Model Adapter for Data Model to User Interface Mapping
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Solution Overview
Problem
Current methods for adapting complex application data models to user interface components are cumbersome, requiring custom model adapters that are difficult to maintain and reuse, especially when the application or user interface evolves, and often result in incomplete or imperfect matches between source and target data models.
Innovation Solution
A fault-tolerant and transparent model adaptation solution that performs loose pattern matching on the application data model, allowing partial matching and discarding or presenting only the portions that can be displayed by the user interface component, eliminating the need for custom adapters and enabling automatic adaptation of any source data model to any target user interface component.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom model adapters are created to interface complex application data models with user interface components, then the user interface component can display the application data model, but the development time and complexity increase significantly
Solution Approach 1:
The patent introduces a model adapter as an intermediary component that automatically bridges the application data model and the user interface component. This adapter is generated automatically based on the data model structure, eliminating the need for manual adapter creation and reducing development time while maintaining the ability to interface complex data models with visualization components
Solution Approach 2:
The patent creates a simplified copy or representation of the application data model that is tailored for the user interface. The model adapter generates a subset or transformed version of the complex data model that matches the expectations of the user interface component, allowing the full complexity of the original model to be preserved while presenting a simplified interface
2Adaptability or versatility
If custom model adapters are created for each application, then the user interface component can be tailored to the specific data model, but the adapters become difficult to maintain and reuse
Solution Approach 1:
The model adapter is designed to be self-generating and self-maintaining. It automatically adapts to changes in the application data model by regenerating based on the current model structure, eliminating the need for manual maintenance. The adapter serves itself by automatically updating when the data model evolves, reducing maintenance burden while preserving tailoring capabilities
Solution Approach 2:
The patent employs parameter-driven generation where the model adapter is defined by a set of parameters or configuration that can be automatically adjusted. When the application data model changes, the adapter regenerates by applying updated parameters, making it easier to maintain and adapt to different applications without rewriting the adapter code
3Measurement precision
If strict data model matching is enforced between source and target models, then the user interface component receives accurate data, but the adaptation process becomes complex and requires manual correction
Solution Approach 1:
The patent applies partial matching instead of requiring complete exact matching between source and target data models. The model adapter identifies and maps the essential attributes and relationships that are sufficient for the user interface component to function correctly, allowing minor discrepancies in the data model structure without requiring manual correction or complex adaptation logic
Data Source
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AI summary
The invention provides a method for generating a display of a source data model on a user interface component being associated with a target data model. The source data model and the target data model comprise a collection of data types (class/object) each comprising a set of named and types attributes. The method comprises : a. for each source data type of the source data model determining a matching target type among said target types in the target data model, b. linking said source type to said matching target type, c. generating a display of said source data model using said link between a source type and a target type. Step a comprises: for each target type in the target data model, determining an attribute score matrix storing a score value for each correspondence between each target attribute in a first attribute set of said target attribute and each source attribute of said source data type, based on a set of predefined matching rules, determining a target type score for the target type based on the attribute score matrix, and determining the matching target type for said source type based on the target type scores associated with the target types of the target model.