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

BPM Runner-Up Test-of-Time Award at BPM 2023 for Andreas Meyer, Luise Pufahl, Dirk Fahland and Mathias Weske - 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“.
Dirk Fahland received the “Best Teacher Award 2022” from Pattern - On May 25th, Dirk Fahland received from Pattern the “Best Teacher Award 2022” in the Bachelor Data Science major at Eindhoven University of Technology and Tilburg University for his Data Challenge course. Congratulations to Dirk!
Best Paper award at ICPM 2021 for Dominique Sommers - Dominique Sommers, Vlado Menkovski, and Dirk Fahland have won the Best Paper award at ICPM 2021 with their paper “Process Discovery using Graph Neural Networks“. Congratulations to Dominique, Vlado, and Dirk!
Test of Time award at BPM 2021 for Dirk Fahland - Dirk Fahland won the Test of Time award for the paper “Repairing Process Models to Reflect Reality” (co-authored by Wil van der Aalst, see also the journal version of this paper). Congratulations to Dirk!
Best Paper award at ICPM 2020 for Zahra Toosinezhad - Zahra Toosinezhad, Dirk Fahland, and Wil van der Aalst have won the Best Paper award at ICPM with her paper “Detecting System-Level Behavior Leading To Dynamic Bottlenecks“. Congratulations to Zahra , Dirk, and Wil!
Best PhD. Dissertation award at ICPM 2020 for Xixi Lu - Xixi Lu, a former PhD student of our group, has won the Best PhD. Dissertation award with her thesis “Using behavioral context in process mining: exploration, preprocessing and analysis of event data“. Her promotor was Wil van der Aalst, and Dirk Fahland was one of her copromotors.

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

Process Mining for Artifact-Centric Processes - Download as PDF

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

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 ...
Aggregating Event Knowledge Graphs for Task Analysis - 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 ...
Exploring Task Execution Patterns in Event Graphs - 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 ...
Special issue: BPM 2020 Selected Papers in Foundations and Engineering - 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
Process tree discovery using a probabilistic inductive miner - 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 ...