Database Table Vector Revision for Metadata Association
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Solution Overview
Problem
Current data governance methods face inaccuracies in associating business metadata with technical metadata due to misinterpretation of table correlations by machine learning algorithms, particularly when custom attributes are not properly weighted based on their reference counts across multiple tables.
Innovation Solution
The method involves revising the vector representation of database tables by incorporating custom attribute reference counts, where the count of a keyword in a table is adjusted by multiplying it with the reference count of the custom attribute, thereby improving the accuracy of table associations and correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning algorithms are used to associate business metadata with technical metadata, then automation is improved, but measurement precision deteriorates due to misinterpretation of table correlations
Solution Approach 1:
The patent introduces custom attributes as intermediary elements that bridge business metadata and technical metadata. These custom attributes serve as mediators that capture domain-specific knowledge and relationships, allowing machine learning algorithms to associate metadata more accurately by referencing these intermediate representations rather than directly correlating tables.
Solution Approach 2:
The patent modifies the parameter representation by incorporating custom attribute reference counts into the vector representation of tables. This parameter change transforms the input features of the machine learning algorithm, providing more meaningful numerical representations that reflect the actual importance and relationships of custom attributes, thereby improving correlation accuracy.
2Measurement precision
If custom attributes are incorporated into table vectors, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing custom attribute reference counts in the database schema. This preprocessing step prepares the data in advance, so that when vectors need to be revised for metadata association, the system can directly utilize these pre-computed values without performing complex real-time calculations, thereby reducing operational complexity.
Solution Approach 2:
The patent segments the vector revision process into distinct components: extracting custom attribute information, retrieving reference counts, calculating weighted contributions, and updating the vector. This segmentation allows each step to be handled independently and efficiently, reducing the overall complexity of the process while maintaining high measurement precision.
Data Source
AI summary
A computer-implemented method includes: reading a vector of a first table in a database, the vector including counts of a plurality of keywords in the first table, the plurality of keywords including a first keyword and a second keyword; determining a first custom attribute describing the first table, the first custom attribute having a vector including counts of at least a first portion of the plurality of keywords in the first table; determining a multiplier of the first custom attribute, the multiplier being a number of other tables that reference the first custom attribute; and revising the vector of the first table based on the first custom attribute.