Dirk Fahland

Dirk is Associate Professor (UHD) in the PA group. He completed his PhD with summa cum laude at Humboldt-Univeristät zu Berlin and Eindhoven University of Technology in 2010. His research interests include distributed processes and systems built from distributed components for which he investigates modeling systems (using process modeling languages, Petri nets, or scenario-based techniques), analyzing systems for errors or misconformances (through verification or simulation), and process mining/specification mining techniques for discovering system models from event logs. He particularly focuses on distributed system with multi-instance characteristics and their synchronizing and interacting behaviors. Dirk published his research results in over 40 articles at international conferences and journals and implemented them in a number of software tools.

Position: UD
Room: MF 7.066
Tel (internal): 4804
Links: Courses
External assignments
Presentations
Projects
Publications
External links: Personal home page
Google scholar page
Scopus page
DBLP page
TU/e page

Awards

Recent courses

  • Responsible Data Challenge (2AMR10) 2023 - Only for BDMA students
  • Professional portfolio (2IMR10) 2023 -
  • Internship (2AMC10) 2023 -
  • 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 ...
  • Advanced Process Mining (2AMI20) 2023 - 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 ...

Recent assignments

  • Develop a Behavioral Event Data Query Language - Query languages are essential for exploring, working with data and directly answering questions from data. SQL is the prime example for answering questions on relational data. Behavioral data is recorded in the form of events with timestamps. Various techniques such as Process Mining use the data in the form of event logs to aggregate and Read More ...
  • Process Discovery using Generative Adversarial Neural Networks - Process Discovery is an unsupervised learning problem with the task of discovering a graph-based model from sequences (or graphs) of event data that describes the data best. Generative Adversarial Neural Networks (GANNs) are a type of neural networks used to learn structures in an unsupervised fashion. The objective of this project is to explore the Read More ...
  • Process Mining on Event Graph Databases (multiple projects) - Process mining assumes event data to be stored in an event log, which is technically either a relational table (attributes as columns) or a stream of events (attribute value pairs). Recently, we developed a new technique to store event data in a Graph database such as Neo4j. This allows to do process mining over various Read More ...
  • Mining processes, social networks, and queues (multiple projects) - A recent visual analytics technique called the “Performance Spectrum” https://github.com/processmining-in-logistics/psm allows us to gain more fine-grained insights into performance behavior and changes over time. A TU/e Master student showed that it is possible to mine synchronization of cases from the performance spectrum data showing that the behavior of a case depends on the mechanisms and Read More ...
  • Efficient unsupervised event context detection - for event log clustering, outlier detection, and pre-processing. We recently developed a technique to detect the context of events from an event log in an efficient way through sub-graph matching. This allows to identify events and parts of event logs which are similar or different to each other, allowing to cluster traces, detect outliers, and Read More ...
  • Smart event log pre-processing - The quality of process mining results highly depends on the quality of the input data where noise, infrequent behaviors, log incompleteness or many different variants undercut the assumptions of process discovery algorithms, and lead to low-quality results. ProM provides numerous event log pre-processing and filtering options, but they require expert knowledge to understand when which Read More ...

Recent external assignments

  • 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 ...
  • 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 ...
  • Understanding the value of Event Knowledge Graphs when applied in the Product Configuration Change Process - This Master project is offered by the Configuration Management department of ASML and was developed together with EAISI (Eindhoven Artificial Intelligence Systems Institute, https://eaisi.tue.nl/). Background information Department: Configuration Management If the journey regarding overlapping changes (multiple changes impacting the same item within the same timeframe) has taught is one thing, it is that the analysis Read More ...
  • Develop a Query Language for Event Data of Many Interacting Processes - Hoekenrode 3, Amsterdam, Netherlands Region: EMEA – Europe, Middle East and Africa Company Description ServiceNow is making the world of work, work better for people. Our cloud‑based platform and solutions deliver digital workflows that create great experiences and unlock productivity for employees and the enterprise. We’re growing fast, innovating faster, and making an impact on Read More ...
  • Process Mining for “Thinking Assistants” in Logistics - Summary In the context of the “Process Mining in Logistics” research project between Vanderlande Industries, we are offering multiple Master projects aimed at laying the foundations for a “Thinking Assistant” for large-scale material handling systems.  Such a “Thinking Assistant” shall support engineers and operators in faster identifying problems and root-causes, predicting possible problems, and proposing Read More ...
  • Advanced Process Mining techniques in Practice (several Master projects with ProcessGold) - ProcessGold is a software supplier bringing together Process Mining and Business Intelligence, driven by highly skilled ICT entrepreneurs and backed by a wealth of experience. ProcessGold recently released a new Process Mining platform, the ProcessGold Enterprise Platform, that combines data extraction, process mining techniques, and visual analytics in order to produce dynamic visual reports which Read More ...

Recent presentations

Recent projects

  • AutoTwin - 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 ...
  • Process Mining in Logistics - 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 ...

Recent publications

  • Multi-Perspective Concept Drift Detection: Including the Actor Perspective - Klijn, E. L., Mannhardt, F., & Fahland, D. (2024). Accepted at CAiSE’24. To appear. Won the Best Paper Award at CAiSE’24.
  • 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 ...
  • The Interplay Between High-Level Problems and the Process Instances that Give Rise to Them - 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
  • 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 ...
  • Process Mining from Jira Issues at a Large Company - 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.
  • 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 ...
  • Chit-Chat or Deep Talk: Prompt Engineering for Process Mining - 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
  • 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 ...

 

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