Database Query Rewriting via Semantic Analysis
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Solution Overview
Problem
Existing database query performance is adversely affected by the complexity of association relationships in multiple dimensions, leading to inefficient query processing.
Innovation Solution
A computer-implemented method that analyzes query patterns and performs semantic analysis to generate a re-write query with improved performance, utilizing machine learning to build a query model and dynamically update parameters in a hybrid cloud database environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If semantic analysis and machine learning are used to generate re-write queries, then query performance is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary semantic analysis and builds query models in advance before actual query execution. The machine learning model is trained beforehand on historical query data, and the system pre-processes incoming queries by analyzing their semantic structure and generating optimized re-write queries before they are executed against the database, thus improving query performance while managing complexity through advance preparation
Solution Approach 2:
The patent introduces an intermediary layer between the user's original query and the database execution. This intermediary consists of the semantic analysis module and machine learning-based query rewriting engine that transforms the original query into an optimized re-write query. This mediator handles the complexity of performance optimization internally, allowing users to submit simple queries while the intermediary system manages the sophisticated transformations needed to improve query performance
2Productivity
If query patterns are analyzed and re-written in real-time, then query performance improves, but processing time increases
Solution Approach 1:
The system performs semantic analysis and generates re-write queries in advance before actual query execution. By pre-processing queries and preparing optimized versions ahead of time, the system reduces the time penalty associated with real-time analysis. The machine learning model is also trained beforehand on historical data, so that when a query arrives, the system can quickly match it against known patterns and apply pre-computed optimizations
Solution Approach 2:
The system anticipates potential performance issues by analyzing query patterns beforehand and pre-generating optimized re-write queries. By performing the complex semantic analysis and query transformation work before the actual query execution, the system prevents performance degradation during runtime. The preliminary analysis identifies optimization opportunities in advance, so that when queries are executed, they already benefit from the optimizations without adding significant processing overhead
Data Source
AI summary
Embodiments analyze a query pattern of an incoming query on a database, perform a semantic analysis of the query pattern of the incoming query, generate a re-write query that has an improved query performance in comparison to a query performance of the incoming query based on the analyzed query pattern and the semantic analysis; build a query model using machine learning based on at least one of the query pattern and the semantic analysis; and apply the re-write query by performing the re-write query on the database to provide the improved query performance.