Adaptation Layer for Enterprise Data Model Customization
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Solution Overview
Problem
Enterprise-wide applications face challenges in bridging the gap between complex data models used by back-end systems and the simplified models required by end-users, due to customization and extensibility, leading to poor usability and increased integration costs.
Innovation Solution
An adaptation layer is created using industry-specific templates and an integrated development environment (IDE) to automatically or semi-automatically adapt the original data model, providing tailored interfaces that account for customization and configuration data, thereby simplifying data consumption and reducing the complexity of the data model for end-users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If enterprise-wide applications use complex data models optimized for consistency and integrity, then data reliability is improved, but usability deteriorates due to the gap between technical structures and real-world user expectations
Solution Approach 1:
The patent introduces an adaptation layer as an intermediary component between the back-end enterprise application and front-end user interfaces. This adaptation layer contains configuration data and customization information that translates complex technical data models into simplified, domain-specific representations. The intermediary enables users to interact with familiar real-world concepts while the back-end maintains its complex but reliable data structure for consistency and integrity.
Solution Approach 2:
The patent segments the system into distinct layers: the back-end enterprise application with its complex data model, the adaptation layer with configuration and customization data, and the front-end user interfaces with simplified models. This segmentation allows each layer to operate independently with its own optimization criteria - the back-end for reliability and the front-end for usability - while the adaptation layer bridges them.
2Adaptability or versatility
If enterprise applications are customized and extended to match specific customer situations, then adaptability is improved, but device complexity increases leading to expensive integration projects
Solution Approach 1:
The patent implements preliminary action by pre-configuring the adaptation layer with customization information and configuration data during system setup or development phases. Domain-specific models, mappings, and adaptations are prepared in advance based on customer requirements. This preliminary configuration eliminates the need for complex integration projects at deployment, as the adaptation layer is already tailored to the specific customer situation.
Solution Approach 2:
The patent utilizes parameter changes by modifying the data model representation through configuration parameters and customization settings in the adaptation layer. Instead of changing the core back-end system, the adaptation layer adjusts parameters such as data field mappings, validation rules, and presentation formats to match customer-specific requirements. This allows high adaptability while keeping the core system unchanged and manageable.
3Reliability
If the original data model is directly exposed to end-users, then data integrity is maintained, but the complexity of the data model increases making it difficult for users to consume
Solution Approach 1:
The patent creates simplified copies or representations of the original complex data model in the adaptation layer. These copies maintain the essential structure and integrity relationships needed for reliable data handling, but present them in a simplified, domain-specific format that users can easily understand and consume. The adaptation layer acts as a copy that preserves necessary integrity constraints while removing unnecessary complexity.
Data Source
AI summary
Apparatus, systems, and methods may operate to publish one or more stored back end data models accessible to a user interface and a development environment; to receive a configuration context; to generate derived model attributes by filtering, according to the configuration context, back end data attributes associated with the stored back end data models; to derive a set of mapping rules based on the derived model attributes; and store the set of mapping rules to be used to direct run-time data model request transformation activity. Further activities may include receiving an end-user request from a displayed user interface, transforming the end-user request into a transformed request according to the stored set of mapping rules, and transmitting the transformed request to one of the stored back end data models. Additional apparatus, systems, and methods are disclosed.