This paper proposes a novel approach for identifying risks in executable business processes and detecting them at run-time. The approach considers risks in all phases of the business process management lifecycle, and is realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of faults to occur. Both historical and current process execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a process automation suite to prompt the results to the user who may take remedial actions. The proposed architecture has been implemented in the YAWL system and its performance has been evaluated in practice. © 2011 Springer-Verlag.
CITATION STYLE
Conforti, R., Fortino, G., La Rosa, M., & Ter Hofstede, A. H. M. (2011). History-aware, real-time risk detection in business processes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7044 LNCS, pp. 100–118). https://doi.org/10.1007/978-3-642-25109-2_8
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