Data Model Normalization via Semantic Entity Analysis
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Solution Overview
Problem
Data models often develop redundancies and inefficiencies due to growth and mergers, making it challenging to identify and remove semantic similarities and differences, which hinders optimization.
Innovation Solution
A computer-implemented method for normalizing data models by refining entity class names and attributes to conform to a naming convention, determining semantic meanings, and comparing these to optimize the data model by removing redundancies and improving structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data models grow to accommodate business growth and sophistication, then the data models can represent more complex business systems, but redundancies and deep levels of recursive hierarchies develop causing inefficiencies
Solution Approach 1:
The patent segments the complex data model into smaller, more manageable components by identifying and separating redundant elements. The system divides the data model structure into distinct entity classes and attributes, allowing individual optimization of each segment while maintaining overall functionality. This segmentation enables the removal of deep recursive hierarchies by breaking them into flatter, more efficient structures.
Solution Approach 2:
The patent extracts redundant elements and inefficiencies from the data model through automated analysis. The system identifies duplicate attributes, redundant entity classes, and unnecessary hierarchical levels, then removes or consolidates these elements. This extraction process eliminates redundancies while preserving the essential business logic and data relationships.
2Adaptability or versatility
If two or more data models are merged into one, then business integration is achieved, but inefficiencies and redundancies are introduced
Solution Approach 1:
The patent merges multiple data models into a unified structure while automatically resolving conflicts and redundancies. The system combines entity classes and attributes from different source models, using semantic analysis to identify and consolidate duplicate elements. This merging process integrates business functionality while eliminating the redundancies that typically arise from model consolidation.
Solution Approach 2:
The patent transforms the parameters of merged data models by standardizing naming conventions, data types, and structural relationships. The system applies parameter changes to align different models, such as unifying attribute names, consistent hierarchical levels, and standardized entity class definitions. This parameter standardization reduces inefficiencies introduced during merging.
3Manufacturing precision
If data model names and structure are closely integrated with business semantics and linguistics, then business accuracy is improved, but identification and removal of semantic similarities and differences becomes challenging
Solution Approach 1:
The patent introduces an intermediary semantic analysis layer between the business semantics and the data model structure. This intermediary system uses natural language processing and semantic analysis techniques to interpret business terminology and translate it into standardized data model elements. The intermediary enables automated detection of semantic similarities and differences by comparing the underlying meanings rather than just surface-level names, making it easier to identify redundancies while preserving business accuracy.
Data Source
AI summary
A name of one or more entity classes of the data model may be refined to conform to a naming convention. A semantic meaning of each of the names and one or more attributes of each entity class may be determined. It may be determined that the name of a first entity class is semantically similar to the name of a second entity class based on a semantic distance between the semantic meaning of the names, where a substantial similarity may be determined between the first entity class and the second entity class by comparing the semantic meaning of the one or more attributes of the first entity class to the semantic meaning of the one or more attributes of the second entity class. The data model may be normalized based on the substantial similarity.