Event Knowledge Graphs for Auditing: A Case Study

Klijn, E. L., Preuss, D., Imeri, L., Baumann, F., Mannhardt, F., & Fahland, D. (2024). Event Knowledge Graphs for Auditing: A Case Study. In J. De Smedt, & P. Soffer (Eds.), Process Mining Workshops – ICPM 2023 International Workshops, 2023, Revised Selected Papers (pp. 84-97). (Lecture Notes in Business Information Processing; Vol. 503 LNBIP). https://doi.org/10.1007/978-3-031-56107-8_7 Read More …

How well can large language models explain business processes?

Fahland, D., Fournier, F., Limonad, L., Skarbovsky, I., & Swevels, A. J. E. (2024). How well can large language models explain business processes? arXiv, abs/2401.12846. https://doi.org/10.48550/arXiv.2401.12846 Abstract Large Language Models (LLMs) are likely to play a prominent role in future AI-augmented business process management systems (ABPMSs) catering functionalities across all system lifecycle stages. One such Read More …

Implementing Object-Centric Event Data Models in Event Knowledge Graphs

Swevels, A., Fahland, D., & Montali, M. (2024). Implementing Object-Centric Event Data Models in Event Knowledge Graphs. In J. De Smedt, & P. Soffer (Eds.), Process Mining Workshops – ICPM 2023 International Workshops, 2023, Revised Selected Papers (pp. 431-443). (Lecture Notes in Business Information Processing; Vol. 503 LNBIP). https://doi.org/10.1007/978-3-031-56107-8_33 Abstract Recent advances in object-centric process Read More …

Multi-perspective Conformance Checking: Identifying and Understanding Patterns of Anomalous Behavior

Mozafari Mehr, A. S. (2024). Multi-perspective Conformance Checking: Identifying and Understanding Patterns of Anomalous Behavior. [Phd Thesis 1 (Research TU/e / Graduation TU/e), Mathematics and Computer Science]. Eindhoven University of Technology. Summary The problem of anomaly detection in business process executions has high level of complexity. On one hand, detecting deviating behavior requires considering various Read More …

Using Machine Learning Techniques to Support the Emergency Department

van Delft, R. A. J. J., & de Carvalho, R. M. (2022). Using Machine Learning Techniques to Support the Emergency Department. Computing and Informatics, 41(1), 154-171. https://doi.org/10.31577/CAI_2022_1_154 Abstract This research lays down foundations for a stronger presence of machine learning in the emergency department. Using machine learning to make predictions on a patient’s situation can Read More …

From predictions to recommendations: Tackling bottlenecks and overstaying in the Emergency Room through a sequence of Random Forests

Verdaasdonk, M. J. A., & de Carvalho, R. M. (2022). From predictions to recommendations: Tackling bottlenecks and overstaying in the Emergency Room through a sequence of Random Forests. Healthcare Analytics, 2, Article 100040. https://doi.org/10.1016/j.health.2022.100040 Abstract One of the goals to improve the quality of care in hospitals is to set a maximum of four hours Read More …

Domain engineering for customer experience management

Benzarti, I., Mili, H., Medeiros de Carvalho, R., & Leshob, A. (2022). Domain engineering for customer experience management. Innovations in Systems and Software Engineering, 18(1), 171-191. https://doi.org/10.1007/s11334-021-00426-2 Abstract Customer experience management (CXM) denotes a set of practices, processes, and tools, that aim at personalizing a customer’s interactions with a company around the customer’s needs and Read More …

An insight to nurse workload: predicting activities in the next shift and analyzing bedside alarms influence

de Carvalho, R. M., Nguyen, H., Heetveld, M., & Luime, J. (2022). An insight to nurse workload: predicting activities in the next shift and analyzing bedside alarms influence. In T. X. Bui (Ed.), Proceedings of the 55th Annual Hawaii International Conference on System Sciences, HICSS 2022 (pp. 4108-4117). IEEE Computer Society. Abstract The effects of Read More …

Predicting Patient Care Acuity: An LSTM Approach for Days-to-day Prediction

Bekelaar, J. W. R., Luime, J. J., & de Carvalho, R. M. (2023). Predicting Patient Care Acuity: An LSTM Approach for Days-to-day Prediction. In M. Montali, A. Senderovich, & M. Weidlich (Eds.), Process Mining Workshops – ICPM 2022 International Workshops, Revised Selected Papers (pp. 378-390). (Lecture Notes in Business Information Processing; Vol. 468 LNBIP). Springer. https://doi.org/10.1007/978-3-031-27815-0_28 Read More …

Identifying the Context of Data Usage to Diagnose Privacy Issues through Process Mining

Mehr, A. S. M., de Carvalho, R. M., & van Dongen, B. (2023). Identifying the Context of Data Usage to Diagnose Privacy Issues through Process Mining. Transactions on Data Privacy, 16(2), 123-151. http://www.tdp.cat/issues21/tdp.a456a22.pdf Abstract In recent years, data privacy issues are increasingly concerned by organisations and gov-ernments. Organisations often define a set of rules as Read More …

New Assistant Professor: Francesca Zerbato

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 …

Francesca Zerbato

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 …

Root-cause performance analysis in multiple process dimensions

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 …

Action-Evolution Petri Nets: a Framework for Modeling and Solving Dynamic Task Assignment Problems

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 …

Analytical Problem Solving Based on Causal, Correlational and Deductive Models

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 …

Scheduling a Real-World Photolithography Area with Constraint Programming

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 …

Combining Deep Reinforcement Learning with Search Heuristics for Solving Multi-Agent Path Finding in Segment-based Layouts

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 …

Data-driven Support of Coaches in Professional Cycling using Race Performance Prediction

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 …

Puck te Rietmole

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

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 Biemans

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 …

Irina Tentina

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 …

Can we Learn from Outliers? Unsupervised Optimization of Intelligent Vehicle Traffic Management Systems

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 …

Conformance checking of process event streams with constraints on data retention

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 …

Predicting Activities of Interest in the Remainder of Customer Journeys Under Online Settings

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 …

PrefixCDD: Effective Online Concept Drift Detection over Event Streams using Prefix Trees.

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 …

An Experiment on Transfer Learning for Suffix Prediction on Event Logs

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 …

Foundations of Data Analytics (2IAB1) 2023

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”)

Yvette van der Haas

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 …