AI Semantic Hub for Enterprise Data Ontology and Dependency Mapping

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Solution Overview

Problem

Diverse technology stacks and disparate data stores make it difficult to develop a comprehensive understanding of application data dependencies and usage across an enterprise, hindering communication and information exchange between applications and data stores.

Innovation Solution

Artificial intelligence generates an enterprise ontology and application data usage models by parsing database data definition language, identifying database statements, and mapping them to metadata, creating a semantic hub that shows interrelationships and data dependencies, and generating a data abstraction layer for improved data access and quality.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If diverse technology stacks and disparate data stores are used, then application functionality and data storage capacity are improved, but understanding and aligning information across data stores becomes difficult

Engineering Contradiction:

Improveapplication functionalityVSAvoidinformation alignment

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The patent introduces an intermediary system comprising an ontology repository, data usage model repository, and semantic hub that mediates between diverse data stores and applications. This intermediary layer translates and aligns information from different technology stacks using standardized ontologies and data models, enabling seamless information exchange without losing meaning or context across disparate systems

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent creates universal ontologies and data usage models that serve multiple functions across different technology stacks. These standardized representations can be used by any application regardless of the underlying data store technology, providing a universal interface that maintains information alignment across diverse systems while supporting various application functionalities

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Quantity of substance

If multiple disparate data stores are used, then data storage capacity and application functionality are improved, but developing comprehensive understanding of data dependencies becomes difficult

Engineering Contradiction:

Improvedata storage capacityVSAvoiddata dependencies

Core Design Contradiction:

Quantity of substanceVSDifficulty of detecting and measuring

Solution Approach 1:

The semantic hub acts as an intermediary that automatically discovers and maps data dependencies between multiple disparate data stores. By using standardized ontologies and data usage models, the system can trace and understand relationships across data stores without manual intervention, making data dependencies detectable and measurable even in complex multi-store environments

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback mechanisms where the ontology repository and data usage model repository continuously learn from data access patterns and relationships. This feedback loop enables the system to automatically update its understanding of data dependencies as new relationships are discovered, improving the comprehensive view of data interconnections across multiple storage systems

Inventive Principle:

Principle #23Feedback

3Measurement precision

If manual methods are used to understand data usage, then accuracy of data dependency analysis is improved, but time and computational resources are excessive

Engineering Contradiction:

Improvedata dependency analysis accuracyVSAvoidanalysis time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical analysis methods with automated machine learning-based systems. The system uses trained models to automatically analyze data usage patterns, generate ontologies, and map dependencies across data stores. This substitution maintains high accuracy in data dependency analysis while dramatically reducing the time and human effort required compared to manual methods

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system implements self-service capabilities where the ontology repository and data usage model repository automatically generate and update their contents without manual intervention. The system autonomously discovers data relationships, trains machine learning models, and maintains accurate data dependency information, eliminating the need for continuous manual analysis while preserving measurement precision

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12153615B2Developing object ontologies and data usage models using machine learning

Publication Date: 2024.11.26 SCI APPL INT CORP

AI summary

An enterprise ontology, an application data usage model, and/or cross-application data dependencies may be developed using artificial intelligence. Using pattern recognition and/or information extraction techniques, the artificial intelligence may analyze application source code to identify common DDL or SQL statements to formulate an ontology and/or a usage model for the application. A plurality of application ontologies and/or data usage models may be used to build a semantic hub. The semantic hub may be analyzed to identify data redundancies, data use frequency, potential data quality challenges, and/or data dependencies between applications to produce a data abstraction model that allows legacy applications to communicate with one or more data stores.