Niek Tax

Niek is a PhD student within the PA group where his main research is in the area of process mining. More concretely, his research interests include seasonality detection, deviation detection, predictions and recommendations based on process mining techniques.

Position: PhD Student
Room: MF 7.108
Tel (internal): 8965
Links: Personal home page
Google scholar page
Scopus page (2nd Scopus 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 ...
  • Philips Flagship - Description The Data Science Centre Eindhoven (DSC/e) is TU/e’s response to the growing volume and importance of data and the need for data & process scientists (http://www.tue.nl/dsce/). The DSC/e has recently started a long-term strategic cooperation with Philips Research Eindhoven on three topics: data science, health and lighting. As a first concrete action, 70 PhD Read More ...

Publications

  • Evaluating conformance measures in process mining using conformance propositions - Syring, A. F., Tax, N., & van der Aalst, W. M. P. (2019). Evaluating conformance measures in process mining using conformance propositions. In M. Koutny, L. Pomello, & L. M. Kristensen (Eds.), Transactions on Petri Nets and Other Models of Concurrency XIV (pp. 192-221). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Read More ...
  • Mining insights from weakly-structured event data - Tax, N. (2019). Mining insights from weakly-structured event data Eindhoven: Technische Universiteit Eindhoven
  • Mining local process models and their correlations - Genga, L., Tax, N., & Zannone, N. (2019). Mining local process models and their correlations. In M. van Keulen, P. Ceravolo, & K. Stoffel (Eds.), Data-Driven Process Discovery and Analysis – 7th IFIP WG 2.6 International Symposium, SIMPDA 2017, Revised Selected Papers (pp. 65-88). (Lecture Notes in Business Information Processing; Vol. 340). Cham: Springer. DOI: Read More ...
  • Mining local process models with constraints efficiently: applications to the analysis of smart home data - Tax, N., Sidorova, N., Haakma, R., & van der Aalst, W. M. P. (2018). Mining local process models with constraints efficiently: applications to the analysis of smart home data. In Proceedings of the 14th International Conference on Intelligent Environments (IE) (pp. 56-63). [8595032] Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/IE.2018.00016 Abstract Sequential pattern Read More ...
  • Generating time-based label refinements to discover more precise process models - Tax, N., Alasgarov, E. E., Sidorova, N., Haakma, R., & van der Aalst, W. M. P. (2019). Generating time-based label refinements to discover more precise process models. Journal of Ambient Intelligence and Smart Environments, 11(2), 165-182. DOI: 10.3233/AIS-190519 Abstract Process mining is a research field focused on the analysis of event data with the aim Read More ...
  • Alarm-based prescriptive process monitoring - Teinemaa, Irene, Tax, Niek, de Leoni, Massimiliano, Dumas, Marlon & Maggi, Fabrizio Maria (2018). Alarm-based prescriptive process monitoring. In Ingo Weber, Jan vom Brocke, Marco Montali & Mathias Weske (Eds.), Business Process Management Forum – BPM Forum 2018, Proceedings (pp. 91-107). (Lecture Notes in Business Information Processing, No. 329). Springer. Abstract Predictive process monitoring is Read More ...
  • Indulpet miner : combining discovery algorithms - Leemans, Sander J.J., Tax, Niek & ter Hofstede, Arthur H.M. (2018). Indulpet miner : combining discovery algorithms. In Dumitru Roman, Henderik A. Proper, Robert Meersman, Hervé Panetto, Christophe Debruyne & Claudio Agostino Ardagna (Eds.), On the Move to Meaningful Internet Systems. OTM 2018 Conferences – Confederated International Conferences (pp. 97-115). (Lecture Notes in Computer Science Read More ...
  • Local process model discovery : bringing petri nets to the pattern mining world - Tax, Niek, Sidorova, Natalia, van der Aalst, Wil M.P. & Haakma, Reinder (2018). Local process model discovery : bringing petri nets to the pattern mining world. In V. Khohamenko & O.H. Roux (Eds.), Application and Theory of Petri Nets and Concurrency (pp. 374-384). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence Read More ...
  • An experimental evaluation of the generalizing capabilities of process discovery techniques and black-box sequence models - 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 ...
  • Interest-driven discovery of local process models - Tax, Niek, Dalmas, Benjamin, Sidorova, Natalia, van der Aalst, Wil M.P. & Norre, Sylvie (2018). Interest-driven discovery of local process models. Information Systems, 77, 105-117. Abstract Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes. Traditional LPM discovery aims to generate a collection of process Read More ...

Presentations

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