Abstract
Process mining enables extracting insights into human resources working in business processes and supports employee management and process improvement. Often, resources from the same organizational group exhibit similar characteristics in process execution, e.g., executing the same set of process activities or participating in the same types of cases. This is a natural consequence of division of labor in organizations. These characteristics can be organized along various process dimensions, e.g., case, activity, and time, which ideally are all considered in the application of resource-oriented process mining, especially analytics of resource groups and their behavior. In this paper, we use the concept of execution context to classify cases, activities, and times to enable a precise characterization of resource groups. We propose an approach to automatically learning execution contexts from process execution data recorded in event logs, incorporating domain knowledge and discriminative information embedded in data. Evaluation using real-life event log data demonstrates the usefulness of our approach.
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Notes
- 1.
Implementation and experiment details: https://royjy.me/to/learn-co.
- 2.
BPIC-15 dataset: https://doi.org/10.4121/uuid:31a308ef-c844-48da-948c-305d167a0ec1.
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The reported research is part of a PhD project supported by an Australian Government Research Training Program (RTP) Scholarship.
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Yang, J., Ouyang, C., ter Hofstede, A.H.M., van der Aalst, W.M.P. (2022). No Time to Dice: Learning Execution Contexts from Event Logs for Resource-Oriented Process Mining. In: Di Ciccio, C., Dijkman, R., del Río Ortega, A., Rinderle-Ma, S. (eds) Business Process Management. BPM 2022. Lecture Notes in Computer Science, vol 13420. Springer, Cham. https://doi.org/10.1007/978-3-031-16103-2_13
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