Abstract
Artifact-driven process monitoring is an effective technique to autonomously monitor business processes. Instead of requiring human operators to notify when an activity is executed, artifact-driven process monitoring infers this information from the conditions of physical or virtual objects taking part in a process. However, SMARTifact, the existing monitoring platform implementing this technique, has been designed to run entirely on edge devices, each of which can monitor only one execution of the process. Thus, monitoring multiple executions at the same time, or reducing the computational requirements of edge devices is not possible. In this paper, we introduce a new artifact-driven monitoring platform that overcomes these limitations and makes artifact-driven monitoring fully scalable.
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Notes
- 1.
Source code available at https://github.com/eGSM-platform.
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Meroni, G., Garda, S. (2023). Artifact-Driven Process Monitoring at Scale. In: Monti, F., Rinderle-Ma, S., Ruiz Cortés, A., Zheng, Z., Mecella, M. (eds) Service-Oriented Computing. ICSOC 2023. Lecture Notes in Computer Science, vol 14420. Springer, Cham. https://doi.org/10.1007/978-3-031-48424-7_1
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