Ontology-Based Data Model Validation for Semantic Independence
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Solution Overview
Problem
In large enterprises, data is often modeled differently by various users for different applications, leading to incompatible models that change over time, making it challenging to manage and maintain consistent data across systems.
Innovation Solution
The method employs an ontology to describe and manage data models, using a validation schema to validate objects derived from data-centric components, allowing for semantic-independent implementation and transformation of data without affecting other content structures, enabling neutral management of data models across different user needs and applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different users model the same data in different ways for different applications, then each user can optimize data for their specific needs, but the data models become incompatible and difficult to manage
Solution Approach 1:
The patent segments data models into modular components that can be independently defined, validated, and managed. Each data model is broken down into reusable elements that can be combined differently for various applications, allowing customization without creating entirely separate incompatible models.
Solution Approach 2:
The patent introduces an intermediary validation mechanism that mediates between different data model representations. This validation layer ensures compatibility and consistency across different user-specific data models, acting as a bridge that allows diversity while maintaining manageability.
2Adaptability or versatility
If data models change over time to meet evolving user needs, then data can remain relevant and useful, but consistency and compatibility across systems become difficult to maintain
Solution Approach 1:
The patent implements preliminary validation schemas that are defined in advance to govern future data model changes. These pre-established validation rules ensure that as data models evolve to meet new requirements, they automatically conform to consistency standards, preventing incompatible changes before they occur.
Solution Approach 2:
The patent incorporates feedback mechanisms through validation that monitor data model changes over time. When data models are modified to meet evolving needs, the validation system provides feedback to ensure changes maintain consistency with established standards, allowing evolution while preserving stability.
3Productivity
If a single data repository is used by multiple users, then data centralization is achieved, but different user models create incompatibility issues
Solution Approach 1:
The patent creates universal data model structures that can serve multiple user needs simultaneously. The same core data repository and model definitions are designed to be multi-functional, supporting different application requirements through configurable validation schemas rather than requiring separate models for each user.
Data Source
AI summary
A method of maintaining data described in a plurality of data models. An ontology is used to describe the data models. The data models are managed using the ontology and using a validation schema to validate object(s) governed by the ontology and derived from data-centric component(s) of content that has a semantically independent structure. Management of the data models is neutral relative to implementation of the content.