Database Engine Integrating Machine Learning Model References
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Solution Overview
Problem
Machine learning models are often inaccessible to developers without specialized skill sets, making it difficult for diverse systems to leverage insights from these models effectively.
Innovation Solution
Implementing a database engine that recognizes and integrates machine learning model references within database queries, allowing for the evaluation and incorporation of machine learning model results directly into query results, thereby simplifying interactions and reducing complexity for clients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are integrated into database queries, then application capabilities and insights are improved, but device complexity and difficulty of operation increase due to specialized skill requirements
Solution Approach 1:
The patent introduces a database engine as an intermediary layer between traditional database operations and machine learning model execution. This engine translates standard SQL queries into machine learning operations automatically, shielding users from complexity while enabling advanced capabilities. The database engine acts as the mediator that handles the specialized knowledge requirements internally while presenting a simple interface to users.
Solution Approach 2:
The system segments the complexity of machine learning integration by separating concerns into distinct layers: the database engine handles model training, evaluation, and inference operations internally, while users interact only with standard SQL syntax. This segmentation isolates the complex machine learning components from the user-facing interface, reducing perceived complexity while maintaining advanced functionality.
2Ease of operation
If machine learning model references are integrated into database queries, then ease of operation is improved, but device complexity increases due to additional integration components
Solution Approach 1:
The patent merges machine learning operations with database operations into a unified system. The database engine combines traditional data storage and retrieval functions with machine learning model training and inference capabilities, allowing users to perform both types of operations through a single interface using standard SQL syntax, thereby improving ease of operation without requiring separate systems.
Solution Approach 2:
The database engine is designed with multi-functionality, serving both as a traditional database system for data storage and retrieval and as a machine learning platform for model training and evaluation. This universal system handles diverse operations through a common interface, eliminating the need for users to learn separate tools and reducing operational complexity.
3Productivity
If machine learning operations are performed within the database engine, then productivity is improved, but use of energy increases due to additional processing requirements
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models using historical data stored in the database, and caching model results where applicable. This preliminary processing reduces the need for repeated heavy computations during query execution, improving productivity while managing energy consumption by performing intensive operations only when necessary.
Solution Approach 2:
The database engine performs self-service by automatically managing machine learning model lifecycles including training, evaluation, and deployment without requiring external specialized systems. This self-contained approach consolidates operations within a single system, improving productivity through integrated workflows while optimizing energy usage by eliminating redundant data transfers and external processing.
Data Source
AI summary
Querying databases may be performed with references to machine learning models. A database query may be received that references a machine learning model and database. In response to the query, the machine learning model may provide information which may be returned as part of a result of the query or may be used to generate a result of the query. The machine learning model may be generated in response to a request to generate a machine learning model that includes a database query that identifies the data upon which a machine learning technique may be applied to generate the machine learning model.