Abstract
The widespread diffusion of Internet-of-Things (IoT) technologies is prompting organizations to rethink their business processes (BPs) towards incorporating the data collected from IoT devices directly into BP models for improved effectiveness and timely decision making. Nonetheless, IoT devices are prone to failure due to their limitations in terms of computational power and energy autonomy, leading to compromise the availability and quality of the collected data, with the risk to prevent the correct execution of the entire BP. To mitigate this issue, resilience is a feature that any data-aware BP should support at design-time, by focusing on the role of available - as an alternative to unreliable - data as a resource for increasing BP robustness to failures. In this paper, we formalize an approach for designing and evaluating resilient-aware BP models in BPMN (Business Process Modeling and Notation) through a maturity model that takes into account their degree of awareness through levels of resilience, which can be computed using the provided formalization. In addition, we show how to extend the metamodel of BPMN 2.0 to address the proposed resiliency levels, and we investigate the feasibility of the approach through a user evaluation.
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This work has been supported by the H2020 project DataCloud and the Sapienza grant BPbots.
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Agostinelli, S., De Luzi, F., di Canito, U., Ferraro, J., Marrella, A., Mecella, M. (2022). A Data-Centric Approach to Design Resilient-Aware Process Models in BPMN. In: Di Ciccio, C., Dijkman, R., del Río Ortega, A., Rinderle-Ma, S. (eds) Business Process Management Forum. BPM 2022. Lecture Notes in Business Information Processing, vol 458. Springer, Cham. https://doi.org/10.1007/978-3-031-16171-1_3
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