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Integration of Well‐Formed Semantic Mapping to Extract System Optimization Information.

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Title: Integration of Well‐Formed Semantic Mapping to Extract System Optimization Information.
Authors: Eddy, Douglas1 (AUTHOR) dceddy@umass.edu, Dunbar, Daniel2 (AUTHOR) ddunbar1@stevens.edu, Blackburn, Mark2 (AUTHOR), Grosse, Ian1 (AUTHOR) grosse@engin.umass.edu, Hagedorn, Thomas2 (AUTHOR) thagedor@stevens.edu
Source: Systems Engineering. Sep2026, Vol. 29 Issue 5, p823-841. 19p.
Subjects: Semantic Web, Modeling languages (Computer science), Multiple criteria decision making, Digital technology, Prediction models, Concept mapping, Mathematical optimization, Design science
Abstract: Systems that are typically modeled within SysML are complex and not easily represented in their full semantic context. Furthermore, model complexity poses challenges to rapid and optimal decision making early in a system development process without significant trial and error. There are some cases where representative data can be obtained to characterize a complex system's overall performance. For such cases, this work introduces a method grounded in the fundamentals of rigorous decision‐based design principles by its integration within a semantic Interoperability and Integration Framework (IoIF) that interfaces with SysML for digital engineering of complex system models. Salient features include simplified formulation of SysML model information to conform to fundamental principles. This resulting Best System Selection (BestSys) method utilizes data that represents any system‐of‐interest along with its requirements information within SysML to identify the best option. The method also constructs a surrogate model from the data to find a potential better solution in the trade space not located at a data point. The workflow further maps that system's information within IoIF in a way that may enable SPARQL queries and consistently meaningful visualizations. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Systems that are typically modeled within SysML are complex and not easily represented in their full semantic context. Furthermore, model complexity poses challenges to rapid and optimal decision making early in a system development process without significant trial and error. There are some cases where representative data can be obtained to characterize a complex system's overall performance. For such cases, this work introduces a method grounded in the fundamentals of rigorous decision‐based design principles by its integration within a semantic Interoperability and Integration Framework (IoIF) that interfaces with SysML for digital engineering of complex system models. Salient features include simplified formulation of SysML model information to conform to fundamental principles. This resulting Best System Selection (BestSys) method utilizes data that represents any system‐of‐interest along with its requirements information within SysML to identify the best option. The method also constructs a surrogate model from the data to find a potential better solution in the trade space not located at a data point. The workflow further maps that system's information within IoIF in a way that may enable SPARQL queries and consistently meaningful visualizations. [ABSTRACT FROM AUTHOR]
ISSN:10981241
DOI:10.1002/sys.70054