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 …

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 …

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 …

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”)

Fine-tuning Pretrained LLMs for Online Anomaly Detection in Customer Journeys of De Volksbank

Background Customer journey thinking is getting more and more established in companies. Customers are getting more volatile with higher expectations and competition is fierce. With customer journey analytics it is possible to use a personal approach on a large dataset of customer behaviour and customer experience. In the field of customer journey analytics multiple well-known Read More …

Speed up data engineering for process mining in practice

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 …

Interpreting workflow deviations for real-life case studies

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 …

Enhancing a visual analysis tool with conformance checking based on anti-pattern analysis at Philips Healthcare

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 …

Supervised learning of process discovery techniques using graph neural networks

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 …

Inferring Missing Entity Identifiers from Context Using Event Knowledge Graphs

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 …

AI-augmented Business Process Management Systems: A Research Manifesto

Dumas, M., Fournier, F., Limonad, L., Marrella, A., Montali, M., Rehse, J. R., Accorsi, R., Calvanese, D., De Giacomo, G., Fahland, D., Gal, A., La Rosa, M., Völzer, H., & Weber, I. (2023). AI-augmented Business Process Management Systems: A Research Manifesto. ACM Transactions on Management Information Systems, 14(1), Article 11. https://doi.org/10.1145/3576047 Abstract AI-augmented Business Process Read More …

Seminar Process Analytics (2IMI00) 2023

This seminar prepares students for their Master project. By studying recent literature, we discuss and identify how to develop a research question, select the right research method, and plan and conduct the right evaluation. In addition, students get in touch with recent and ongoing research and practical application in the area of Processes and Information Read More …

Foundations of Process Mining (2AMI10) 2023

Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based Read More …

Data analytics for engineers (2IAB0) 2023

Learning goals Students gain insight in basic techniques for processing large amounts of data in an efficient, reliable, and consistent way. Students develop skills in understanding, interpreting, and documenting data and information in the context of realistic scenarios. Students get understanding of the data life cycle and develop skills for structuring their solutions of practical Read More …

Data Challenge 3 (JBG060) 2023

The objective of the Data Challenge courses is to teach students how to perform large-scale data-driven analyses themselves, combining the technical skills acquired earlier in the Data Science program with insights gained in methodological courses. Data Challenge 3 is the final course in this series and shall familiarize students with the skills of designing and Read More …