Wil van der Aalst does not work here anymore.
Dixit, Prabhakar M., Suriadi, Suriadi, Andrews, Robert, Wynn, Moe T., ter Hofstede, Arthur H.M., Buijs, Joos C.A.M. & van der Aalst, Wil M.P. (2018). Detection and interactive repair of event ordering imperfection in process logs. Advanced Information Systems Engineering – 30th International Conference, CAiSE 2018, Proceedings (pp. 274-290). (Lecture Notes in Computer Science (including subseries Read More …
Yong, Jianming, Fortino, Giancarlo, Shen, Weiming, Yang, Yun, Chao, Kuo Ming & Van Der Aalst, Wil (2018). Special Issue on Service-Oriented Collaborative Computing and Applications. IEEE Transactions on Services Computing, 11(2), 277-278. Abstract The papers in this special issue focus on the research and development of service-oriented collaborative computing technologies and their applications to the Read More …
Heinzl, Armin, van der Aalst, Wil & Bichler, Martin (2018). Why the community should care about technology-centric journal rankings. Business & Information Systems Engineering : The International Journal of WIRTSCHAFTSINFORMATIK, 60(2), 91-93.
van der Aalst, Wil M.P., Ignatov, Dmitry I., Khachay, Michael, Kuznetsov, Sergei O., Lempitsky, Victor, Lomazova, Irina A., Loukachevitch, Natalia, Napoli, Amedeo, Panchenko, Alexander, Pardalos, Panos M., Savchenko, Andrey V. & Wasserman, Stanley (2018). Preface. Lecture Notes in Computer Science, 10716, V-VII.
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 …
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 …
Mendling, J., Weber, I., van der Aalst, W.M.P., vom Brocke, J., Cabanillas, C., Daniel, F., Debois, S., Di Ciccio, C., Dumas, M., Dustdar, S., Gal, A., García-Bañuelos, L., Governatori, G., Hull, R., La Rosa, Marcello, Leopold, Henrik, Leymann, Frank, Recker, Jan, Reichert, Manfred, Reijers, H.A., Rinderlema, Stefanie, Solti, Andreas, Rosemann, Michael, Schulte, Stefan, Singh, Munindar Read More …
Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P. & Toussaint, P. (2018). Guided Process Discovery : A Pattern-based Approach. Information Systems, 76, 1-18 Abstract Process mining techniques analyze processes based on events stored in event logs. Yet, low-level events recorded by information systems may not directly match high-level activities that make sense Read More …
Bolt, A., de Leoni, M. & van der Aalst, W.M.P. (2018). Process variant comparison: using event logs to detect differences in behavior and business rules. Information Systems, 74(1), 53-66. Abstract This paper addresses the problem of comparing different variants of the same process. We aim to detect relevant differences between processes based on what was Read More …
Alizadeh, M., Lu, X., Fahland, D., Zannone, N. & van der Aalst, W.M.P. (2018). Linking data and process perspectives for conformance analysis. Computers and Security, 73, 172-193. Abstract The detection of data breaches has become a major challenge for most organizations. The problem lies in the fact that organizations often lack proper mechanisms to control Read More …
van der Aalst, Wil (2018). Spreadsheets for business process management : Using process mining to deal with “events” rather than “numbers”?. Business Process Management Journal, 24(1), 105-127. Abstract Purpose: Process mining provides a generic collection of techniques to turn event data into valuable insights, improvement ideas, predictions, and recommendations. This paper uses spreadsheets as a Read More …
Process Mining in Logistics is a joint project of the Data Science Center Eindhoven and Vanderlande industries. Description Logistics processes are notoriously difficult to design, analyze, and to improve. Where classical processes are scoped around the processing of information associated to a specific unique case, logistics deals with physical objects that are grouped and processed 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 …
Data Science Center Eindhoven Description Recent technological and societal changes led to an explosion of digitally available data. Exploiting the available data to its fullest extent, in order to improve decision making, increase productivity, and deepen our understanding of scientific questions, is one of today’s key challenges. Data science is an emerging area that aims 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 …
Prof.dr.ir. Wil van der Aalst is a full professor of the Process and Data Science (PADS) group at the RWTH in Aachen (Germany) and a part-time professor in the PA group. His personal research interests include process mining, business process management, workflow management, Petri nets, process modeling, and process analysis. Position: HGL Room: MF 7.064 Read More …
CORE – Consistently Optimised REsilient secure global supply-chains Description The CORE project aims to produce cost effective, fast and robust solutions for worldwide Global Supply Chain system. The project will implement an ecosystem where interoperability, security, resilience and real-time are optimized. The role of the AIS group in the project is to employ process mining 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 …
