Stable Record Identifiers for Database Redundancy Management
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Solution Overview
Problem
Combining data from different sources in business databases is burdensome, time-consuming, and costly due to redundancy and the need for frequent optimization, which can lead to lossy processes when consolidating records.
Innovation Solution
A software application that uses clustering techniques to group similar records with stable identifiers, maintaining both original records and their identifiers during database updates, avoiding the consolidation of records into a single surviving record and allowing for accurate reassignment of identifiers during updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If records are consolidated into a single surviving record during database optimization, then redundancy is reduced, but data integrity is compromised due to lossy processes
Solution Approach 1:
The patent creates a copy of the record grouping information (cluster identifiers) separate from the original records. This allows the system to maintain redundant-free data by storing only the necessary grouping metadata while preserving all original record data, eliminating the need for lossy consolidation processes.
Solution Approach 2:
The patent segments the database into two distinct components: the original record data and the cluster identifier metadata. This segmentation allows independent management of each component, enabling redundancy reduction in the metadata while preserving complete record integrity without requiring consolidation of the actual record data.
2Productivity
If clustering techniques are used to group similar records, then data organization is improved, but the process becomes time-consuming and costly
Solution Approach 1:
The patent performs clustering analysis and assigns cluster identifiers to records in advance, before any database updates occur. This preliminary organization allows the database to maintain efficient grouping information without requiring time-consuming re-clustering operations during subsequent update cycles, significantly reducing optimization time.
Solution Approach 2:
The system maintains cluster identifier information automatically as part of the database structure, allowing it to self-update and self-maintain the grouping information without requiring external intervention or complex reprocessing. The cluster identifiers are preserved and updated automatically during database operations.
3Measurement precision
If records are frequently updated and reoptimized, then data accuracy is maintained, but the frequency of updates compromises system stability
Solution Approach 1:
By maintaining a separate copy of cluster identifier information rather than reprocessing the entire record set, the system can update and verify data accuracy without triggering full reoptimization cycles. This copying approach allows incremental updates to accuracy while maintaining overall database stability.
Solution Approach 2:
The patent implements periodic reoptimization only when necessary, rather than continuous reprocessing. The cluster identifier structure allows the system to maintain accuracy through targeted updates while avoiding frequent full reoptimization cycles that would compromise database stability.
Data Source
AI summary
The present disclosure relates to optimizing one or more database tables that may include one or more redundant records. Records are clustered and assigned stable identifiers. In this manner, the underlying records within a cluster are not removed or deleted. As updates to the database are made, new clustering analyses are performed using the underlying records and any updates made. Newly identified clusters are reassigned stable identifiers.