Glossary-Based Expansion of Abbreviated Database Columns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current database management systems face challenges in accurately expanding column names, which are often abbreviated, leading to labor-intensive, error-prone processes that can result in incomplete or misleading data analysis and security vulnerabilities.
Innovation Solution
A three-phase process involving approximate string matching, extraction of partial expansions, and combination of best partial expansions to generate accurate and domain-specific column name expansions using a glossary-based approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual processes are used to expand column names, then flexibility and adaptability are maintained, but labor intensity increases and error rates rise
Solution Approach 1:
The system performs self-service by automatically expanding column names using glossary terms and machine learning models without requiring manual human intervention. The automated process identifies abbreviations, matches them with glossary definitions, and generates expanded column names autonomously, thereby reducing labor intensity while maintaining consistent accuracy through structured algorithms
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Machine learning models and natural language processing algorithms substitute human operators in the column name expansion task, eliminating manual labor while providing reliable, repeatable results through programmed logic and trained models
2Productivity
If automated expansion methods are used, then productivity increases and labor intensity decreases, but accuracy and precision may deteriorate
Solution Approach 1:
The glossary serves as an intermediary between automated processing and accurate expansion results. The system uses glossary terms as a reference medium to validate and guide the expansion process, ensuring that automated matching produces accurate results by comparing against predefined authoritative definitions rather than relying solely on algorithmic guesses
Solution Approach 2:
The system implements feedback mechanisms where expansion results are validated against glossary terms and can be refined through iterative processing. The machine learning models learn from feedback signals about correct expansions, continuously improving accuracy while maintaining high processing speeds through optimized algorithms
3Measurement precision
If comprehensive glossary terms are used for matching, then expansion accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of column name expansion into distinct phases: abbreviation identification, glossary term matching, and expansion generation. This segmentation allows each component to be optimized independently, managing system complexity by breaking down the overall process into manageable, modular steps that can be processed sequentially
Solution Approach 2:
The system manages complexity by dynamically adjusting matching parameters and thresholds based on the specific context. The machine learning models adapt parameters such as matching sensitivity and glossary term selection criteria to balance accuracy requirements with computational efficiency, optimizing performance without requiring overly complex fixed structures
Data Source
AI summary
A method, a structure, and a computer system for expanding database column names. Exemplary embodiments may include identifying glossary terms that sufficiently syntactically match a column name, extracting partial expansions from the glossary terms, selectively combining a set of the partial expansions, and storing the combined set of partial expansions in association with the column name.