AI Graph Schema Generation from Relational Data for Complex Queries
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Solution Overview
Problem
Existing data querying technologies face challenges in query optimization, scalability, and complexity when dealing with large-scale graph-based data, particularly in creating efficient graph schemas for databases, which is a time-consuming manual process.
Innovation Solution
An automated graph schema generation system using AI and a graph analytics engine that automatically identifies and generates vertices and edges from relational data, allowing users to interact dynamically through a graphical interface, reducing manual effort and enhancing scalability and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual graph schema creation is used, then schema accuracy can be ensured, but time consumption increases significantly
Solution Approach 1:
The system enables self-service automated graph schema generation that allows users to create accurate graph schemas without manual intervention. The computational model automatically analyzes relational database schemas and generates corresponding graph schemas, eliminating the time-consuming manual creation process while maintaining schema accuracy through intelligent algorithms.
Solution Approach 2:
The patent replaces the mechanical manual process of graph schema creation with an AI-based computational model. Instead of manually mapping relational tables to graph structures, the system uses automated algorithms to analyze database schemas and generate graph representations, significantly reducing time consumption while preserving accuracy.
2Productivity
If automated graph schema generation is implemented, then time consumption is reduced, but system complexity increases
Solution Approach 1:
The system introduces a computational model as an intermediary between the relational database schema and the graph schema output. This intermediary layer automatically performs the complex transformation logic, handling the complexity internally while presenting a simple interface to users. The computational model acts as a mediator that manages the complexity of schema conversion algorithms.
Solution Approach 2:
The system creates a computational model that copies and adapts relational database schema structures into graph schema formats. By using template-based approaches and structured transformation rules, the system replicates the essential elements of relational schemas in graph form through automated copying and adaptation, reducing the need for complex custom transformation logic.
3Ease of operation
If existing tabular databases are queried directly, then data access is simple, but query performance degrades for complex graph relationships
Solution Approach 1:
The system performs preliminary action by pre-computing and storing graph representations of relational database schemas. Before actual queries are executed, the computational model transforms the relational schema into an optimized graph schema that prepares the data structure for efficient graph traversal. This preliminary transformation enables fast query execution while maintaining ease of data access through familiar database interfaces.
Solution Approach 2:
The system implements dynamic adaptation by providing different query interfaces depending on the operation type. For simple data access, it maintains the familiar tabular database interface. For complex graph relationship queries, it dynamically switches to optimized graph traversal mechanisms, allowing the system to adapt its performance characteristics to the specific query requirements while preserving ease of operation.
Data Source
AI summary
This application is directed to automated graph schema generation. A schema generation method includes receiving a graph schema having vertices and edges associated with a plurality of data tables. The method also includes automatically generating, based on the plurality of data tables and the graph schema, one or more suggestions using a computational model. The one or more suggestions identify one or more vertices and/or one or more edges. The one or more suggestions include a respective suggestion that identifies a respective subset of the one or more vertices and/or the one or more edges. The method further includes receiving a first user input that is a selection of the respective suggestion from the one or more suggestions. The method further includes updating, based on the first user input, the graph schema. The method further includes outputting the updated graph schema.