Abstract
Enterprises increasingly rely on cognitive capabilities to enhance their core business processes by adopting systems that utilize machine learning and deep learning approaches to support cognitive decisions to aid humans responsible for business process execution. Unlike conventional information systems, for which the design and implementation is a much-studied area, the design of cognitive systems and their integration into existing enterprise business processes is less well understood. This results in long drawn-out implementation and adoption cycles, and requires individuals with highly specialized skills. As cognitively-assisted business processes involve human and machine collaboration, non-functional requirements, such as reusability and configurability that are prominent for software system design, must also be addressed at the enterprise level. Supporting processes may emerge and evolve over time to monitor, evaluate, adjust, or modify these cognitively-enhanced business processes. In this paper, we utilize a goal-oriented approach to analyze the requirements for designing cognitive systems for simplified adoption in enterprises, which are then used to guide and inform the design of a process architecture for cognitive business operations.
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This work was partially funded by IBM Canada Ltd. through the Centre for Advanced Studies (CAS) Canada (Project #1030).
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Babar, Z., Yu, E., Carbajales, S., Chan, A. (2019). Managing and Simplifying Cognitive Business Operations Using Process Architecture Models. In: Giorgini, P., Weber, B. (eds) Advanced Information Systems Engineering. CAiSE 2019. Lecture Notes in Computer Science(), vol 11483. Springer, Cham. https://doi.org/10.1007/978-3-030-21290-2_40
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