Metadata Recommendations Generation via NLP
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current metadata management processes are time-consuming, labor-intensive, prone to human errors, and result in poor quality metadata, especially with the increasing volume and velocity of data.
Innovation Solution
A system and method for generating metadata element recommendations using natural language processing, which acquires metadata via a user interface, processes it to generate candidate table and attribute names, and provides real-time recommendations for standardization, reducing manual intervention and improving metadata quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual metadata management processes are used, then flexibility and adaptability are maintained, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system enables self-service by automatically generating metadata recommendations without requiring manual intervention. The natural language processing system autonomously analyzes unstructured metadata, generates standardized recommendations, and presents them to users for approval, eliminating the need for time-consuming manual metadata management while maintaining high adaptability.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Natural language processing algorithms substitute human analysts, automatically extracting entities, relationships, and attributes from unstructured metadata and generating standardized recommendations, thereby dramatically increasing productivity while reducing time loss.
2Measurement precision
If automated natural language processing is used for metadata generation, then productivity and accuracy are improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer between raw unstructured metadata and final standardized output. The natural language processing system acts as a mediator that automatically transforms unstructured metadata into structured recommendations, handling the complexity of accuracy improvement while presenting simplified results to users through an intuitive interface.
Solution Approach 2:
The system creates standardized copies of metadata recommendations based on analyzed patterns from unstructured data. By generating template-based recommendations that can be reused and adapted, the system achieves high accuracy without requiring users to manually handle complex processing logic, effectively copying successful metadata patterns across different datasets.
3Reliability
If comprehensive metadata analysis is performed, then metadata quality is enhanced, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis by pre-processing and understanding the structure of unstructured metadata before generating recommendations. By预先 analyzing data patterns, relationships, and attributes, the system prepares standardized templates in advance, enabling rapid generation of high-quality metadata recommendations without compromising processing throughput.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the complexity and volume of metadata input. By changing analysis depth, extraction granularity, and recommendation generation parameters adaptively, the system maintains high metadata quality while optimizing processing throughput to prevent bottlenecks in the overall workflow.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for generating metadata element recommendations. For example, the method includes acquiring, by at least one processor and via a user interface, metadata associated with a data store. The method also includes performing natural language processing on the metadata to generate processed metadata, generating a candidate table name and a table description associated with the candidate table name for a table included in the metadata, and generating a first candidate attribute name, an attribute description associated with the first candidate attribute name, and a corresponding data type for each attribute associated with the table. The method also includes generating a second candidate attribute name for each attribute by extracting one or more keywords from the first candidate attribute name, and modifying the user interface to include at least the candidate table name.