J., van Dongen, B.F. & van der Aalst, W.M.P. (2018). Event stream-based process discovery using abstract representations. Knowledge and Information Systems, 54(2), 407-435. Abstract The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery Read More …
van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P. && Verbeek, H.M.W. (2018). Discovering workflow nets using integer linear programming. Computing, 100(5), 529-556. Abstract Process mining is concerned with the analysis, understanding and improvement of business processes. Process discovery, i.e. discovering a process model based on an event log, is considered the most challenging Read More …
“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 …
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 …
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 Read More …
Article Scientific peer reviewed Arriagada-Benítez, M., Sepúlveda, M., Munoz-Gama, J. & Buijs, J.C.A.M. (2017). Strategies to automatically derive a process model from a configurable process model based on event data. Applied Sciences, 7(10):1023. Bolt, A., de Leoni, M. & van der Aalst, W.M.P. (2017). Process variant comparison: using event logs to detect differences in behavior Read More …
Article Scientific peer reviewed Van Der Aa, Han, Leopold, H. & Reijers, H.A. (2016). Dealing with behavioral ambiguity in textual process descriptions. Lecture notes in computer science, 9850, 271-288. Scopus. van der Aa, J.H., Reijers, H.A. & Vanderfeesten, I.T.P. (2016). Designing like a pro : the automated composition of workflow activities. Computers in Industry, 75, Read More …
Article Scientific peer reviewed Adriansyah, Arya, Munoz Gama, Jorge, Carmona, J., van Dongen, Boudewijn & van der Aalst, Wil (2015). Measuring precision of modeled behavior. Information Systems and e-Business Management, 13(1), 37-67. Claes, Jan, Vanderfeesten, Irene, Pinggera, J., Reijers, Hajo, Weber, B. & Poels, G. (2015). A visual analysis of the process of process modeling. Read More …
