ML Data Standardization for Heterogeneous Schema Mapping
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Solution Overview
Problem
Existing data standardization systems struggle to effectively transform heterogeneous data structures into a canonical format due to the inability to generalize well with unseen data types, as they rely heavily on heuristics or rule-based computational techniques that require manual encoding of various representations.
Innovation Solution
A large-scale data standardization system utilizing machine learning models, such as decision trees and neural networks, to identify and transform data from diverse schemas into a standardized schema, enhancing accuracy and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If heuristics or rule-based computational techniques are used for data standardization, then the system can process data with manually encoded representations, but the system cannot generalize well with unseen data types
Solution Approach 1:
The patent replaces manual rule-based computational techniques with machine learning models that automatically learn data transformation patterns. The ML models are trained on sample data to generalize across unseen data types, eliminating the need for manual encoding of various data representations while improving adaptability to new data formats.
2Measurement precision
If machine learning models are used to automate data transformation, then the system improves accuracy and performance, but the system complexity increases
Solution Approach 1:
The patent performs preliminary training of machine learning models on representative sample data before deployment. This pre-training phase allows the models to learn transformation patterns in advance, so that during actual data standardization, the models can accurately transform unseen data types without requiring complex real-time decision-making logic.
3Productivity
If manual encoding of various data representations is performed, then the system can handle specific data formats, but the processing time and effort increase significantly
Solution Approach 1:
The patent implements self-service through machine learning models that automatically learn and apply data transformation rules without human intervention. The models are trained once on sample data and then autonomously handle the standardization of diverse data formats, eliminating the need for continuous manual encoding and significantly improving processing efficiency.
Data Source
AI summary
At least some embodiments are directed to a large-scale data standardization system. The system receives a set of documents with records formatted according to a third-party data schema. The system utilizes a first machine learning model to select a document from the set of documents. The system utilizes a machine learning model to select data classification labels formatted according to the third-party data schema. The classification labels are associated with a set of records. The system utilizes a second machine learning model to generate a canonical data structure constructed according to a standardized data schema based on the classification labels and the records associated with the classification labels.