Database Matching Using Reference Position Masks
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Solution Overview
Problem
Existing database systems face inefficiencies in rapidly matching large datasets, as full match analysis between sample records and database records is computationally intensive, and pre-screening processes often rely on specific algorithm peculiarities or data characteristics, limiting their applicability.
Innovation Solution
A method and system that define reference positions within data records, create key patterns, combine data into key values, associate record identifiers, and use enrollment and sample masks to efficiently identify possible matches by determining the number of occurrences of record identifiers, allowing for fast candidate-matching with additional effort during record enrollment rather than search processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full match analysis is performed between sample record and every database record, then matching accuracy is improved, but computational workload and processing time increase significantly
Solution Approach 1:
The database matching process is divided into two distinct phases: pre-screening phase that uses key values and reference positions to quickly identify candidate matches, and detailed matching phase that performs full analysis only on those candidates. This segmentation allows the system to maintain high accuracy while dramatically improving processing speed by avoiding full analysis of all database records.
Solution Approach 2:
The system performs preliminary actions during database record enrollment by pre-computing key values from specific reference positions and storing them in an index structure. When a sample record needs matching, these pre-computed key values enable rapid filtering to identify candidate matches before performing detailed analysis, thus preparing the groundwork for efficient accurate matching.
2Productivity
If pre-screening processes are used to reduce computational workload, then processing speed is improved, but applicability is limited due to reliance on specific algorithm peculiarities or data characteristics
Solution Approach 1:
The key value generation mechanism is designed to be application-agnostic and data-type independent. The same pre-screening framework can be applied to various record types (biometric data, text documents, images, audio) and matching algorithms without modification. The reference positions and key patterns can be configured differently for each application domain, making the system universally applicable while maintaining fast processing.
3Quantity of substance
If key values are created from reference positions in data records, then memory utilization is improved, but device complexity increases due to enrollment masks and sample masks
Solution Approach 1:
Instead of storing complete database records in memory for rapid access, the system creates compact copies in the form of key values derived from specific reference positions. These key values serve as simplified representations that enable fast comparison. The enrollment masks and sample masks are simple binary or weighted structures that indicate which reference positions are significant, adding minimal complexity while enabling efficient memory usage.
Data Source
AI summary
A method of improving the speed with which a sample data record can be matched against records in a database comprises defining a list of possible key values (430), testing those key values against the sample and, for each record in the database, counting the number of key values that match both the record and the sample at reference positions selected by a mask. A list of possible matches is then selected on the basis of that count, for more detailed matching or analysis. Such a method provides very fast matching at the expense of some additional effort when registering a new record within the database.