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Our work is concerned with the development of knowledge structures to support correct-by-design cyber-physical systems (CPS). This class of systems is defined by a tight integration of software and physical processes, the need to satisfy stringent constraints on performance and safety, and a reliance on automation for the management of system functionality and decision making. To assure correctness of functionality with respect to requirements, there is a strong need for system models to account for semantics of the domains involved. This paper introduces a new ontological-based knowledge and reasoning framework for decision support for CPS. It enables the development of determinate, provable and executable CPS models supported by sound semantics strengthening the model-driven approach to CPS design. An investigation into the structure of basic description logics (DL) has identified the needed semantic extensions to enable the web ontology language (OWL) as the ontological language for our framework. The SROIQ DL has been found to be the most appropriate logic-based knowledge formalism as it maps to OWL 2 and ensures its decidability. Thus, correct, stable, complete and terminating reasoning algorithms are guaranteed with this SROIQ-backed language. The framework takes advantage of the commonality of data and information processing in the different domains involved to overcome the barrier of heterogeneity of domains and physics in CPS. Rules-based reasoning processes are employed. The framework provides interfaces for semantic extensions and computational support, including the ability to handle quantities for which dimensions and units are semantic parameters in the physical world. Together, these capabilities enable the conversion of data to knowledge and their effective use for efficient decision making and the study of system-level properties, especially safety. We exercise these concepts in a traffic light time-based reasoning system.

We conceptualize, develop and prototype a semi‐automatic graph‐based framework for quality assessment (i.e., completeness, consistency, correctness) and probing — at all stages of development and maturity — architecture models developed in the System Modeling Language (SysML). Relevant data is extracted via partial model transformation, then organized and stored in a labeled property graph. Analytics powered by graph algorithms enable the System Analyst to gain deep insight into the model and answer increasingly complex and diverse questions. Multidigraph formalisms provide solid theoretical foundations for our approach while graph database technologies coupled with custom software development support its implementation. We demonstrate the capability of our approach in an analysis of requirements in a publicly available spacecraft architecture model.

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