Cascading Data Record Matching Algorithm for Disparate Databases
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Solution Overview
Problem
Merging data records from disparate databases with no unique key matches is challenging due to inconsistent data representations, requiring advanced matching models to improve the probability of correct matches while balancing confidence and number of matches.
Innovation Solution
A cascading matching algorithm that iteratively merges data records using multiple fields, applying a combinatorial function and Bayesian classification to generate a compatibility index for ranking matches, and utilizing business rules to handle variations in data fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If merging is performed on a single field, then the number of matched records increases, but the confidence rating of matches decreases
Solution Approach 1:
The patent combines multiple fields (company name, address, phone number, etc.) into a composite matching criterion. Instead of relying on a single field match, the system evaluates records across multiple fields simultaneously, merging the information from each field to determine overall match confidence. This resolves the contradiction by allowing the system to maintain high confidence ratings while still identifying a substantial number of matches through the combined weight of multiple field comparisons.
Solution Approach 2:
The system dynamically adjusts matching parameters and weights assigned to different fields based on their reliability and importance. By changing the parameters of the matching algorithm to accommodate varying field qualities, the system can optimize between finding more matches and maintaining high confidence ratings, depending on the specific data quality present in each dataset.
2Reliability
If merging is performed on multiple fields, then the confidence rating of matches increases, but the number of matched records decreases
Solution Approach 1:
The system performs partial matching by not requiring all fields to match perfectly. Instead, it allows matches to be established based on a sufficient subset of fields, where the combined confidence from partial matches can still reach threshold levels. This approach recovers some records that would be lost through strict multi-field matching while maintaining acceptable confidence ratings.
3Measurement precision
If advanced matching models with n-field comparison are used, then the probability of correct matches improves, but the system complexity increases
Solution Approach 1:
The complex matching process is segmented into distinct stages: initial filtering based on key fields, intermediate scoring across multiple fields, and final confidence calculation. This segmentation breaks down the complex n-field comparison into manageable steps, reducing system complexity while maintaining high matching accuracy through progressive refinement.
Data Source
AI summary
In an illustrative embodiment, systems and methods for performing cascading matching of data records from disparate data sources comprise identifying matches using at least one uniquely identifying data field and at least one additional data field shared by a first data set and a second data set. Potential matches may be resolved through calculating differences between one or more shared data fields of a matched data record of the first data set and both a first matched record and a second matched record of the second data set, and determining a best match through analyzing the calculated differences. Unmatched records may be iteratively matched using a different uniquely identifying data field and/or different at least one additional data field(s).