Record Matching System for Database Search and Fragmented Record Detection
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Solution Overview
Problem
Traditional database search methods often return incomplete results due to variations in identifier representation and mistyped data, leading to inaccurate matching and the creation of duplicate records, resulting in fragmented files.
Innovation Solution
A record-matching computing system that generates identifier scores and compound scores for numerical, name, address, and date identifiers, using machine learning models to determine match classifications and detect fragmented files by identifying connected components in a graph of matching records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional exact matching methods are used for database searches, then search simplicity is maintained, but search accuracy deteriorates due to variations in identifier representation and mistyped data
Solution Approach 1:
The patent changes the matching parameter from binary exact match to continuous similarity scores. Instead of returning only records with identical identifiers, the system calculates similarity scores (e.g., 0-1 range) that measure the degree of matching between query and database records, allowing flexible threshold-based retrieval of partially matching records.
Solution Approach 2:
The patent introduces an intermediary scoring mechanism between the query and database records. The similarity score acts as a mediator that quantifies the degree of matching, enabling the system to handle variations in identifier representation and mistyped data without requiring complex manual matching rules.
2Reliability
If traditional exact matching methods are used, then data entry requirements are simple, but duplicate records are created leading to fragmented files
Solution Approach 1:
The patent performs preliminary similarity assessment before creating new records. When incoming data is processed, the system first calculates similarity scores against existing records to determine if a match exists, and only creates a new record if the similarity score falls below the threshold, preventing duplicates before they are created.
Solution Approach 2:
The patent implements feedback mechanisms where the similarity scoring system continuously monitors and provides information about potential duplicates. This feedback allows the system to adjust data entry processes and identify fragmented records that should be merged, improving data quality over time.
3Loss of information
If fragmented files are present in the database, then data completeness is reduced, but detecting and merging them increases computational complexity
Solution Approach 1:
The patent segments the database into manageable units for analysis by calculating similarity scores for individual record pairs. This segmentation allows the system to process and identify fragmented records systematically through graph-based approaches, where records are nodes and similarity relationships are edges, making the complex task of detecting fragmentation more manageable.
Solution Approach 2:
The patent transforms the two-dimensional database structure into a multi-dimensional similarity space. By calculating similarity scores across multiple identifier dimensions (name, address, date, numerical identifiers), the system creates a rich representation of record relationships that enables efficient detection of fragmented files through graph analysis.
Data Source
AI summary
In some aspects, a record-matching computing system for matching records to facilitate database search and fragmented records detection is provided. The record-matching computing system is configured to search for a data record that matches a query record. The record-matching computing system retrieves a reference record from data records and generates multiple identifier attributes for the query record and reference record, including identifier scores and compound scores. Each identifier score measures a degree of matching between the corresponding identifiers in the query record and reference record. A compound score is generated by combining two or more identifier scores. The record-matching computing system applies the identifier attributes to a machine learning model configured to predict a match classification based on input identifier attributes for a pair of data records. The record-matching server can identify the reference records as a match to the query record based on the match classification indicating a match.