Bio: For over 25 years, David Long has focused on helping organizations increase their systems engineering proficiency while simultaneously working to advance the state of the art. David is the founder and president of Vitech where he leads the team in delivering innovative, industry-leading methods and software (CORE™ and GENESYS™) to help organizations engineer next-generation systems. He co-authored A Primer for Model-Based Systems Engineering and frequently delivers keynotes and tutorials at industry events around the world. An INCOSE Fellow and Expert Systems Engineering Professional (ESEP), David was the 2014/2015 president of INCOSE.
David holds a bachelor’s degree in Engineering Science and Mechanics, as well as a master’s degree in Systems Engineering from Virginia Tech.
Contact Information: Vitech, Blacksburg, Virginia, USA, email: firstname.lastname@example.org
Abstract: Over the last five years, there has been a growing fascination with conceptual data models, metamodels, and ontologies in systems engineering. What began as a murmur – something living largely at the fringes of systems engineering and MBSE – has grown as many projects and practitioners delve into these topics.
So what are these concepts? What differentiates them, and more importantly, why should I care? How do I properly leverage these ideas to advance my projects and my enterprise?
As organizations apply model-based systems engineering, managing information in a computer model requires a defined data structure. Combined with the ease of modern ontology editors such as OWL or capabilities embedded in many tools, practitioners have begun to develop their own conceptual data models and ontologies. As systems engineers experiment and leverage these capabilities, they cross into the area of language design, often developing custom languages for their projects without the greater depth or consideration necessary to connect enterprise practices.
There is a fundamental information model that underpins systems engineering. This information model characterizes the knowledge we must elicit, develop, analyze, and manage in order to successfully engineer systems. It lives implicitly in the process standards that guide our practice, the data item descriptions that define our artifacts, and the representations we use.
The challenge is to move from implicit and explicit, not to advance MBSE but to advance the greater practice of systems engineering. To do so means that we must do more than develop independent data models for projects (the trap of “define and use”). We can leverage decades of practical experience to develop a shared systems metamodel that enables us to effectively communicate, analyze, and reason as we address today’s systems challenges. Rather than each project or each organization isolated on an island of their own language, we can and must achieve consistency of data and commonality of practice across the enterprise, across the supply chain, and across the profession.