Annotation-Based Enterprise Model Transformation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for transforming enterprise models into technical cross-organizational business process models are manual, leading to information loss and high costs due to redundant modeling activities, and are not executable in information and computing technology systems.
Innovation Solution
An annotation-based transformation method that captures semantic entities and their relationships, converts them into a technical representation, and reassembles them into a cross-organizational business process model, using a three-phase approach that includes an annotation framework, mapping repository, and model converter to preserve business knowledge and reduce errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual transformation is used, then flexibility and adaptability are maintained, but information loss occurs and costs increase
Solution Approach 1:
The patent replaces the manual mechanical transformation process with an automated computer-based system. The model transformer receives enterprise models as input, processes them through defined rules and mappings, and automatically generates technical business process models, eliminating manual intervention and its associated information loss and cost issues.
Solution Approach 2:
The system creates a systematic copy of the enterprise model structure and semantics, preserving all information through defined mapping rules. The transformer replicates the model's intent and relationships in the target technical modeling language, ensuring complete information transfer without manual adaptation losses.
2Productivity
If manual transformation is used, then adaptability to different models is maintained, but productivity decreases
Solution Approach 1:
The model transformer is designed as a universal system that can handle multiple enterprise model types and transform them into various technical business process models. It incorporates multiple mapping rules and configurations within a single framework, enabling it to process different model formats without requiring separate specialized tools for each case.
Solution Approach 2:
The system performs preliminary configuration of mapping rules and transformations during setup, so that during actual transformation operations, the complex mapping logic is already prepared and executed automatically. This preliminary preparation eliminates the need for manual analysis and adaptation during the transformation process itself.
3Loss of time
If manual transformation is used, then flexibility in handling edge cases is maintained, but time consumption increases
Solution Approach 1:
The transformation system incorporates validation and verification mechanisms that provide feedback on transformation quality. It checks for completeness, consistency, and correctness of the transformed models, allowing automatic correction of common errors and ensuring high accuracy while maintaining fast processing speeds through systematic validation rules.
Data Source
AI summary
A transformation method is described having three phases. The first phase provides a tool and language independent annotation framework to capture semantics of entities as well as their relationships with other entities. The second phase converts the entities on the business level into corresponding entities on a technical level. The conversion preserves the semantics of the entities defined at the business level and transforms them into a representation implementable at the technical level. The third phase reassembles the transformed entities into a cross-organizational business process in a desired technical modeling language and tool.