Article

Supervised learning of process discovery techniques using graph neural networks

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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 …

AI-augmented Business Process Management Systems: A Research Manifesto

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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 …

Privacy and Confidentiality in Process Mining: Threats and Research Challenges

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Elkoumy, G., Fahrenkrog-Petersen, S. A., Sani, M. F., Koschmider, A., Mannhardt, F., Voigt, S. N. V., Rafiei, M., & Waldthausen, L. V. (2022). Privacy and Confidentiality in Process Mining: Threats and Research Challenges. ACM Transactions on Management Information Systems, 13(1), [11]. https://doi.org/10.1145/3468877 Abstract Privacy and confidentiality are very important prerequisites for applying process mining to Read More …

Process mining for healthcare: Characteristics and challenges

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Jorge Munoz-gama, Niels Martin, Carlos Fernandez-llatas, Owen A. Johnson, Marcos Sepúlveda, Emmanuel Helm, Victor Galvez-yanjari, Eric Rojas, Antonio Martinez-millana, Davide Aloini, Ilaria Angela Amantea, Robert Andrews, Michael Arias, Iris Beerepoot, Elisabetta Benevento, Andrea Burattin, Daniel Capurro, Josep Carmona, Marco Comuzzi, Benjamin Dalmas, Rene De La Fuente, Chiara Di Francescomarino, Claudio Di Ciccio, Roberto Gatta, Chiara Read More …

Animation as a dynamic visualization technique for improving process model comprehension

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Aysolmaz, B., & Reijers, H. A. (2021). Animation as a dynamic visualization technique for improving process model comprehension. Information and Management, 58(5), [103478]. https://doi.org/10.1016/j.im.2021.103478 Abstract Process models are widely used for various system analysis and design activities, but it is challenging for stakeholders to understand these complex artifacts. In this work, we focus on the Read More …

Conformance checking of mixed-paradigm process models

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van Dongen, B. F., De Smedt, J., Di Ciccio, C., & Mendling, J. (2021). Conformance checking of mixed-paradigm process models. Information Systems, 102, [101685]. https://doi.org/10.1016/j.is.2020.101685 Abstract Mixed-paradigm process models integrate strengths of procedural and declarative representations like Petri nets and DECLARE. They are specifically interesting for process mining because they allow capturing complex behavior in Read More …

Prefix Imputation of Orphan Events in Event Stream Processing

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Zaman, R., Hassani, M., & van Dongen, B. F. (2021). Prefix Imputation of Orphan Events in Event Stream Processing. Frontiers in Big Data, 4, [705243]. https://doi.org/10.3389/fdata.2021.705243 Abstract In the context of process mining, event logs consist of process instances called cases. Conformance checking is a process mining task that inspects whether a log file is Read More …

What Averages Do Not Tell – Predicting Real Life Processes with Sequential Deep Learning

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Ketykó, I., Mannhardt, F., Hassani, M., & van Dongen, B. F. (2021). What Averages Do Not Tell – Predicting Real Life Processes with Sequential Deep Learning. CoRR, abs/2110.10225. https://arxiv.org/abs/2110.10225 Abstract Deep Learning is proven to be an effective tool for modeling sequential data as shown by the success in Natural Language, Computer Vision and Signal Read More …

Augmented Business Process Management Systems: A Research Manifesto

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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

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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

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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 …

Information-preserving abstractions of event data in process mining

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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 …

Protecting Citizens’ Personal Data and Privacy: Joint Effort from GDPR EU Cluster Research Projects

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Medeiros de Carvalho, R., Del Prete, C., Martin, Y. S., Araujo Rivero, R. M., Önen, M., Schiavo, F. P., Rumín, Á., Mouratidis, H., Yelmo, J. C., & Koukovini, M. N. (2020). Protecting Citizens’ Personal Data and Privacy: Joint Effort from GDPR EU Cluster Research Projects. SN Computer Science, 1, [217]. https://doi.org/10.1007/s42979-020-00218-8 Abstract Confidence in information Read More …

