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., Giacomo, G. D., Fahland, D., Gal, A., Rosa, M. L., Völzer, H., & Weber, I. (2022). Augmented Business Process Management Systems: A Research Manifesto. CoRR, abs/2201.12855. https://dblp.org/db/journals/corr/corr2201.html#abs-2201-12855

Inferring Unobserved Events in Systems With Shared Resources and Queues

Fahland, D., Denisov, V., & van der Aalst, W. M. P. (2021). Inferring Unobserved Events in Systems With Shared Resources and Queues. Fundamenta Informaticae, 183(3-4), 203-242. https://doi.org/10.3233/FI-2021-2087 Abstract To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for Read More …

Multi-Dimensional Event Data in Graph Databases

Esser, S., & Fahland, D. (2021). Multi-Dimensional Event Data in Graph Databases. Journal on Data Semantics, 10(1-2), 109–141. https://doi.org/10.1007/s13740-021-00122-1 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 (sub)sequences of events based on temporal Read More …

Process Discovery Using Graph Neural Networks

Sommers, D., Menkovski, V., & Fahland, D. (2021). Process Discovery Using Graph Neural Networks. In C. Di Ciccio, C. Di Francescomarino, & P. Soffer (Eds.), Proceedings – 2021 3rd International Conference on Process Mining, ICPM 2021 (pp. 40-47) https://doi.org/10.1109/ICPM53251.2021.9576849 Abstract Automatically discovering a process model from an event log is the prime problem in process Read More …

Striking a new Balance in Accuracy and Simplicity with the Probabilistic Inductive Miner

Brons, D., Scheepens, R., & Fahland, D. (2021). Striking a new Balance in Accuracy and Simplicity with the Probabilistic Inductive Miner. In C. Di Ciccio, C. Di Francescomarino, & P. Soffer (Eds.), Proceedings – 2021 3rd International Conference on Process Mining, ICPM 2021 (pp. 32-39) https://doi.org/10.1109/ICPM53251.2021.9576864 Abstract Numerous process discovery techniques exist for generating process Read More …

Business Process Management – 18th International Conference, BPM 2020, Seville, Spain, September 13-18, 2020, Proceedings

Fahland, D., Ghidini, C., Becker, J., & Dumas, M. (Eds.) (2020). Business Process Management – 18th International Conference, BPM 2020, Seville, Spain, September 13-18, 2020, Proceedings. (Lecture Notes in Computer Science; Vol. 12168). Springer. https://doi.org/10.1007/978-3-030-58666-9

Business Process Management Forum – BPM Forum 2020, Seville, Spain, September 13-18, 2020, Proceedings

Fahland, D., Ghidini, C., Becker, J., & Dumas, M. (Eds.) (2020). Business Process Management Forum – BPM Forum 2020, Seville, Spain, September 13-18, 2020, Proceedings. (Lecture Notes in Business Information Processing; Vol. 392). Springer. https://doi.org/10.1007/978-3-030-58638-6

Information-preserving abstractions of event data in process mining

Leemans, S. J. J., & Fahland, D. (2020). Information-preserving abstractions of event data in process mining. Knowledge and Information Systems, 62(3), 1143–1197. https://doi.org/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 Read More …

Identify Typical Process Optimization Use Cases for the Analysis of Interacting Instances of Different Processes Together (Process/data analyst intern)

Hoekenrode 3, Amsterdam, Netherlands Intern Region: EMEA – Europe, Middle East and Africa Employee Type: Intern (fixed time) 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 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 …

Classifying and Detecting Task Executions and Routines in Processes Using Event Graphs

Klijn, E. L., Mannhardt, F., & Fahland, D. (2021). Classifying and Detecting Task Executions and Routines in Processes Using Event Graphs. In A. Polyvyanyy, M. T. Wynn, A. Van Looy, & M. Reichert (Eds.), Business Process Management Forum, BPM 2021, Proceedings (pp. 212-229). (Lecture Notes in Business Information Processing; Vol. 427 LNBIP). https://doi.org/10.5281/zenodo.5091610, https://doi.org/10.1007/978-3-030-85440-9_13 Abstract Business Read More …

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 …

New master: Data Science and Artificial Intelligence (in Dutch)

Data als brandstof voor kunstmatige intelligentie; TU/e start nieuwe masteropleiding EINDHOVEN – Zonder brandstof komt een auto niet vooruit. Hetzelfde principe gaat op voor kunstmatige intelligentie: zonder voldoende en goede data is daar niets intelligents aan. Een nieuwe master van de TU/e combineert daarom die twee disciplines. Bron: ED

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 …

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.

Process Mining and Process Prediction in Logistics (Vanderlande)

Summary In the context of the “Process Mining in Logistics” research project between Vanderlande Industries, we are offering multiple Master projects on process mining on event data of large-scale material handling systems. The fundamental challenges addressed are size (logistics processes are a factor 10-100 larger than business processes), reliable performance analysis and process prediction. We Read More …

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 …

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, 63(3), 277-300. https://doi.org/10.1007/s12599-020-00649-w. Abstract Process mining offers a set of techniques for gaining data-based insights into business processes from event logs. The literature acknowledges Read More …

Two new PhD students: Eva Klijn and Fatemeh Shafiee

On February 1st, Eva Klijn and Fatemeh Shafiee started working as PhD students in the PA group. Eva started as PhD-TA, and is supervised by Dirk Fahland. Fatemeh started as a PhD student on the TACTICS project, and is supervised by Natalia Sidorova. A big welcome to the both of them!

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 …