On April 15th, Francesca Zerbato started working as an Assistant Professor in the PA group. Francesca will be working with Dirk Fahland on the design, development and evaluation of interactive tools and software artifacts that can support the real needs of human analysts when dealing with complex and knowledge-intensive tasks such as data sense-making. A Read More …
Position: UD Room: MF 7.119 Tel (internal): Links: External links: Google Scholar pageScopus pageTU/e page Francesca Zerbato received her Ph.D. from the Department of Computer Science at the University of Verona (Italy). Her thesis focused on the modeling of temporal aspects and data in business process models under the supervision of Prof. Carlo Combi. After Read More …
Description Reliable analysis of performance problems and their Root Causes (RCs) requires understanding if a case was delayed by other cases in employees’ or teams’ queues of the same process and/or by cases of other processes. The former is described by the multiple execution single object process dimension, and the latter by the single execution Read More …
Lo Bianco, R., Dijkman, R. M., Nuijten, W. P. M., & van Jaarsveld, W. L. (2023). Action-Evolution Petri Nets: a Framework for Modeling and Solving Dynamic Task Assignment Problems. In C. Di Francescomarino, A. Burattin, C. Janiesch, & S. Sadiq (Eds.), Business Process Management: 21st International Conference, BPM 2023, Utrecht, The Netherlands, September 11–15, 2023, Read More …
de Mast, J., Steiner, S., Nuijten, W. P. M., & Kapitan, D. (2023). Analytical Problem Solving Based on Causal, Correlational and Deductive Models. American Statistician, 77(1), 51-61. https://doi.org/10.1080/00031305.2021.2023633 Abstract Many approaches for solving problems in business and industry are based on analytics and statistical modeling. Analytical problem solving is driven by the modeling of relationships Read More …
Deenen, P. C., Nuijten, W. P. M., & Akcay, A. (2023). Scheduling a Real-World Photolithography Area with Constraint Programming. IEEE Transactions on Semiconductor Manufacturing, 36(4), 590-598. Article 10214506. https://doi.org/10.1109/TSM.2023.3304517 Abstract This paper studies the problem of scheduling machines in the photolithography area of a semiconductor manufacturing facility. The scheduling problem is characterized as an unrelated Read More …
Reijnen, R., Zhang, Y., Nuijten, W. P. M., Senaras, C., & Goldak, M. (2021). Combining Deep Reinforcement Learning with Search Heuristics for Solving Multi-Agent Path Finding in Segment-based Layouts. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI 2020) (pp. 2647-2654). Article 9308584 IEEE Press. https://doi.org/10.1109/SSCI47803.2020.9308584 Abstract A multi-agent path finding (MAPF) problem is concerned Read More …
Karetnikov, A., Nuijten, W., & Hassani, M. (2021). Data-driven Support of Coaches in Professional Cycling using Race Performance Prediction. In P. Pezarat-Correia, J. Vilas-Boas, & J. Cabri (Eds.), icSPORTS 2021 – Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support (pp. 43-53). SciTePress Digital Library. Abstract In individual sports, the judgment Read More …
On November 18th, 2023, January 1st, 2024 and April 1st, 2024, Bram Biemans, Igor Smit and Puck te Rietmole started working as PhD students in the PA group. Irina will be working with Wim Nuijten on different projects. A big welcome to Bram, Igor and Puck!
Puck obtained her M.Sc. degree at the Mathematics Department at Utrecht University (Netherlands). Her M.Sc. dissertation was carried out on the topic Stochastic Scheduling with a Single Sample, under the supervision of Prof. Dr. Marc Uetz from TU Twente. Currently, she is a Ph.D. student at Eindhoven University of Technology – Data Science Domain – Read More …
Igor Smit obtained his dual MSc. degree in Data Science in Engineering and Operations Management & Logistics cum laude at the Departments of Mathematics & Computer Science and Industrial Engineering & Innovation Sciences at Eindhoven University of Technology (Netherlands). His MSc. dissertation “Learning to Be Efficient and Fair for Collaborative Order Picking” was carried out Read More …
Bram obtained his M.Sc. degree Data Science in Business and Entrepreneurship at the Jheronimus Academy of Data Science (Den Bosch, Netherlands). His M.Sc. dissertation was carried out on the topic “Movement Prediction for Off-the-ball Football Players” under the supervision of Wim Nuijten. Currently, he is a Ph.D. student at Eindhoven University of Technology – Data Read More …
On March 1st, Irina Tentina started working as a PhD-TA student in the PA group. Irina will be working with Boudewijn van Dongen and Felix Mannhardt. A big welcome to Irina!
