Self-configuring API Adapter for Relational Schema Mapping
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Solution Overview
Problem
Existing methods for mapping endpoints exposed via API interfaces to relational schemas are inefficient due to schema changes, lack of machine-readable documentation, and human error, making accurate and timely mapping challenging.
Innovation Solution
A self-configuring adapter that uses processors to sample data from endpoints, infer data types and relational structures, and generate configuration profiles, allowing for automatic mapping and querying in relational query languages like SQL without relying on human documentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual mapping is performed by a person, then the mapping can be created, but it is time consuming and risks introduction of human error
Solution Approach 1:
The system performs self-service by automatically generating the mapping between API endpoints and relational schemas through machine-readable schema inspection and automated mapping algorithms, eliminating the need for manual human mapping while maintaining high accuracy and reducing time consumption
Solution Approach 2:
The manual mechanical process of human mapping is replaced with an automated computational system that uses processors to inspect schemas, infer relationships, and generate mappings programmatically, thereby eliminating human error and significantly reducing mapping time
2Adaptability or versatility
If the schema of the service changes over time, then the service can be updated, but the mapping becomes temporary and requires regeneration
Solution Approach 1:
The system implements feedback by continuously monitoring schema changes through machine-readable schema inspection and automatically regenerating mappings when changes are detected, ensuring the mapping remains synchronized with the current schema state without manual intervention
Solution Approach 2:
The mapping system is designed to be dynamic rather than static, automatically adapting to schema changes by detecting modifications and regenerating mappings in real-time, thereby maintaining mapping stability despite service evolution
3Loss of information
If the schema of the service is not machine readable or inaccurate, then the service can be documented, but the mapping must be manually performed which is time consuming
Solution Approach 1:
The system replaces manual mapping mechanics with automated computational processes that directly inspect machine-readable schemas, eliminating the need for human interpretation and significantly improving mapping efficiency while maintaining high accuracy
Solution Approach 2:
The system creates an automated copy of the schema structure in machine-readable format and uses this copy to generate the mapping, eliminating the need for manual documentation and mapping processes
Data Source
AI summary
A system and method for mapping an application program interface (API) to a relational schema. In one embodiment, the system samples a first endpoint, the first endpoint exposed via a first application programming interface (API); automatically infers, based on a set of results received from the first endpoint responsive to the sampling and based on a set of inference rules, a first set of data types and a first relational data structure representing data stored by the first endpoint and exposed via the first API; generates a configuration profile based on the first set of inferred data types and the first relational data structure representing the data stored by the first endpoint and exposed via the first API; and obtains, using the configuration profile and via the first application programming interface, data from the first endpoint responsive to a query, the query received in a relational query language.