Privacy and Confidentiality in Process Mining – Threats and Research Challenges

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Elkoumy, G., Fahrenkrog-Petersen, S. A., Sani, M. F., Koschmider, A., Mannhardt, F., Voigt, S. N. V., Rafiei, M., & Waldthausen, L. V. (Accepted/In press). Privacy and Confidentiality in Process Mining – Threats and Research Challenges. ACM Transactions on Management Information Systems, XX(X). https://arxiv.org/abs/2106.00388 Abstract Privacy and confidentiality are very important prerequisites for applying process mining Read More …

The Log Skeleton Visualizer in ProM 6.9

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Verbeek, H.M.W. The Log Skeleton Visualizer in ProM 6.9: The winning contribution to the process discovery contest 2019. Int J Softw Tools Technol Transfer, 24(4), 549-561. https://doi.org/10.1007/s10009-021-00618-y Abstract Process discovery is an important area in the field of process mining. To help advance this area, a process discovery contest (PDC) has been set up, which Read More …

Business Process Management: The evolution of a discipline

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Reijers, H. A. (2021). Business Process Management: The evolution of a discipline. Computers in Industry, 126, [103404]. https://doi.org/10.1016/j.compind.2021.103404 Abstract Business Process Management (BPM) embodies a management philosophy, which is supported by a range of methods, techniques, and tools. Academics are continuously expanding this repertoire. In this overview article, the themes are sketched that characterize the Read More …

Process training for industrial organisations using 3D environments: An empirical analysis

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Leyer, M., Aysolmaz, B., Brown, R., Türkay, S., & Reijers, H. A. (2021). Process training for industrial organisations using 3D environments: An empirical analysis. Computers in Industry, 124, [103346]. https://doi.org/10.1016/j.compind.2020.103346 Abstract Industrial organisations spend considerable resources on training employees with respect to the organisations’ business processes. These resources include business process models, diagrams depicting vital Read More …

Efficient process conformance checking on the basis of uncertain event-to-activity mappings

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van der Aa, H., Leopold, H., & Reijers, H. A. (2020). Efficient process conformance checking on the basis of uncertain event-to-activity mappings. IEEE Transactions on Knowledge and Data Engineering, 32(5), 927-940. [8634941]. https://doi.org/10.1109/TKDE.2019.2897557 Abstract Conformance checking enables organizations to automatically identify compliance violations based on the analysis of observed event data. A crucial requirement for Read More …

Privacy-preserving process mining in healthcare

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Pika, A., Wynn, M. T., Budiono, S., ter Hofstede, A. H. M., van der Aalst, W. M. P., & Reijers, H. A. (2020). Privacy-preserving process mining in healthcare. International Journal of Environmental Research and Public Health, 17(5), [1612]. https://doi.org/10.3390/ijerph17051612 Abstract Process mining has been successfully applied in the healthcare domain and has helped to uncover Read More …

Selecting a process variant modeling approach: guidelines and application

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Aysolmaz, B., Schunselaar, D. M. M., Reijers, H. A., & Yaldiz, A. (2019). Selecting a process variant modeling approach: guidelines and application. Software and Systems Modeling, 18(2), 1155-1178. https://doi.org/10.1007/s10270-017-0648-z Abstract Various modeling approaches have been introduced to manage process diversity in a business context. For practitioners, it is difficult to select an approach suitable for Read More …

Scalable Discovery of Hybrid Process Models in a Cloud Computing Environment

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Cheng, L., van Dongen, B. F., & van der Aalst, W. M. P. (2020). Scalable Discovery of Hybrid Process Models in a Cloud Computing Environment. IEEE Transactions on Services Computing, 13(2), 368-380. [8669858]. https://doi.org/10.1109/TSC.2019.2906203 Abstract Process descriptions are used to create products and deliver services. To lead better processes and services, the first step is Read More …

Enabling efficient process mining on large data sets: realizing an in-database process mining operator

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Dijkman, R., Gao, J., Syamsiyah, A., van Dongen, B., Grefen, P., & ter Hofstede, A. (2020). Enabling efficient process mining on large data sets: realizing an in-database process mining operator. Distributed and Parallel Databases, 38(1), 227-253. https://doi.org/10.1007/s10619-019-07270-1 Abstract Process mining can be used to analyze business processes based on logs of their execution. These execution Read More …

Aligning observed and modelled behaviour by maximizing synchronous moves and using milestones

