What makes concept computing different?


Webs of meanings and knowledge. Constraint-based, direct model execution systems that know and can share what they know. Architectures of learning that perform better with use and scale.

Three stages of semantic solution envisioning

Three stages of semantic solution envisioning

The diagram above depicts three stages of semantic solution envisioning. Business capability exploration focuses on reaching agreement about goals, constraints, and the basic concepts and terms that different groups use. Different stakeholders express their requirements in their own language. Solution exploration defines needed capabilities and concept computing solution patterns. Semantic software design is knowledge-centric, iterative and incremental. Changes in ontology, data, rules, workflow, user experience, etc, can automatically update system functionality, without impacting underlying legacy systems and databases.

The process of semantic solution development is declarative, direct execution model-driven, and knowledge-centric and rather than being procedural, specification waterfall translation-driven, and document centric. Build cycles are fast, iterative, non-invasive. Semantic solution development typically entails less time, cost, and risk to deploy, maintain, and upgrade.

Knowledge is extracted and modeled separately from documents, schemas, or program code so it can be managed integrally across these different forms of expression, shared easily between applications (knowledge-as-a-service), aligned and harmonized across boundaries, and evolved. Requirements, policies, and solution patterns expressed as semantic models that execute directly can be updated with new knowledge as the application runs.

Semantic solutions emerge from a techno-social collaboration that also supports do-it-yourself development. The development process is business and user driven versus IT and developer driven. The collective knowledge developed is interpretable by different human stakeholder as well as machine executable. Different skills are key including domain expertise, concept computing architecture, knowledge modeling, and (smart) user experience design.

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