The Study & Career Orientation Program is intended to support the students in a structured way in their decision-making process towards graduation with a research group of their preference and to prepare students for making choices regarding their future career. The program is mandatory for all students in the DSAI, CSE, IST and ES programs Read More …
Objectives At the end of the Preparation Phase the student is able to (context [C], research question [Q] and project preparation [P]): [C1] summarize and discuss the problem context, domain-knowledge, and the requirements these set [C2] demonstrate an understanding of and discuss the relevant state-of-the-art in research and other prior work in academia (and the organization). [Q1] formulate one or more research questions relevant with respect to the given context and state-of-the-art Read More …
Objectives Non Bachelor Data Science wanting to register for this course should reach out to program management via mcs.academic.advisor.bds@tue.nl for formal approval After taking this course students should be able to independently: recognize the phases of data analytics research and divide the research process in these phases familiarize themselves with techniques to understand a complex dataset Read More …
Objectives Acquire state-of-the-art scientific knowledge of a particular topic in the information systems domain. Typical topics are process modeling, workflow management, process mining, web services, service oriented architectures, language transformations, etc. Content People interested in the ‘process side’ of information systems can take the course ‘Capita selecta architecture of information systems’. This course will be Read More …
Verbeek, E., & Fahland, D. (2023). Generating Event Logs with CPN IDE. In Doctoral Consortium and Demo Track 2023 at the International Conference on Process Mining, ICPM-DCDT 2023 (CEUR Workshop Proceedings; Vol. 3648). Abstract This extended abstract introduces the event log generation facility of CPN IDE. CPN IDE has replaced CPN Tools as a tool Read More …
Klijn, E. L., Mannhardt, F., & Fahland, D. (2024). Multi-perspective Concept Drift Detection: Including the Actor Perspective. In G. Guizzardi, F. Santoro, H. Mouratidis, & P. Soffer (Eds.), Advanced Information Systems Engineering – 36th International Conference, CAiSE 2024, Proceedings (pp. 141-157). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Read More …
Eva Klijn, Felix Mannhardt and Dirk Fahland have received the Best Paper Award at CAiSE’24 for their paper titled “Multi-Perspective Concept Drift Detection: Including the Actor Perspective“. Congratulations to Eva, Felix and Dirk!
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 …
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 …
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 …
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 …
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 …
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
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 …
Andreas Meyer, Luise Pufahl, Dirk Fahland and Mathias Weske have received the BPM Runner-Up Test-of-Time Award at BPM 2023 for their paper titled “Modeling and Enacting Complex Data Dependencies in Business Processes“.
Only for BDMA students
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 …
Many real-life phenomena studied with Data Science methods unfold over time. They often involve many people, objects, agents, machines, entities, etc. that interact with each other while distributed in time and space. Such dynamics are called processes and are present everywhere: in software systems medical treatments, logistics systems, manufacturing, and even entire organizations. Process mining Read More …
Klijn, E. L., Mannhardt, F., & Fahland, D. (2023). Aggregating Event Knowledge Graphs for Task Analysis. In M. Montali, A. Senderovich, & M. Weidlich (Eds.), Process Mining Workshops – ICPM 2022 International Workshops, Revised Selected Papers (pp. 493-505). (Lecture Notes in Business Information Processing; Vol. 468 LNBIP). Springer. https://doi.org/10.1007/978-3-031-27815-0_36 Abstract Aggregation of event data is a Read More …
Description Reliable analysis of performance problems and their root causes requires data of a high quality to understand how cases were handed from one employee to another and how they were processed in queues. However, the recorded event data is often of lower quality, e.g., information about queues is missing. The objective of this project Read More …
Klijn, E. L., Mannhardt, F., & Fahland, D. (2021). Exploring Task Execution Patterns in Event Graphs. In M. Jans, G. Janssenswillen, A. Kalenkova , & F. M. Maggi (Eds.), ICPM 2021 Doctoral Consortium and Demo Track 2021: Proceedings of the ICPM Doctoral Consortium and Demo Track 2021 co-located with 10th International Conference on Process Mining Read More …
Fahland, D., Ghidini, C., Dumas, M., & Reichert, M. (2022). Special issue: BPM 2020 Selected Papers in Foundations and Engineering. Information Systems, 109, [102093]. https://doi.org/10.1016/j.is.2022.102093
Scheepens, R. J., Brons, D., & Fahland, D. (2022). Process tree discovery using a probabilistic inductive miner. (Patent No. US11500756B2). https://patents.google.com/patent/US20220075705A1/en Abstract Systems and methods for generating a process tree of a process are provided. An event log of the process is received. It is determined whether a base case applies to the event log Read More …
Fahland, D. (2022). Process Mining over Multiple Behavioral Dimensions with Event Knowledge Graphs. In Process Mining Handbook (pp. 274-319). (Lecture Notes in Business Information Processing; Vol. 448). https://doi.org/10.1007/978-3-031-08848-3_9 Abstract Classical process mining relies on the notion of a unique case identifier, which is used to partition event data into independent sequences of events. In this Read More …
Fahland, D. (2022). Multi-dimensional Process Analysis. In C. Di Ciccio, R. Dijkman, A. del Río Ortega, & S. Rinderle-Ma (Eds.), Business Process Management – 20th International Conference, BPM 2022, Proceedings: Lecture Notes in Computer Science (Vol. 13420, pp. 27-33). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Read More …
Fahland, D. (2022). Extracting and Pre-Processing Event Logs. CoRR, abs/2211.04338. https://doi.org/10.48550/arXiv.2211.04338
Brunings, M., Fahland, D., & Verbeek, E. (2022). Discover Context-Rich Local Process Models (Extended Abstract). In M. Hassani, A. Koschmider, M. Comuzzi, F. M. Maggi, & L. Pufahl (Eds.), ICPM 2022 Doctoral Consortium and Demo Track 2022: Proceedings of the ICPM Doctoral Consortium and Demo Track 2022 (ICPM-D 2022), Bolzano, Italy, October, 2022 (pp. 100-103). Read More …
Brunings, M., Fahland, D., & van Dongen, B. (2022). Defining Meaningful Local Process Models. In M. Koutny, F. Kordon, & D. Moldt (Eds.), Transactions on Petri Nets and Other Models of Concurrency XVI (pp. 24-48). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13220 LNCS). Read More …
Description The AutoTwin project addresses the technological shortcoming and economic liability of the development and usage of digital twins that are accepted as the accelerator and enabler of Circular Economy in businesses and production by conduction research in 3 areas: introducing a breakthrough method for automated process-aware discovery towards autonomous Digital Twins generation, to support Read More …
Only for BDMA students
