Recursive Ontology-Based Model Engineering for Scalable Business Solutions
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Solution Overview
Problem
Current Model-Driven Engineering (MDE) standards face limitations in transforming models into executable business solutions and do not support multilevel meta-modeling, restricting the development of large model-based infrastructures, particularly in scalability, persistence, composition, and automated deployment.
Innovation Solution
A recursive ontology-based model engineering method and system that creates standardized models of reality, transforms them into computable artifacts without programming, and uses collaborative knowledge representations, enabling scalable and persistent business solutions through Domain Specific Languages (DSLs) and Language Processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If standard MDE models are used, then model generation is simplified, but transformation into executable solutions is not provided
Solution Approach 1:
The patent introduces an intermediary transformation layer between models and executable solutions. This intermediary consists of a model execution engine that mediates between the abstract model layer and the concrete executable layer, enabling automatic transformation without requiring direct programming intervention.
Solution Approach 2:
The patent replaces manual programming mechanisms with automated model-driven mechanisms. Instead of programmers manually writing code, the system uses automated transformations that convert models directly into executable solutions, substituting the mechanical process of manual coding with automated computational transformation.
2Device complexity
If single-level modeling is used, then model simplicity is maintained, but multilevel meta-modeling is not supported
Solution Approach 1:
The patent implements nested modeling where multiple levels of models are contained within each other. Each model can be instantiated as a meta-model for another model, creating a nested structure that supports multilevel abstraction while maintaining manageable complexity through hierarchical organization.
Solution Approach 2:
The patent adds a new dimension to modeling by introducing temporal and contextual layers. Models are not just static structures but evolve through time and context, enabling multilevel abstraction that captures complex realities across different dimensions without overwhelming complexity.
3Adaptability or versatility
If large-scale models are used, then scalability is improved, but model management complexity increases
Solution Approach 1:
The patent segments large models into manageable components organized across multiple levels. Each level contains specific aspects of the system, and models can be independently managed, instantiated, and transformed. This segmentation reduces management complexity while maintaining scalability through hierarchical composition.
Solution Approach 2:
The patent creates universal model structures that can serve multiple functions across different domains. The same modeling framework and transformation mechanisms can handle various types of systems, reducing the need for domain-specific management tools and simplifying scalability through reuse.
4Manufacturing precision
If manual transformation is used, then transformation accuracy is controlled, but automated deployment is not achieved
Solution Approach 1:
The patent implements self-service transformation where the model execution engine automatically performs transformations without human intervention. The system monitors, transforms, and deploys models autonomously, maintaining accuracy through built-in validation mechanisms while achieving full automation.
Solution Approach 2:
The patent incorporates feedback mechanisms that automatically validate transformation results and adjust processes as needed. The system monitors transformation accuracy and deployment status, providing feedback loops that ensure precision while enabling automated continuous deployment without manual verification.
Data Source
AI summary
The present disclosure proposes a new model engineering method and system that permits the creation of application systems without the need of program development. The system allows organizations to search for high performance development teams and methods, and develop high quality solutions. The present disclosure covers the three central areas of systems engineering: (1) a method for creating models which represent reality in a standardized way; (2) a procedure for transforming models into computable artifacts, that is, computer systems that behave as specified in the model; and (3) a collaborative method based in knowledge representations.