Non-Relational Database Inference for Simplified ML Access
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Solution Overview
Problem
Non-relational databases require proprietary protocols and system-specific APIs, posing a barrier for developers to integrate machine learning models effectively, diverting focus from core application development.
Innovation Solution
Integrate a non-relational database system with machine learning techniques, allowing client applications to leverage inferences generated by machine learning models directly, reducing the need for developers to manage performance, availability, and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proprietary protocols and system-specific APIs are used for non-relational database access, then database functionality is achieved, but developer complexity and integration difficulty increase
Solution Approach 1:
The patent introduces a machine learning model as an intermediary layer between the application and the non-relational database. This model translates standard database operations into proprietary protocol commands, allowing developers to use familiar interfaces while maintaining access to specialized database functionality. The model acts as a mediator that handles the complexity of proprietary protocols internally.
2Adaptability or versatility
If developers manage machine learning models separately for database access, then customization is possible, but time and resource consumption increase
Solution Approach 1:
The machine learning model is pre-trained and configured to handle specific non-relational database operations. By performing the model setup and training in advance, the system eliminates the need for developers to manually manage or retrain models during application development, significantly reducing development time while maintaining customization capabilities.
Solution Approach 2:
The machine learning model autonomously handles the translation and optimization of database operations without requiring developer intervention. The model self-manages its inference processes, adapting to different database schemas and operations automatically, which reduces the time developers would otherwise spend on manual configuration and management.
Data Source
AI summary
Inferences may be obtained to handle access requests at a non-relational database system. An access request may be received at a non-relational database system. The non-relational database system may determine that the access request uses a machine learning model to complete the access request. The non-relational database system may cause an inference to be generated using data items for the access request as input to the machine learning model. The access request may be completed using the generated inference.