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Bloemen, V., van Zelst, S., van der Aalst, W., van Dongen, B., & van de Pol, J. (Accepted/In press). Aligning observed and modelled behaviour by maximizing synchronous moves and using milestones. Information Systems, 103, [101456]. https://doi.org/10.1016/j.is.2019.101456 Abstract Given a process model and an event log, conformance checking aims to relate the two together, e.g. to Read More …

Scalable alignment of process models and event logs: An approach based on automata and S-components

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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 …

On Enabling GDPR Compliance in Business Processes Through Data-Driven Solutions

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Zaman, R., & Hassani, M. (2020). On Enabling GDPR Compliance in Business Processes Through Data-Driven Solutions. SN Computer Science, 1(4), 1-15. [210]. https://doi.org/10.1007/s42979-020-00215-x Abstract The collection and long-term retention of excessive data enables organisations to process data for insights in non-primary processes. The discovery of insights is promoted to be useful both for organisations and Read More …

Detection of batch activities from event logs

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Martin, N., Pufahl, L., & Mannhardt, F. (2021). Detection of batch activities from event logs. Information Systems, 95, [101642]. https://doi.org/10.1016/j.is.2020.101642 Abstract Organizations carry out a variety of business processes in order to serve their clients. Usually supported by information technology and systems, process execution data is logged in an event log. Process mining uses this Read More …

Event abstraction in process mining: literature review and taxonomy

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van Zelst, S. J., Mannhardt, F., de Leoni, M., & Koschmider, A. (2020). Event abstraction in process mining: literature review and taxonomy. Granular Computing, XX(XX). https://doi.org/10.1007/s41066-020-00226-2 Abstract The execution of processes in companies generates traces of event data, stored in the underlying information system(s), capturing the actual execution of the process. Analyzing event data, i.e., Read More …

Recommendations for enhancing the usability and understandability of process mining in healthcare

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Martin, N., De Weerdt, J., Fernández-Llatas, C., Gal, A., Gatta, R., Ibáñez, G., Johnson, O., Mannhardt, F., Marco-Ruiz, L., Mertens, S., Munoz-Gama, J., Seoane, F., Vanthienen, J., Wynn, M. T., Boilève, D. B., Bergs, J., Joosten-Melis, M., Schretlen, S., & Acker, B. V. (2020). Recommendations for enhancing the usability and understandability of process mining in Read More …

The Internet of Things Meets Business Process Management: A Manifesto

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Janiesch, C., Koschmider, A., Mecella, M., Weber, B., Burattin, A., Di Ciccio, C., Fortino, G., Gal, A., Kannengiesser, U., Leotta, F., Mannhardt, F., Marrella, A., Mendling, J., Oberweis, A., Reichert, M., Rinderle-ma, S., Serral, E., Song, W., Su, J., Torres, V., Weidlich, M., Weske, M., Zhang, L. (2020). The Internet of Things Meets Business Process Read More …

A trust and privacy framework for smart manufacturing environments

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Mannhardt, F., Petersen, S. A., & Oliveira, M. F. (2019). A trust and privacy framework for smart manufacturing environments. Journal of Ambient Intelligence and Smart Environments, 11(3), 201-219. https://doi.org/10.3233/AIS-190521 Abstract Operators in industrial manufacturing environments are under pressure to cope with the ever increasing flexibility and complexity of work. Transitioning towards data-driven smart manufacturing environments Read More …

Privacy-Preserving Process Mining: Differential Privacy for Event Logs

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Mannhardt, F., Koschmider, A., Baracaldo, N., Weidlich, M., & Michael, J. (2019). Privacy-Preserving Process Mining: Differential Privacy for Event Logs. Business and Information Systems Engineering, 61(5), 595-614. https://doi.org/10.1007/s12599-019-00613-3 Abstract Privacy regulations for data can be regarded as a major driver for data sovereignty measures. A specific example for this is the case of event data Read More …

Connecting databases with process mining: a meta model and toolset

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González López de Murillas, E., Reijers, H. A., & van der Aalst, W. M. P. (2019). Connecting databases with process mining: a meta model and toolset. Software and Systems Modeling, 18(2), 1209-1247. https://doi.org/10.1007/s10270-018-0664-7 Abstract Process mining techniques require event logs which, in many cases, are obtained from databases. Obtaining these event logs is not a Read More …