Semantic Business Model Generation via Intermediate Model Merging
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Solution Overview
Problem
The manual generation of semantic business models is tedious, error-prone, and requires domain expertise, often leading to incomplete models and increased burden on users like business consultants, who need to identify relationships between business concepts.
Innovation Solution
A computer-implemented solution that transforms multiple business area models into intermediate models using a similar meta-modeling language and merges them to generate a semantic business model, reducing user expertise and time requirements, while enabling more detailed and larger-scale models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual generation of semantic business models is performed by experts, then the model accuracy and completeness improve, but the time consumption and user burden increase significantly
Solution Approach 1:
The patent segments the semantic business model generation process into multiple independent business area models (e.g., human resources, finance, operations). Each area model can be developed and validated separately, then automatically merged into a comprehensive semantic business model. This segmentation allows parallel development by different experts, reducing overall time consumption while maintaining model accuracy through specialized focus.
Solution Approach 2:
The patent implements preliminary action by providing pre-defined business area models and templates that capture common business patterns and relationships. These pre-built models serve as starting points that reduce the time required to create new semantic business models while maintaining accuracy through proven structures. The system performs preliminary validation and consistency checks before final model generation.
2Reliability
If manual generation of semantic business models is performed, then domain expertise can be applied, but the process becomes tedious and error-prone
Solution Approach 1:
The patent implements self-service through automated consistency validation and conflict resolution mechanisms. The system automatically checks for errors, validates model completeness, and resolves conflicts between different business area models without requiring manual intervention. This self-service approach reduces tedious manual checking while maintaining high reliability through systematic validation rules.
Solution Approach 2:
The patent incorporates feedback mechanisms that automatically validate business area models against predefined standards and constraints. The system provides immediate feedback on model completeness, consistency, and potential errors, allowing experts to correct issues systematically. This feedback loop maintains high model reliability while reducing the tedious nature of manual validation through automated checking.
3Quantity of substance
If multiple business area models are merged into a semantic business model, then the model scale and detail increase, but the complexity of integration and mapping increases
Solution Approach 1:
The patent uses an intermediary automated merging system that handles the complex integration of multiple business area models. This intermediary system performs automatic mapping between different model representations, resolves conflicts, and ensures consistency across the merged semantic business model. The intermediary approach manages integration complexity while enabling large-scale model merging that would be impractical to perform manually.
Data Source
AI summary
A computer-implemented solution for managing a semantic business model is provided. In particular, one or more business area models for a business entity are obtained and transformed into intermediate model(s). Each intermediate model is represented using a substantially similar meta-modeling language. Subsequently, the semantic business model is generated by merging the set of intermediate models. In this manner, the semantic business model can be generated in a manner that reduces the overall burden on a user. The semantic business model can be provided for display to the user and/or use in performing qualitative analysis on various aspects of the business entity.