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Runtime Evolution of IoT Processes

IoT processes are enacted in a dynamic and highly connected physical environment. IoT grants the process the capability to understand and change the context where the process is executed. Plenty of context data may be relevant for a process, making IoT processes knowledge-intensive. For managing IoT knowledge-intensive processes, a combination of process models and decision models have shown to be successful. However, although runtime changes are extremely common in such processes, no approach in the literature supports their runtime evolution. Such processes must cope with runtime changes without having to shut down the system to perform the required adaptations. This proposal aims at developing a framework that identifies, formalizes, and supports all the change patterns that can occur for the runtime adaptation of an IoT process, where changes can take place in a process model, in a decision model, or in the IoT infrastructure that connects these models with the physical environment.
Date:3 Dec 2018 →  30 Sep 2020
Keywords:Decision models
Disciplines:Applied economics, Workflow, process and database management