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

  • 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

  • 2AMR10 Responsible Data Challenge - Only for BDMA students. Links 2AMR10 / 2020Q1 @ Osiris Staff
  • 2IMC90 Data Sci & Engineering - In this meeting the student gets insight into the study program. Links 2IMC90 / 2020 @ Osiris Staff
  • 2AMI20 Advanced Process Mining - Understanding and predicting behavior of people and machines in a shared setting (task, project, factory, process, organization) is central to Data Science and Artificial Intelligence. Actions of people and machines can be recorded as discrete events in event sequences (logs), event databases (tables, graphs), and real-time event streams. Learning behavioral models of discrete event data Read More ...
  • 2IMI05 Capita selecta process analytics - People interested in the ‘process side’ of information systems can take the course ‘Capita selecta architecture of information systems’. This course will be organized in an ad-hoc manner taking into account the interests of the student. The focus will always be on a particular ‘hot topic’ in the information systems domain. The course can, in Read More ...
  • 2IMI00 Seminar AIS - In this seminar, a group of master students will get in touch with research in the area of Information Systems, where Process Mining and Process Analysis from Event Data are the central themes. We study recent publications in the area of process mining and practical applications on real-life examples, to provide a good insight into Read More ...
  • JBG060 Data Challenge 3 - The objective of the Data Challenge courses is to teach students how to perform large-scale data-driven analyses themselves, combining technical skills acquired earlier with insights gained in methodological courses. The focus of Data Challenge 3 is to take students through the entire life-cycle of a data analysis for public stakeholders, starting in a typical situation 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

Recent presentations

Recent projects

  • 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

  • BPIC’2018: Mining Concept Drift in Performance Spectra of Processes - Denisov, V. V., Belkina, E., & Fahland, D. (2018). BPIC’2018: Mining Concept Drift in Performance Spectra of Processes. (BPI Challenge 2018). https://doi.org/10.4121/uuid:3301445f-95e8-4ff0-981f1f204972
  • Using graph data structures for event logs - Esser, S., & Fahland, D. (2019). Using graph data structures for event logs. https://doi.org/10.5281/zenodo.3333831 Abstract Process mining as described in by Wil van der Aalst in is a combination of data mining and business process management to a new discipline. The general purpose of process mining is to derive process insights from event data captured Read More ...
  • Visualizing Token Flows Using Interactive Performance Spectra - van der Aalst, W. M. P., Tacke Genannt Unterberg, D., Denisov, V., & Fahland, D. (2020). Visualizing Token Flows Using Interactive Performance Spectra. In R. Janicki, N. Sidorova, & T. Chatain (Eds.), Application and Theory of Petri Nets and Concurrency – 41st International Conference, PETRI NETS 2020, Proceedings (pp. 369-380). (Lecture Notes in Computer Science Read More ...
  • Scalable alignment of process models and event logs: An approach based on automata and S-components - Reißner, D., Armas-Cervantes, A., Conforti, R., Dumas, M., Fahland, D., & La Rosa, M. (2020). Scalable alignment of process models and event logs: An approach based on automata and S-components. Information Systems, 94, [101561]. https://doi.org/10.1016/j.is.2020.101561 Abstract Given a model of the expected behavior of a business process and given an event log recording its observed Read More ...
  • Repairing Event Logs with Missing Events to Support Performance Analysis of Systems with Shared Resources - Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2020). Repairing Event Logs with Missing Events to Support Performance Analysis of Systems with Shared Resources. In R. Janicki, N. Sidorova, & T. Chatain (Eds.), Application and Theory of Petri Nets and Concurrency – 41st International Conference, PETRI NETS 2020, Proceedings (pp. 239-259). (Lecture Notes Read More ...
  • Multi-dimensional performance analysis and monitoring using integrated performance spectra - Denisov, V., Fahland, D., & Van Der Aalst, W. M. P. (2020). Multi-dimensional performance analysis and monitoring using integrated performance spectra. In C. Di Ciccio (Ed.), Proceedings of the ICPM Doctoral Consortium and Tool Demonstration Track 2020 co-located with the 2nd International Conference on Process Mining (ICPM 2020): Padua, Italy, October 4-9, 2020 (pp. 27-30). Read More ...
  • Identifying and reducing errors in remaining time prediction due to inter-case dynamics - Klijn, E. L., & Fahland, D. (2020). Identifying and reducing errors in remaining time prediction due to inter-case dynamics. In B. van Dongen, M. Montali, & M. T. Wynn (Eds.), Proceedings – 2020 2nd International Conference on Process Mining, ICPM 2020 (pp. 25-32). [9229927] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICPM49681.2020.00015 Abstract Remaining time prediction Read More ...
  • Detecting system-level behavior leading to dynamic bottlenecks - Toosinezhad, Z., Fahland, D., Köroglu, Ö., & Van Der Aalst, W. M. P. (2020). Detecting system-level behavior leading to dynamic bottlenecks. In B. van Dongen, M. Montali, & M. T. Wynn (Eds.), Proceedings – 2020 2nd International Conference on Process Mining, ICPM 2020 (pp. 17-24). [9230102] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICPM49681.2020.00014 Abstract Dynamic Read More ...
  • Defining meaningful local process models - Brunings, M., Fahland, D., & van Dongen, B. (2020). Defining meaningful local process models. In W. van der Aalst, R. Bergenthum, & J. Carmona (Eds.), ATAED 2020 Algorithms & Theories for the Analysis of Event Data 2020: Proceedings of the International Workshop on Algorithms & Theories for the Analysis of Event Data 2020: Satellite event Read More ...
  • Process mining for six sigma: a guideline and tool support - Graafmans, T. L. F., Türetken, O., Poppelaars, J. J. G. H., & Fahland, D. (Accepted/In press). Process mining for six sigma: a guideline and tool support. Business & Information Systems Engineering. Abstract Process mining offers a set of techniques for gaining data-based insights into business processes from event logs. The literature acknowledges the potential benefits Read More ...

 

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