Machine Learning Agent for Database Query Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large organizations face inefficiencies in database query processes due to varied query drafting skills across departments, leading to wasted resources and information silos, where desired data insights are not effectively retrieved, causing repeated query modifications and processor overload.
Innovation Solution
A computer-based agent employing machine learning analyzes database queries, logs, and results to improve query efficiency and architecture, providing optimized queries through a user interface, thereby reducing computing resources and enhancing data insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple departments draft their own database queries independently, then each department can access the database, but information silos are created and query efficiency deteriorates
Solution Approach 1:
The patent creates a centralized query management system that serves all departments universally. The system provides a common query drafting interface, centralized query optimization, and shared access to improved queries across all departments, eliminating the need for each department to independently draft and optimize queries while maintaining universal access to the database.
Solution Approach 2:
The patent introduces an intermediary query management system between users and the database. This intermediary analyzes incoming queries, optimizes them using machine learning models, and returns improved queries to users. This mediator resolves the contradiction by enabling efficient query execution while maintaining broad departmental access through centralized intelligence.
2Adaptability or versatility
If database queries are repeatedly run and modified by different users, then various data needs may be addressed, but computing resources are wasted
Solution Approach 1:
The patent implements preliminary query optimization by analyzing and improving queries before they are executed. The machine learning model pre-processes queries to generate optimized versions, and the system caches these improved queries. When the same or similar queries are requested again, the pre-optimized versions are reused, eliminating redundant computation while maintaining the ability to address various data needs.
Solution Approach 2:
The patent establishes a feedback loop where query performance data is collected, analyzed, and used to continuously improve the machine learning model. The system learns from execution patterns, identifies optimization opportunities, and feeds this information back into the query optimization process. This feedback mechanism enables the system to progressively reduce computing resource waste while maintaining flexible data retrieval capabilities.
3Productivity
If a centralized system optimizes all queries, then resource efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements self-service query optimization where the machine learning model automatically analyzes and optimizes queries without requiring manual intervention from database administrators. The system autonomously captures queries, generates optimized versions, validates them, and makes them available to users. This automation reduces the operational complexity burden while maintaining high query execution efficiency through intelligent self-optimization.
Solution Approach 2:
The patent replaces manual query optimization mechanics with automated machine learning-based optimization. Instead of relying on human database administrators to manually review and rewrite queries, the system uses AI models to automatically analyze query patterns, generate optimized versions, and manage the optimization process. This substitution reduces operational complexity while enhancing query execution efficiency through scalable automated intelligence.
Data Source
AI summary
A computer based agent may employ a variety of techniques including machine learning to analyze queries to a database, improve the queries to a database and make the improved queries available to new and old user through a user interface.