Automated Data Field Mapping via Vector Similarity
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Solution Overview
Problem
The challenge lies in automating the process of mapping data fields from one organizational application to another, particularly in scenarios where different organizations use the same ATS or other software applications differently, leading to increased error rates, inconsistency, and higher integration costs due to the need for human labor in manually matching and understanding data fields across systems.
Innovation Solution
A computer-implemented method that generates mathematical representations of field keys and data in both applications, uses similarity functions to match data fields, and optimizes the mapping process to automate the translation of data from one application schema to another, including handling different field types and languages, thereby reducing human intervention and improving data consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual mapping of data fields is performed by human labor, then data field matching can be performed with understanding of data semantics, but error rates increase, quality consistency decreases, and time and integration cost increase
Solution Approach 1:
The patent replaces the mechanical human labor process with an automated computer-based system that uses mathematical representations (vectors) of data fields and similarity functions to automatically match fields between applications. This substitution eliminates human error while maintaining matching accuracy through algorithmic comparison of field semantics and data characteristics.
Solution Approach 2:
The patent transforms data fields into mathematical parameters (vectors) that can be computationally compared. By converting semantic data field information into mathematical representations with specific parameters (similarity scores, vector dimensions), the system enables automated, consistent comparison without human intervention, thereby improving both accuracy and reliability.
2Measurement precision
If manual mapping of data fields is performed by human labor, then data field matching can be performed with understanding of data semantics, but time consumption and integration cost increase
Solution Approach 1:
The patent replaces time-consuming manual mapping with automated computational processes that generate mathematical representations and execute similarity comparisons instantaneously. The system processes multiple data fields simultaneously through algorithmic operations, reducing integration time from hours or days of manual work to minutes or seconds of automated computation.
Solution Approach 2:
The patent performs preliminary actions by pre-computing mathematical representations (vectors) of data fields and storing them for future comparison. This preparation work is done automatically before the actual mapping task, enabling rapid matching when needed and significantly reducing the time required for integration operations.
3Productivity
If automated mapping is implemented, then human error is reduced and integration cost decreases, but the system requires complex mathematical representations and similarity functions
Solution Approach 1:
The patent introduces mathematical representations (vectors) as intermediary structures that bridge the gap between different application data fields. These vectors serve as a universal language that simplifies the comparison process, transforming a complex many-to-many mapping problem into a series of straightforward similarity calculations between vector representations.
4Adaptability or versatility
If different organizations use the same application differently with custom field names, then organizational flexibility is maintained, but mapping complexity and error rates increase
Solution Approach 1:
The patent handles organizational flexibility by transforming diverse field names and data structures into standardized mathematical parameters (vectors). This parameter transformation allows the system to compare fields from different organizations on a common numerical basis, maintaining each organization's custom naming conventions while achieving accurate automated matching through mathematical similarity comparison.
Data Source
AI summary
Mapping data from a first computer application to a second computer application by obtaining a second organization's application schema with data, generating a mathematical representation of the field keys and the data in the data fields of the second organization's application, extracting data from a first organization's application; processing the data from the first organization's application and data from the second organization's application, and mapping data from the first organization's application into the second organization's application schema.