Abstract
Process mining enables organizations to capture and improve their processes based on fact-based process execution data. A key question in the context of process improvement is how response s to an event (action) result in desired or undesired outcomes (effects). From a process perspective, this requires understanding the action-response patterns that occur. Current discovery techniques do not allow organizations to gain such insights. In this paper we present a novel approach to tackle this problem. We propose and formalize a technique to discover action-response-effect patterns. In this technique we use well-established statistical tests to uncover potential dependency relations between each response and its effect s on the cases. The goal of this technique is to provide organizations with processes that are: (1) appropriately represented, and (2) effectively filtered to show meaningful relations. The approach is evaluated on a real-world data set from a Dutch healthcare facility in the context of aggressive behavior of clients and the response s of caretakers.
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Notes
- 1.
Source code and results: github.com/xxlu/ActionEffectDiscovery.
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Acknowledgments
This research was supported by the NWO TACTICS project (628.011.004) and Lunet Zorg in the Netherlands. We would also like to thank the experts from the Lunet Zorg for their valuable assistance and feedback.
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Koorn, J.J., Lu, X., Leopold, H., Reijers, H.A. (2020). Looking for Meaning: Discovering Action-Response-Effect Patterns in Business Processes. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds) Business Process Management. BPM 2020. Lecture Notes in Computer Science(), vol 12168. Springer, Cham. https://doi.org/10.1007/978-3-030-58666-9_10
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