Model Pipeline Record Matching for Heterogeneous Data Sources
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data matching techniques struggle to accurately combine data from disparate sources with varying formats and structures, leading to poor data integration and inaccurate predictions due to reliance on strict or fuzzy matching, manual mapping, and rule-based methods.
Innovation Solution
A pipeline of machine learning models that includes an embedding module to generate matching features, a matching model to identify similar records, and a prediction model to refine the matching process, trained together to enhance data integration and reduce complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional strict or fuzzy matching techniques are used to combine data from different data sources, then the matching process is simpler to implement, but the accuracy of data integration deteriorates due to inability to identify nuanced correlations
Solution Approach 1:
The patent segments the data matching process into distinct functional modules: an embedding module that transforms data into vector representations, a matching model that computes similarity scores between records, and a prediction model that refines matching decisions. This segmentation allows each module to specialize in a specific aspect of matching, improving overall accuracy while making the complex process more manageable and interpretable
Solution Approach 2:
The patent introduces embedding vectors as an intermediary representation between raw data and matching decisions. Instead of directly comparing raw data values, the system transforms data into vector space where semantic relationships are preserved, enabling more accurate similarity computation. This intermediary layer bridges the gap between disparate data formats and enables nuanced correlation identification
2Productivity
If manual mapping and rule-based methods are used for data integration, then the system requires less computational resources, but the processing time increases and scalability deteriorates
Solution Approach 1:
The patent replaces manual mapping and rule-based methods with machine learning models that automatically learn matching patterns from data. The embedding module and matching models substitute for human expertise in creating mapping rules, enabling the system to scale to large datasets without proportionally increasing computational overhead. The models learn to identify correlations directly from data rather than requiring explicit rule specification
Solution Approach 2:
The patent transforms the data matching problem from operating on raw data values to operating on embedding vectors in a transformed parameter space. By changing the representation parameters from original data formats to vector embeddings, the system enables more efficient similarity computation and scaling to large datasets while maintaining accuracy
3Reliability
If data sources with varying formats and structures are integrated using traditional methods, then the system maintains simplicity in implementation, but the data quality and reliability of predictions deteriorate
Solution Approach 1:
The patent creates a universal embedding framework that can handle multiple data formats and structures through a common vector representation. The embedding module is designed to transform diverse data types (text, numerical, categorical) into a unified vector space, enabling the same matching and prediction models to work across different data sources. This universality improves prediction reliability by ensuring consistent processing of heterogeneous data while the modular architecture manages complexity
4Measurement precision
If comprehensive data matching is performed to identify all possible correlations between records, then the accuracy improves, but the computational complexity increases from O(2^m1*m2) to a higher value
Solution Approach 1:
The patent performs preliminary embedding of all data records into vector representations before the actual matching process. This preliminary action transforms the data into a format where similarity computation is more efficient. By pre-computing embeddings and using similarity metrics like cosine similarity, the system reduces the computational complexity from exponential O(2^m1*m2) to polynomial time complexity, while still identifying comprehensive correlations through the vector space relationships
Data Source
AI summary
Techniques are disclosed relating to training a prediction model using a pipeline of machine learning models. A system embeds values of records of at least two different data sources within a multi-dimensional embedding space. The system may calculate similarity scores for respective pairs of clusters within the multi-dimensional embedding space. Based on the similarity scores, the system identifies correlations between values of records from the two different data sources. Based on the identified correlations, the system generates matching features and inputs the matching features into a matching model. Based on output of the matching model for the matching features, the system combines similar records from the at least two different data sources, where the combining produces an enhanced data source. The system may then input the enhanced data source into the prediction model. The disclosed record matching techniques may advantageously provide customized matching for prediction models.