11.4.1 Systems of Systems (SoS) Engineering: a view from the inside looking out

Siemieniuch, Carys E.; Sinclair, M.A.

doi:10.1002/j.2334-5837.2012.tb01428.x

Cited by 4 publications

(3 citation statements)

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“…Further, the evolution of a constituent system is often dictated by the circumstances in the owning organization. Hence, there is a significant role for formalization, dissemination, and conservation of knowledge in the context of architecting the SoS . Constituent systems, developed for other purposes, often get leveraged to be part of a SoS at a later point in time.…”

Section: Related Work and Challenges In Architecting Complex Systems mentioning

confidence: 99%

“…Hence, there is a significant role for formalization, dissemination, and conservation of knowledge in the context of architecting the SoS. 21 23 and approaches, such as separating behaviors and interactions, formalizing models for automatic execution, and using abstraction/refinement, 24 have been proposed to address emergent behavior. There has also been research pertaining to adopting machine learning techniques to assist in making design decisions for complex systems (for instance, in FPGA design 25 ), and in ensuring that fundamental architectural aspects do not erode with evolution.…”

Section: Related Workmentioning

confidence: 99%

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Decision learning framework for architecture design decisions of complex systems and system‐of‐systems

Raman

D’Souza

2019

Systems Engineering

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Architecting complex systems and complex system‐of‐systems (SoS) have evinced keen interest recently. Architectural design decisions have a significant bearing on the operational measures of success, referred to as Measures of Effectiveness (MOEs), of the systems and SoS. Architecting complex systems and SoS involves making architecture design decisions despite uncertainty (due to knowledge gaps) on the implications associated with the decisions. The learning of whether the decision is optimal or not, and the impact on the MOEs and the emergent behavior of the SoS, often occur later, resulting in Learning Cycles. This paper proposes an integrated decision learning framework for architecture design decisions for complex systems and SoS. The proposed framework adopts a decision oriented view that factors the uncertainty associated with architectural decisions and the learning cycles and feedback loops experienced. The framework enables leverage of machine learning approaches to learn from the decision learning cycles experienced and factor it into the uncertainty assessments of the decisions. By inculcating various aspects such as knowledge gaps and learning cycles, by building models such as Learning Cycle Model and Uncertainty Model, and by incorporating deployment approaches such as codification of decision attributes and decision uncertainty assessments, the proposed framework enables progressive maturity of the architectural knowledge base and aids robustness in architecture design decisions.

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Section: Related Work and Challenges In Architecting Complex Systems mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Decision learning framework for architecture design decisions of complex systems and system‐of‐systems

Raman

D’Souza

2019

Systems Engineering

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“…Further, the evolution of a constituent system is often dictated by the circumstances in the owning organization. Hence, there is a significant role for formalization, dissemination and conservation of knowledge in the context of designing the SoS (Siemieniuch and Sinclair, 2012). The INCOSE SoS Working Group (SoSWG) study surfaced many pain points associated with SoS (Dahmann, 2014).…”

Section: Architecture and Evolution Challenges In Complex Sosmentioning

confidence: 99%

Knowledge Based Decision Model for Architecting and Evolving Complex System‐of‐Systems

Raman

D’Souza

2017

INCOSE International Symp

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Architecting complex System‐of‐System (SoS) has evinced keen interest recently, specifically towards factoring in the operational and managerial independence of the constituent systems, and the evolutionary and adaptive nature of SoS development. Architectural decisions have a significant bearing on the operational measures of success, referred to as Measures of Effectiveness (MOEs), of the SoS. There is inherent uncertainty while making an architectural decision for complex SoS due to the incomplete knowledge on the implications of the decision. The impact on the MOEs of the SoS may be realized only later in the development lifecycle, influencing the performance and the emergent behavior of the SoS. Further, when learning cycles on the architectural decisions are experienced, once the implication of decision is realized, there needs to be means for incorporating the feedback on the decisions taken, and to reflect back on the uncertainty associated with the decisions. This paper proposes a knowledge based decision model for architecting and evolving complex SoS, that takes into account the uncertainty associated with architectural decisions and the learning cycles and feedback loops experienced. It also enables augmenting the architectural knowledge base, both at a constituent system level as well as at the System‐of‐System level. The proposed model adopts a decision oriented view that enables factoring in uncertainty, learning cycles and feedback loops in architectural decisions. It facilitates exploring the implications of various SoS evolution scenarios on architectural decisions, while analyzing MOEs of the SoS in relation to the MOEs of the constituent systems.

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Knowledge management for metrics: Enabling analysis and dissemination of metrics

Walworth

Yearworth

Shrieves

2014

2014 IEEE International Systems Conference Proceedings

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11.4.1 Systems of Systems (SoS) Engineering: a view from the inside looking out

Cited by 4 publications

References 25 publications

Decision learning framework for architecture design decisions of complex systems and system‐of‐systems

Decision learning framework for architecture design decisions of complex systems and system‐of‐systems

Knowledge Based Decision Model for Architecting and Evolving Complex System‐of‐Systems

Knowledge management for metrics: Enabling analysis and dissemination of metrics

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