Irina obtained her M.Sc. degree at the Department of Informatics and Applied Mathematics at Saint-Petersburg National Research University of Information Technologies, Mechanics and Optics (Saint-Petersburg, Russia). Her M.Sc. dissertation was carried out on the topic “Developing Methods for Analyzing Business Processes with the use of R Language”. She obtained her master’s degree in 2018. During Read More …
Mertens, T., & Hassani, M. (2023). Can we Learn from Outliers? Unsupervised Optimization of Intelligent Vehicle Traffic Management Systems. In M.-R. Amini, S. Canu, A. Fischer, T. Guns, P. Kralj Novak, & G. Tsoumakas (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part VI Read More …
Zaman, R., Hassani, M., & van Dongen, B. F. (2023). Conformance checking of process event streams with constraints on data retention. Information Systems, 117, Article 102228. https://doi.org/10.1016/j.is.2023.102228 Abstract Conformance checking (CC) techniques in process mining determine the conformity of cases, by means of their event sequences, with respect to a business process model. Online conformance Read More …
Wolters, L., & Hassani, M. (2023). Predicting Activities of Interest in the Remainder of Customer Journeys Under Online Settings. In M. Montali, A. Senderovich, & M. Weidlich (Eds.), Process Mining Workshops – ICPM 2022 International Workshops, Revised Selected Papers (pp. 145-157). (Lecture Notes in Business Information Processing; Vol. 468 LNBIP). https://doi.org/10.1007/978-3-031-27815-0_11 Abstract Customer journey analysis Read More …
Huete, J., Qahtan, A. A., & Hassani, M. (2023). PrefixCDD: Effective Online Concept Drift Detection over Event Streams using Prefix Trees. In H. Shahriar, Y. Teranishi, A. Cuzzocrea, M. Sharmin, D. Towey, AKM. J. A. Majumder, H. Kashiwazaki, J.-J. Yang, M. Takemoto, N. Sakib, R. Banno, & S. I. Ahamed (Eds.), COMPSAC (pp. 328-333) https://doi.org/10.1109/COMPSAC57700.2023.00051 Read More …
van Luijken, M., Ketykó, I., & Mannhardt, F. (2024). An Experiment on Transfer Learning for Suffix Prediction on Event Logs. In J. De Weerdt, & L. Pufahl (Eds.), Business Process Management Workshops – BPM 2023 International Workshops, Utrecht, The Netherlands, September 11–15, 2023, Revised Selected Papers (pp. 31-43). (Lecture Notes in Business Information Processing; Vol. 492 LNBIP). Springer. Read More …
On January 30, we had the first workshop of the Eindhoven-Leuven-Aachen AI Triangle. Researchers from Eindhoven, Leuven and Aachen shared their current work in progress on Process Mining.
Learning goals Working with data data exploration statistical techniques data visualisation data mining data organization and data retrieval Programming (customizable, reproducible) Communication skills (visualisations, a poster and a pitch in the assignments) Systematic way to approach problems (“scientific method”)
On December 1st, Muskan Muskan started working as PhD student in the PA group. Muskan will work under the supervision of Felix Mannhardt on the Smart Journey Mining project. A big welcome to Muskan!
On December 1st, Yvette van der Haas started working as PhD student in the PA group. Yvette will work under the supervision of Renata Medeiros de Carvalho on a project in cooperation with the St. Antonius hospital. A big welcome to Yvette!
Position: PhD Student Room: MF 7.070 Tel (internal): Links: Projects External links: TU/e page Recent courses Recent presentations Recent projects Recent publications
Yvette obtained her M.Sc. Data Science degree at the Lulea Technical University (Sweden). Her M.Sc. dissertation was carried out on the topic “predicting a myocardial infarction (heart infarct) with prehospital text data” under the supervision of Saguna Saguna. Currently, she is a Ph.D. student at Eindhoven University of Technology – Data Science Domain – Process Read More …
Position: Guest researcher Room: MF 7.062 External links: Google scholar pageUnipd page
You’ve learned about process mining during your courses, but how much do you know about creating the event log for process mining? In business, creating the event log required for process mining is one of the most time-intensive, most complex parts of a process mining project. At Konekti, we’ve built a platform that simplifies and Read More …
On October 9th, Allmin Susaiyah started working as PostDoc in the PA group. Allmin will work under the supervision of Natalia Sidorova on the TACTICS project.
Position: PostDoc Room: MF 7.117 Links: PresentationsProjectsPublications External links: Google scholar pageScopus pageDBLP pageTU/e page Recent presentations Recent projects Recent publications
Process models are used to describe and reason about the execution of a process (e.g., package delivery) where a process instance (package), also called as a case, moves through the system. A case in a process is often subject to interaction with other cases and/or resources (e.g. deliverer), impacting the workflow. Event logs record which Read More …
Philips Healthcare produces an image-guided therapy system, called Azurion, that helps surgeons to execute complex procedures. In the past Master projects at Philips Healthcare, Master students have already developed a visual logfile analysis tool that has been making waves within our organization and conformance checking methods to address anti-patterns describing unwanted behavior. Now, we’re looking Read More …
Bakullari, B., Thoor, J. V., Fahland, D., & Aalst, W. M. P. v. d. (2023). The Interplay Between High-Level Problems and the Process Instances that Give Rise to Them. In BPM (Forum) (pp. 145-162) https://doi.org/10.1007/978-3-031-41623-1_9
Sommers, D., Menkovski, V., & Fahland, D. (2023). Supervised learning of process discovery techniques using graph neural networks. Information Systems, 115, Article 102209. https://doi.org/10.1016/j.is.2023.102209 Abstract Automatically discovering a process model from an event log is the prime problem in process mining. This task is so far approached as an unsupervised learning problem through graph synthesis Read More …
Coremans, B., Klomp, A., Rukmono, S. A., Krüger, J., Fahland, D., & Chaudron, M. R. V. (2023). Process Mining from Jira Issues at a Large Company. In International Conference on Software Maintenance and Evolution (ICSME) IEEE Press.
Swevels, A., Dijkman, R. M., & Fahland, D. (2023). Inferring Missing Entity Identifiers from Context Using Event Knowledge Graphs. In C. Di Francescomarino, A. Burattin, C. Janiesch, & S. Sadiq (Eds.), Business Process Management: 21st International Conference, BPM 2023, Utrecht, The Netherlands, September 11–15, 2023, Proceedings (pp. 180-197). (Lecture Notes in Computer Science (LNCS); Vol. Read More …
Jessen, U., Sroka, M., & Fahland, D. (2023). Chit-Chat or Deep Talk: Prompt Engineering for Process Mining. CoRR, abs/2307.09909. https://doi.org/10.48550/arXiv.2307.09909
