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

Recent courses

  • 2IMI05 – Capita selecta architecture of information systems - Links 2IMI05 @ Osiris Staff involved
  • 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 ...
  • 2IMI20 Advanced Process Mining - Process mining provides a new means to understand and improve processes in an objective way in a variety of application domains through the analysis of recorded event data. This advanced course on process mining teaches students the fundamental concepts and theoretical foundations of process mining along a complete process mining methodology, and exposes students to 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

  • 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 ...
  • Storing and querying multi-dimensional process event logs using graph databases - Esser, S., & Fahland, D. (2019). Storing and querying multi-dimensional process event logs using graph databases. In 15th International Workshop on Business Process Intelligence Abstract Process event data is usually stored either in a sequential process event log or in a relational database. While the sequential, single-dimensional nature of event logs aids querying for event Read More ...
  • Predictive performance monitoring of material handling systems using the performance spectrum - Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2019). Predictive performance monitoring of material handling systems using the performance spectrum. In Proceedings – 2019 International Conference on Process Mining, ICPM 2019 (pp. 137-144). [8786068] Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/ICPM.2019.00029 Abstract Predictive performance analysis is crucial for supporting operational Read More ...
  • Performance mining for batch processing using the performance spectrum - Klijn, E. L., & Fahland, D. (2019). Performance mining for batch processing using the performance spectrum. In 15th International Workshop on Business Process Intelligence Abstract Performance analysis from process event logs is a central element of business process management and improvement. Established performance analysis techniques aggregate time-stamped event data to identify bottlenecks or to visualize Read More ...
  • Describing behavior of processes with many-to-many interactions - Fahland, D. (2019). Describing behavior of processes with many-to-many interactions. In S. Haar, & S. Donatelli (Eds.), Application and Theory of Petri Nets and Concurrency – 40th International Conference, PETRI NETS 2019, Proceedings (pp. 3-24). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11522 LNCS). Read More ...
  • Preface: Workshop on Petri Nets and Modeling 2018 - Fahland, D., Köhler-Bußmeier, M., & Moldt, D. (2018). Preface: Workshop on Petri Nets and Modeling 2018. In MOD-WS 2018 (CEUR Workshop Proceedings; Vol. 2060). CEUR-WS.org.  
  • Information-preserving abstractions of event data in process mining - Leemans, S. J. J., & Fahland, D. (2019). Information-preserving abstractions of event data in process mining. Knowledge and Information Systems. DOI: 10.1007/s10115-019-01376-9 Abstract Process mining aims at obtaining information about processes by analysing their past executions in event logs, event streams, or databases. Discovering a process model from a finite amount of event data thereby Read More ...
  • The performance spectrum miner : visual analytics for fine-grained performance analysis of processes - Denisov, Vadim, Belkina, Elena, Fahland, Dirk & van der Aalst, Wil M.P. (2018). The performance spectrum miner : visual analytics for fine-grained performance analysis of processes. CEUR Workshop Proceedings, 2196, 96-100. Abstract We present the Performance Spectrum Miner, a ProM plugin, which implements a new technique for fine-grained performance analysis of processes. The technique uses Read More ...
  • Who is behind the model? classifying modelers based on pragmatic model features - Burattin, Andrea, Soffer, Pnina, Fahland, Dirk, Mendling, Jan, Reijers, Hajo A., Vanderfeesten, Irene, Weidlich, Matthias & Weber, Barbara (2018). Who is behind the model? classifying modelers based on pragmatic model features. In Ingo Weber, Jan vom Brocke, Marco Montali & Mathias Weske (Eds.), Business Process Management – 16th International Conference, BPM 2018, Proceedings (pp. 322-338). Read More ...
  • Unbiased, fine-grained description of processes performance from event data - Denisov, V.V., Fahland, D. & van der Aalst, W.M.P. (2018). Unbiased, fine-grained description of processes performance from event data. Business Process Management – 16th International Conference, BPM 2018, Sydney, NSW, Australia, September 9-14, 2018, Proceedings. (pp. 139-157). (Lecture Notes in Computer Science, No. 11080). Springer. Abstract Performance is central to processes management and event data Read More ...

 

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