Entity Representation Splitting via Bloat Index Calculation
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Solution Overview
Problem
Database systems face challenges in accurately delinking records that were initially linked due to ambiguity, leading to erroneous links over time, requiring techniques to identify and correct these errors without human interaction.
Innovation Solution
The system calculates a bloat index using field inconsistency weights derived from probabilities, determining if linked records should be delinked based on a threshold, and identifies cleave points to split entity representations into separate entities, all without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If records are linked based on initial matching criteria, then data processing efficiency is improved, but erroneous links accumulate over time reducing accuracy
Solution Approach 1:
The system performs preliminary actions by establishing initial links between records based on matching criteria, then later performs corrective actions by identifying and delinking erroneous connections. The bloat index calculation is performed periodically to detect and correct accumulated errors before they propagate further.
Solution Approach 2:
The system implements feedback mechanisms by calculating the bloat index to assess link accuracy, comparing it against thresholds, and automatically delinking records when errors are detected. This closed-loop feedback allows the system to correct erroneous links while maintaining efficient processing.
2Productivity
If automated delinking is implemented without human interaction, then operational efficiency is improved, but complexity of the system increases
Solution Approach 1:
The system performs self-service by automatically calculating the bloat index, identifying erroneous links, and delinking records without human intervention. The automated threshold comparison and decision-making processes eliminate the need for manual review while managing system complexity through standardized algorithms.
Solution Approach 2:
The system manages complexity by transforming the delinking decision into a parameter-based approach, where the bloat index and threshold values automatically determine when delinking should occur. This parameter-driven automation simplifies the decision logic while maintaining high operational efficiency.
3Measurement precision
If iterative correction of links is performed as new data is added, then data quality is improved, but processing time increases
Solution Approach 1:
The system implements periodic action by calculating the bloat index at scheduled intervals or when specific conditions are met, rather than continuously. This periodic correction approach maintains data quality through iterative improvements while minimizing processing time by acting only when necessary.
Solution Approach 2:
The system applies partial action by performing delinking operations only when the bloat index exceeds predetermined thresholds, rather than correcting all links uniformly. This selective correction approach improves data quality where needed while reducing unnecessary processing time.
Data Source
AI summary
Disclosed is a system for, and method of, determining whether records and entity representations should be delinked. The system and method need no human interaction in order to calculate parameters and utilizing formulas used for the delinking decisions.