Abstract

Model-based systems engineering (MBSE) provides an important capability for managing the complexities of system development. MBSE empowers the formalism of system architectures for supporting model-based requirement elicitation, specification, design, development, testing, fielding, etc. However, the modeling languages and techniques are heterogeneous, even within the same enterprise system, which leads to difficulties for data interoperability. The discrepancies among data structures and language syntaxes make information exchange among MBSE models more difficult, resulting in considerable information deviations when connecting data flows across the enterprise. Therefore, this article presents an ontology based upon <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">graphs</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">objects</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">points</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">properties</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">roles</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">relationships</i> with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">extensions</i> (GOPPRRE), providing metamodels that support the various MBSE formalisms across lifecycle stages. In particular, knowledge graph models are developed to support unified model representations to further implement ontological data integration based on GOPPRRE throughout the entire lifecycle. The applicability of the MBSE formalism is verified using quantitative and qualitative approaches. Moreover, the GOPPRRE ontologies are used to create the MBSE formalisms in a domain-specific modeling tool, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MetaGraph</i> , for evaluating its availability. The results demonstrate that the proposed ontology supports the formal structures and descriptive logic of the systems engineering lifecycle.

Highlights

  • T HE increasing complexity of technological innovations and their interoperability requirements within systemsof-systems, systems, subsystems, and components have led to overcomplexity of architectures and data structures

  • Through the SPARQL query of knowledge graph models, which are generated from model-based systems engineering (MBSE) models, model components, and their relationships can be captured to identify whether all the components and relationships are stored in the knowledge graph models

  • The results proved that the ontology based on the GOPPRRE approach could formalize at least five MBSE modeling languages, which are commonly used to model systems of systems (e.g., UPDM), system architectures (e.g., Systems Modeling Language (SysML)), business processes (e.g., Business Process Modeling Notation (BPMN)), and domain-specific knowledge for the architectural description of automotive embedded systems

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Summary

INTRODUCTION

T HE increasing complexity of technological innovations and their interoperability requirements within systemsof-systems, systems, subsystems, and components have led to overcomplexity of architectures and data structures. Much of the complexity is the result of individual stakeholder interests; they may have different concerns about the systems and artifacts of interest, and they may, in turn, demand unique informational and data-standard feedback These results can often be seen within the architectural models, as discrepancies among such models create a system-integration issue, resulting in barriers to communications, understandability, and more importantly, operations. This ontology presents a formalization solution for the MBSE modeling with a unified syntax and data structure to support systems engineering information exchange via the integration of AI and ML. The main contributions of this defined ontology are as follows: 1) it supports integrated architectural representation across the lifecycle; 2) it promotes MSBE tools built upon data interoperability and consistency; 3) it provides potential solutions for developing AI/ML MBSE roadmaps.

Literature Review
Summary
Research Methodology
Problem Statement
Overview
GOPPRRE Concept Mappings to Knowledge Graph Models
CASE STUDY
Quantitative Analysis
Qualitative Analysis
Discussion
CONCLUSION
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