Towards an Architecture for Assistive Process Execution Environments

Flexibility in Business Processes is still a challenging issue in research and practice. Performing tasks outside a predefined control flow gains importance in times of dynamic environments, higher employer autonomy, and value-oriented process management. We believe that data science and machine learning can support flexible processes' execution and design by providing assistance. This article sketches an architecture that supports this approach based on a short review of current and past approaches to process modeling and execution.

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