5.5.2 Connections in System of Systems

Osmundson, John S.; Langford, Gary O.

doi:10.1002/j.2334-5837.2012.tb01371.x

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Several architectural patterns suitable for SoS, including centralized pattern, service‐oriented pattern, publish‐subscribe pattern, and SoS contract monitor have been proposed . The importance of clearly defining the interfaces, connectivity, and exchanges within the constituent systems was highlighted . With the increasing convergence of embedded computing platforms and the internet, architecting SoS explicitly has also acquired greater relevance in non‐military domains .…”

Section: Related Work and Challenges In Architecting Complex Systems mentioning

confidence: 99%

“…18 The importance of clearly defining the interfaces, connectivity, and exchanges within the constituent systems was highlighted. 19 With the increasing convergence of embedded computing platforms and the internet, architecting SoS explicitly has also acquired greater relevance in non-military domains. 4 The INCOSE SoS Working Group study surfaced many pain points associated with SoS.…”

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

View full text Add to dashboard Cite

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“…(2015), while highlighting that unsynchronized evolution of constituent systems poses significant challenges in architecting SoS, illustrated several architectural patterns suitable for SoS, including Centralized pattern, Service-Oriented pattern, Publish-Subscribe pattern, and SoS Contract Monitor. The importance of clearly defining the interfaces, connectivity and exchanges within the constituent systems was highlighted by Osmundson and Langford (2012). With the increasing convergence of embedded computing platforms and the internet, architecting SoS explicitly has acquired greater relevance in non-military domains (Nielsen et al, 2015).…”

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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5.5.2 Connections in System of Systems

Cited by 2 publications

References 6 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

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