Achieving System‐of‐Systems Interoperability Levels Using Linked Data and Ontologies

Axelsson, Jakob

doi:10.1002/j.2334-5837.2020.00746.x

Cited by 14 publications

(2 citation statements)

References 19 publications

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“…Axelsson relates each of the LCIM levels to the Semantic Web technology stack with a special focus on the RDF data model, gives specific examples and also concludes that RDF is suited to resolve interoperability problems [5]. The use of ontologies to improve the MBSE process with a focus on enforcing consistent views on a product among its developers is investigated in detail by Tudorache [40].…”

Section: Support For Model-based Systems Engineeringmentioning

confidence: 99%

Dynamic system models and their simulation in the Semantic Web

Stüber

Frey

2024

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Modelling and Simulation (M&S) are core tools for designing, analysing and operating today’s industrial systems. They often also represent both a valuable asset and a significant investment. Typically, their use is constrained to a software environment intended to be used by engineers on a single computer. However, the knowledge relevant to a task involving modelling and simulation is in general distributed in nature, even across organizational boundaries, and may be large in volume. Therefore, it is desirable to increase the FAIRness (Findability, Accessibility, Interoperability, and Reuse) of M&S capabilities; to enable their use in loosely coupled systems of systems; and to support their composition and execution by intelligent software agents. In this contribution, the suitability of Semantic Web technologies to achieve these goals is investigated and an open-source proof of concept-implementation based on the Functional Mock-up Interface (FMI) standard is presented. Specifically, models, model instances, and simulation results are exposed through a hypermedia API and an implementation of the Pragmatic Proof Algorithm (PPA) is used to successfully demonstrate the API’s use by a generic software agent. The solution shows an increased degree of FAIRness and fully supports its use in loosely coupled systems. The FAIRness could be further improved by providing more “ rich” (meta)data.

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Section: Support For Model-based Systems Engineeringmentioning

confidence: 99%

Dynamic system models and their simulation in the Semantic Web

Stüber

Frey

2024

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“…Interoperability is defined as the cooperation between two or more software components, which are designed in different interfaces, languages and execution platforms [13]. Guychard et al [14] proposed an approach to ensure consistency and maintain traceability based on three interoperable spaces.…”

Section: Interoperability Approachmentioning

confidence: 99%

Conflict Resolution in Mechatronic Collaborative Design Using Category Theory

et al. 2021

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Due to the multitude of disciplines involved in mechatronic design, heterogeneous languages and expert models are used to describe the system from different domain-specific views. Despite their heterogeneity, these models are highly interrelated. As a consequence, conflicts among expert models are likely to occur. In order to ensure that these models are not contradictory, the necessity to detect and manage conflicts among the models arises. Detecting these inconsistencies at an early stage significantly reduces the amount of engineering activities re-execution. Therefore, to deal with this issue, a formal framework relying upon mathematical concepts is required. The mathematical theory, namely category theory (CT), is considered as an efficient tool to provide a formal and unifying framework supporting conflict detection and management. This paper proposes a comprehensive methodology that allows conflict detection and resolution in the context of mechatronic collaborative design. CT is used in order to explicitly capture the inconsistencies occurred between the disparate expert models. By means of this theory, the conflicts can be detected and handled in an easy and formal way. Our proposed approach is applied to a collaborative scenario concerning the electro-mechanical actuator (EMA) of the aileron.

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An Agent‐Based Ontology to Support Modeling of Socio‐Technical Systems‐of‐Systems

Axelsson

2024

INCOSE International Symp

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Systems‐of‐systems are characterized by the independence of their constituent elements. Such an element is usually socio‐technical, comprising technology, humans, or organizations. To capture its independence, it needs to be viewed as an intelligent agent that relies on an internal model of the world for its decision‐making. Hence, a system‐of‐systems model will include multiple agents that inside themselves contain different models of the same system‐of‐systems. Describing these overlapping subjective models and their usage by the agents is essential to properly understand the resulting behavior of the overall system‐of‐systems. Current modeling practices are not well suited for dealing with this, and the paper therefore outlines an ontology that makes the agents and their internal models more explicit. The paper also discusses the implications such models have on systems engineering practices and how they address known system‐of‐systems engineering pain points.

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Achieving System‐of‐Systems Interoperability Levels Using Linked Data and Ontologies

Cited by 14 publications

References 19 publications

Dynamic system models and their simulation in the Semantic Web

Dynamic system models and their simulation in the Semantic Web

Conflict Resolution in Mechatronic Collaborative Design Using Category Theory

An Agent‐Based Ontology to Support Modeling of Socio‐Technical Systems‐of‐Systems

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