Niek Tax does not work here anymore.
Tax, N., Sidorova, N., Haakma, R., & van der Aalst, W. M. P. (2018). Mining process model descriptions of daily life through event abstraction. In S. Kapoor, R. Bhatia, & Y. Bi (Eds.), Intelligent Systems and Applications: Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2016 (pp. 83-104). (Studies in Computational Intelligence; Read More …
Tax, N., Sidorova, N., Haakma, R., & van der Aalst, W. M. P. (2018). Event abstraction for process mining using supervised learning techniques. In Y. Bi, S. Kapoor, & R. Bhatia (Eds.), Proceedings of the SAI Intelligent Systems Conference (IntelliSys 2016), 21-22 September 2016, London, United Kingdom (pp. 251-269). (Lecture Notes in Networks and Systems; Read More …
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 …
Tax, N. (2019). Mining insights from weakly-structured event data Eindhoven: Technische Universiteit Eindhoven
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 …
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 …
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 …
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 …
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 …
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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 …
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 …
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 …
Tax, N., Lu, X., Sidorova, N., Fahland, D. & van der Aalst, W.M.P. (2018). The imprecisions of precision measures in process mining. Information Processing Letters, 135, 1-8. Abstract In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been Read More …
Tax, N., Sidorova, N. & van der Aalst, W.M.P. (2019). Discovering more precise process models from event logs by filtering out chaotic activities. Journal of Intelligent Information Systems, 52(1), 107-139. DOI: 10.1007/s10844-018-0507-6 Abstract Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that 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 …
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 …
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 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 …
