Quantum Cloud API Parsing for Secure Multi-Source Schema Matching
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Solution Overview
Problem
Large organizations face challenges in efficiently, securely, and uniformly managing information exchange between internal and external computer systems due to differing data formats across multiple databases, leading to delays and inefficiencies in cloud-based applications like CRM systems.
Innovation Solution
A quantum cloud-enabled API parsing engine with an AI-based schema analysis engine identifies and formats data across multiple databases using a quantum key pool schema processor, enabling efficient and secure data access by automatically training on schema variables and encrypting sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a cloud-based application accesses data from multiple databases with different formats, then data access capability is improved, but system complexity and difficulty of management increase
Solution Approach 1:
The patent introduces an API gateway as an intermediary component that sits between the cloud-based application and multiple databases with different formats. The gateway receives data access requests, automatically identifies the target database format, performs format conversion and schema mapping, and returns standardized results to the application. This mediator handles the complexity of multiple formats internally while presenting a unified interface to the application, thus improving data access capability without increasing visible system complexity.
Solution Approach 2:
The system dynamically changes parameters such as data format, schema structure, and query syntax based on the target database being accessed. The API gateway maintains a registry of database-specific parameters and automatically adjusts these parameters when routing requests to different databases. This parameter adaptation allows the system to access diverse database formats through a single standardized interface, resolving the contradiction between versatility and complexity.
2Measurement precision
If manual schema selection is used to access data from different databases, then data access accuracy can be maintained, but processing time and efficiency decrease
Solution Approach 1:
The API gateway implements self-service functionality by automatically analyzing incoming data requests, identifying the target database type, selecting the appropriate schema, and performing format conversion without human intervention. The system uses automated schema detection algorithms and database metadata to make intelligent decisions about how to access and transform data. This self-service capability maintains high data access accuracy while eliminating the time-consuming manual schema selection process, thus resolving the contradiction between precision and efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-configuring and caching database schemas, format conversion rules, and access patterns in the API gateway. When a data request arrives, the gateway retrieves pre-processed schema information and applies predetermined conversion rules, eliminating the need for real-time manual analysis. This preliminary preparation of data access pathways maintains accuracy through pre-validated schemas while dramatically improving processing speed and efficiency.
3Adaptability or versatility
If multiple data formats are supported across databases, then system versatility is improved, but data management security and uniformity decrease
Solution Approach 1:
The API gateway implements a universal data access interface that can handle multiple database formats and protocols through a single standardized entry point. All data requests, regardless of target database format, pass through the same gateway which applies unified authentication, authorization, encryption, and validation rules. This universal interface maintains system versatility by supporting diverse databases while ensuring consistent security and uniformity in data management through centralized control, resolving the contradiction between adaptability and reliability.
Data Source
AI summary
Various aspects of the disclosure relate to a quantum cloud enabled API parsing engine to automatically and intelligently identify API based schemas improves efficiency and dynamically increases accuracy through use of an artificial intelligence-based schema analysis engine. A dynamic request is received from a cloud-based application to fetch data from different sources. The API request is received by a quantum cloud environment including a dual quantum storage system for storing multiple API schemas. The quantum cloud-based application includes an AI-based query schema analysis engine that may process the input request across the various API schemas and then formats the API response before sending back to source cloud-based application. The AI-based query schema analysis engine trains an AI data set to find a match based on a schema variable parameter. The AI-based query schema analysis engine leverages a quantum key pool schema processor generated matched encrypted schema area.