ML Column Mapping for Accurate HCM Data Ingestion
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Solution Overview
Problem
Current human capital management systems face challenges in efficiently and accurately mapping data from client files to corresponding database columns during data migration, particularly for small to medium-sized businesses, leading to inefficiencies and potential data integrity issues.
Innovation Solution
Utilizing a combination of machine learning models, including column name-based, data type-based, and decision tree algorithms, to predict and validate column header names, with continuous retraining based on client feedback to enhance accuracy and ensure data integrity during ingestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data mapping methods are used during data migration, then the process is simpler to implement, but mapping accuracy and data integrity deteriorate
Solution Approach 1:
The patent introduces machine learning models as intermediary components between the source data files and the target database. These models act as smart mediators that analyze column headers, data types, and patterns to automatically suggest and validate mappings, thereby improving mapping accuracy without requiring complex manual configuration processes
Solution Approach 2:
The system enables self-service data mapping through automated machine learning models that independently analyze and map columns without extensive human intervention. The models learn from historical mapping data and continuously improve their accuracy, allowing the system to serve itself rather than relying on complex manual processes
2Productivity
If manual data mapping is performed, then data integrity can be verified, but the time and effort required increases significantly
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on historical mapping data before actual data ingestion occurs. The models are prepared in advance to quickly and accurately map columns during the actual data migration process, eliminating the need for time-consuming manual mapping during production
Solution Approach 2:
The system replaces the mechanical manual data mapping process with automated machine learning-based mapping. This substitution eliminates the need for human operators to manually analyze and map each column, dramatically reducing data setup time while maintaining or improving mapping quality through intelligent algorithms
3Reliability
If multiple machine learning models are used for validation, then mapping reliability improves, but processing complexity increases
Solution Approach 1:
The patent merges multiple machine learning models into a unified validation framework. Different models (e.g., for column header matching, data type validation, and pattern recognition) are combined and orchestrated to work together, improving data integrity through comprehensive validation while managing complexity through integrated model coordination
Data Source
AI summary
Intelligent data ingestion is provided. A determined column header name of a selected column in an imported data file is mapped to a predicted corresponding column header name of a particular column in a database corresponding to a human capital management application using a plurality of machine learning models. It is determined whether the predicted corresponding column header name output by each respective machine learning model of the plurality of machine learning models matches. In response to determining that the predicted corresponding column header name output by each respective machine learning model of the plurality of machine learning models does match, the predicted corresponding column header name of the particular column in the database is utilized as a target column name for the determined column header name of the selected column in the imported data file.