Database Table Vector Representation for Relationship Analysis
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Solution Overview
Problem
Existing technologies for measuring table relationships in distributed database management systems are complex and ineffective, making it difficult to quantify and analyze relationships between database tables, especially in large databases with frequent updates.
Innovation Solution
The technology represents database tables as vectors in a multi-dimensional vector space, allowing for the measurement of usage proximity or distance between tables based on their join operations, foreign keys, and views, thereby enabling efficient and accurate analysis of table relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing technologies are used to measure table relationships, then table relationship analysis can be performed, but the analysis becomes complex and ineffective
Solution Approach 1:
The patent replaces complex mechanical/mathematical relationship analysis systems with a semantic embedding system that uses vector representations and cosine similarity. Instead of traditional complex algorithms for measuring table relationships, the system converts table schemas into semantic vectors and uses simple cosine similarity calculations to determine relationship strength, thereby reducing system complexity while maintaining or improving measurement accuracy.
2Productivity
If traditional methods are used for table relationship analysis, then relationships can be identified, but the methods are ineffective especially in large databases
Solution Approach 1:
The system substitutes traditional ineffective relationship analysis methods with semantic embedding and cosine similarity calculations. This replacement enables efficient processing of large databases by converting complex relationship identification into simple vector operations, simultaneously improving both productivity and measurement precision through the mathematical properties of cosine similarity.
Solution Approach 2:
The patent changes the parameter space from traditional relationship metrics to semantic vector embeddings. By representing tables as vectors in a high-dimensional space and measuring relationships through cosine similarity, the system transforms the problem into a computationally efficient parameter space that scales well with database size while providing accurate relationship quantification.
3Measurement precision
If complex analysis methods are applied to large databases, then comprehensive relationship analysis is possible, but the complexity increases and effectiveness decreases
Solution Approach 1:
The patent replaces complex and difficult-to-operate relationship analysis systems with a streamlined semantic embedding approach. The system automatically converts table schemas to vectors and computes cosine similarities, providing accurate relationship analysis without requiring users to manage complex configurations or understand sophisticated algorithms, thereby improving ease of operation while maintaining high measurement precision.
Data Source
AI summary
A computer-implemented method includes representing a plurality of database tables as respective vectors in a multi-dimensional vector space, receiving an indication that a first database table represented by a first vector and a second database table represented by a second vector are related to each other, moving positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space in response to the indication, and grouping the plurality of database tables into one or more table clusters based on positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space.