17931963. AUTOMATED QUERY SELECTIVITY PREDICTIONS USING QUERY GRAPHS simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
- 1 AUTOMATED QUERY SELECTIVITY PREDICTIONS USING QUERY GRAPHS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 AUTOMATED QUERY SELECTIVITY PREDICTIONS USING QUERY GRAPHS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Original Abstract Submitted
AUTOMATED QUERY SELECTIVITY PREDICTIONS USING QUERY GRAPHS
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Mohammed Fahd Alhamid of Stouffville (CA)
Vincent Corvinelli of Mississauga (CA)
Calisto Zuzarte of Pickering (CA)
AUTOMATED QUERY SELECTIVITY PREDICTIONS USING QUERY GRAPHS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17931963 titled 'AUTOMATED QUERY SELECTIVITY PREDICTIONS USING QUERY GRAPHS
Simplified Explanation
The computer-implemented method described in the abstract involves training a machine learning model using a set of training queries generated from a query workload or relationships between tables in a database. The method includes building query graphs for each training query, computing selectivity based on the query graph, and creating an initial join result distribution based on the set of training queries.
- Training a machine learning model using a set of training queries
- Generating training queries from a query workload or database table relationships
- Building query graphs for each training query
- Computing selectivity based on the query graph
- Creating an initial join result distribution from the set of training queries
Potential Applications
This technology could be applied in database management systems, query optimization, and data analytics platforms.
Problems Solved
- Improving query performance - Enhancing database optimization - Streamlining data analysis processes
Benefits
- