Point-to-point connections between information systems or third-party data managers glue together the majority of today's systems engineering ecosystems. The focus is on data movement and traceability rather than organizing data in ways to better derive meaning and knowledge from it.
Meanwhile, data complexity increases exponentially as systems become more complex and the number of engineering models increases to cover more aspects of systems engineering processes, including requirements, architecture, independent verification and validation, and sustainment. And, over a system’s life cycle, the models and their data evolve as the system goes into operations and sustainment.
Engineers and logisticians need to track decisions made on a system by the varied domains and disciplines in the ecosystem, with each creating information in its own language and jargon. This presents challenges to understanding system impacts and limiting the complexity of information.
With the rise of data analytics, the systems engineering community is figuring out how to use ontologies to better define, fuse, and reason the data it generates. Ontologies guide the definitions and structuring of different things in an ecosystem based on their relationships with each other.
Ontology development in systems engineering will improve how data generated by different domains and disciplines is organized. The use of ontologies will improve data shareability and aid the discovery of system component relationships from data.
SAIC is making investments in building a set of ontologies for the systems engineering ecosystem, eliminating differences in syntax and semantics and establishing sets of common languages. We are building out a systems engineering process ontology, as well as ontologies for systems engineering artifacts and digital engineering artifacts. A fundamental, top-level ontology and the ISO/IEC/IEEE systems engineering process ontology seed our suite of ontologies, which lays the groundwork for interoperable ontologies and expansion across domains and disciplines.
With ontologies and semantic brokers in place, the systems engineering ecosystem will be able to dictate how data is curated, thus facilitating better integration and a holistic picture of a system.