Heterogeneous Data Linking via Vector Sub-segmentation
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Solution Overview
Problem
Existing probabilistic record matching methods face challenges with noise, performance, and scalability issues when linking disparate data sources lacking unique identifiers, and require manual intervention to determine algorithms and parameters for effective data linking.
Innovation Solution
The development of a system using advanced machine-learning, data-mining, and artificial intelligence algorithms, in conjunction with private and publicly available dictionaries, to probabilistically match records across heterogeneous data sources, enabling real-time data linking and de-duplication, even in databases with different formats and low data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If probabilistic record matching methods are used to link heterogeneous data sources, then data linking capability is improved, but noise and performance issues worsen
Solution Approach 1:
The patent segments the data linking process into multiple independent modules: data cleaning module, feature extraction module, similarity calculation module, and machine learning classification module. Each module handles specific aspects of the linking process, isolating noise and errors to individual segments rather than propagating them system-wide. This modular segmentation improves reliability while maintaining adaptability across heterogeneous data sources.
Solution Approach 2:
The patent introduces intermediary components including data normalization layers and feature transformation modules that mediate between heterogeneous data sources and the matching algorithm. These intermediaries standardize data formats, handle inconsistencies, and filter noise before data enters the core matching process, thereby improving both reliability and performance without sacrificing linking capability.
2Measurement precision
If advanced machine-learning and data-mining algorithms are used, then data linking quality is improved, but system complexity worsens
Solution Approach 1:
The patent applies preliminary data cleaning, normalization, and feature extraction before the data enters the machine learning algorithms. By preparing data in advance with automated preprocessing pipelines, the system reduces the complexity burden on the core algorithms while maintaining high linking quality. The preliminary actions handle routine complexity, allowing sophisticated algorithms to focus only on the critical matching decisions.
Solution Approach 2:
The system incorporates self-training and automated parameter optimization capabilities where the machine learning models automatically adjust their parameters based on feedback from the data linking results. This self-service mechanism reduces the need for manual tuning and configuration, thereby managing system complexity while sustaining high data linking quality through adaptive optimization.
3Productivity
If manual intervention is reduced, then productivity is improved, but measurement precision worsens
Solution Approach 1:
The patent implements feedback loops where the system continuously monitors matching results and uses them to refine its algorithms and parameters. The machine learning models are trained on labeled data and continuously improved through feedback from actual linking performance, enabling the system to maintain high precision automatically without manual intervention. This closed-loop feedback mechanism bridges the gap between automated productivity and precision.
Solution Approach 2:
The system dynamically adjusts parameters such as similarity thresholds, weighting factors, and algorithm selection based on the characteristics of the input data and performance metrics. These automatic parameter changes allow the system to maintain high matching accuracy across different data scenarios without requiring manual recalibration, thereby sustaining both productivity and precision in automated operation.
Data Source
AI summary
A method for linking records (related to an entity) from separate databases may include extracting a first record from a first database as a first vector, extracting a second record from a second database as a second vector, generating first and second sub-vectors for the first and second vectors, where each sub-vector includes quality features from the respective vector, pre-processing the first and second sub-vectors using domain knowledge, calculating a distance assessment classifier based on the first and second sub-vectors, and determining whether the distance represented by the distance assessment classifier is greater than a threshold. If the distance is greater than the threshold, the records may be linked; if not, the method extracts additional records and repeats after generating first and second sub-vectors until the distance is greater than the threshold. A system for linking records is also disclosed.