API Data Element Matching for Scalable Cross-Service Integration
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Solution Overview
Problem
Manual mapping of data elements between internal and external systems is impractical and error-prone as differences in data element names and formats increase, especially when interfacing with multiple external systems.
Innovation Solution
A system and method using machine learning models to automatically match non-standard data elements from external systems to standardized data elements within an organization, utilizing APIs datasets, preprocessing, and natural language processing techniques to enhance accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual mapping of data elements is used to interface with external systems, then data element compatibility can be achieved, but the process becomes impractical and error-prone as the number of external systems increases
Solution Approach 1:
The system enables automatic self-mapping of data elements through machine learning models that autonomously match non-standard data elements from external systems to standardized internal data elements, eliminating the need for manual mapping intervention while maintaining high compatibility and accuracy
Solution Approach 2:
The patent replaces the manual mechanical mapping process with an automated machine learning-based system that uses trained models to perform data element matching, substituting human effort with intelligent algorithms that scale efficiently across multiple external systems
2Adaptability or versatility
If manual mapping is used to handle differences in data element names and formats, then data integration can be achieved, but the process becomes increasingly complex and error-prone
Solution Approach 1:
The system introduces standardized data elements as an intermediary layer between external non-standard data elements and internal systems, using machine learning models to automatically map and translate between different data formats and naming conventions, thereby simplifying the integration process while maintaining adaptability
Solution Approach 2:
The patent transforms the complex manual mapping process by changing the parameters of data element representation through standardized formats and using machine learning models to automatically adjust and map different data element parameters across systems, reducing process complexity while maintaining integration capability
3Adaptability or versatility
If the number of external systems increases, then system versatility improves, but manual mapping becomes impractical and error-prone
Solution Approach 1:
The machine learning-based automatic mapping system enables self-service data element matching that scales with the number of external systems, maintaining consistent accuracy levels without requiring additional manual intervention as system connectivity expands
Solution Approach 2:
The system implements feedback mechanisms where the machine learning models are trained on mapping data and continuously improved through evaluation and retraining, ensuring that mapping accuracy is maintained and enhanced as the number of external systems increases
Data Source
AI summary
A method may include: collecting application programming interface (API) datasets, each API dataset identifying data elements used by the API, descriptions of the data elements, and datatypes for the data elements; splitting the API datasets into a training API dataset and a validation API dataset; labeling the data elements in the training API dataset using standard data elements that are defined by an organization for use by the organization; training a machine learning model with the training API dataset and the labels, wherein the machine learning model is trained to match the data elements to the standard data element; and integrating the machine learning model into a workflow, The machine learning model matches a non-standard data element in a new API dataset to one of the standard data elements. A downstream system uses the non-standard data element in the same manner as the matching standard data element.