Cloud Graph Database Automated Relationship Detection
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Solution Overview
Problem
Current graph databases struggle to automatically detect relationships between data elements, relying on user-defined models and lacking scalability, which limits their ability to handle complex data connections and large datasets effectively.
Innovation Solution
The system employs processors to obtain structured and unstructured data, apply name entity recognition analysis, and generate graph models with nodes and edges based on connection information, storing these models in a cloud-based graph database to enhance relationship detection and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If current graph databases rely on user-defined models to define relationships, then the system is simple to implement, but the ability to automatically detect relationships between data elements is poor
Solution Approach 1:
The system performs self-service by automatically detecting relationships between data elements using machine learning models without requiring user intervention. The graph database autonomously analyzes data patterns, identifies entities and relationships, and constructs graph models independently, transforming a manual process into an automated self-service system.
Solution Approach 2:
The patent replaces the mechanical manual process of defining relationships with an automated machine learning-based system. Instead of users manually specifying graph schemas and relationships, the system uses trained models to automatically detect entities, relationships, and graph structures from raw data, substituting human effort with computational intelligence.
2Speed
If graph databases are used to store highly connected data, then relationship-based searches are faster, but the ability to handle large and complex datasets with limited operations is reduced
Solution Approach 1:
The system dynamically adapts its graph model structure based on the characteristics of the data being stored. Rather than using a fixed graph schema, the machine learning models automatically adjust the graph structure, entity types, and relationship definitions to match the specific dataset, enabling the system to handle diverse and complex data while maintaining fast relationship-based searches.
Solution Approach 2:
The patent changes key parameters of the graph database system by using machine learning-derived schemas instead of fixed schemas. The system dynamically adjusts graph parameters such as node types, edge types, and relationship hierarchies based on data analysis, allowing flexible handling of large and complex datasets while preserving the performance benefits of graph-based queries.
3Ease of manufacture
If relational databases with highly-structured tables are used, then data storage is simple, but operations are compute-heavy and memory-intensive with exponential cost
Solution Approach 1:
The system performs preliminary action by pre-computing and storing relationship information in graph structures during data ingestion. Instead of performing heavy computational operations at query time as in relational databases, the system提前 builds optimized graph models with pre-established relationships, enabling fast queries without exponential computational cost.
Data Source
AI summary
Embodiments include systems, methods, articles of manufacture, and computer-readable media configured process data in a structured format and an unstructured format and applying one or more algorithms to detect elements and links between the elements in the data. Embodiments are further configured to generate a graph model comprising nodes comprising the elements and edges comprising the links.