Machine Learning Code Mapping for Schema Incompatibility
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Solution Overview
Problem
Existing data processing systems face challenges in handling and classifying diverse and inconsistent codes from different organizations and software platforms, making it difficult to import and analyze data across disparate sources due to incompatible data schemas.
Innovation Solution
The implementation of machine learning techniques to classify codes by determining similarities between recognized and unrecognized codes, allowing for accurate mapping and classification regardless of the source schema, using a network environment with a verifier computing environment, code classifier service, and machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data processing systems are used to handle codes from different organizations, then data can be processed using existing schemas, but the system cannot correctly handle new or unrecognized codes and fails when source schemas are incompatible
Solution Approach 1:
The patent introduces an intermediary system consisting of a code classifier service and machine learning model that acts as a mediator between diverse source schemas and the target data warehouse schema. This intermediary automatically classifies and maps codes from different organizations into a unified schema, enabling the system to handle diverse code schemas while maintaining reliable classification through trained machine learning algorithms.
2Measurement precision
If manual classification methods are used for code mapping, then accurate classification can be achieved, but the process becomes time-consuming and cannot scale to handle large volumes of data from multiple sources
Solution Approach 1:
The patent replaces manual mechanical classification processes with an automated machine learning-based code classifier service. The machine learning model automatically performs code classification and mapping tasks that would otherwise require manual intervention, thereby maintaining high classification accuracy while dramatically increasing data processing throughput and scalability.
3Stability of the object's composition
If a unified schema is imposed on all data sources, then data consistency can be achieved, but the system loses the ability to accommodate organization-specific code structures and granularities
Solution Approach 1:
The patent segments the code mapping process into distinct components: a configurable mapping layer that handles organization-specific code structures, a machine learning classification layer that adapts to different code granularities, and a unified output schema. This segmentation allows the system to maintain data consistency in the target schema while preserving the ability to accommodate various source-specific code structures through configurable mapping rules and adaptive classification.
Data Source
AI summary
Disclosed are various embodiments for machine learning based code mapping. A computing device can obtain a set of code identifiers. Then, the computing device can provide a first one of the set of code identifiers to a machine learning model and receive a potential classification from the machine learning model in response. Then, the computing device can determine that the confidence score for the potential classification meets or exceeds a predefined threshold value. In response, the computing device can then create a first mapping pair that links the first one of the set of code identifiers as being associated with the bucket.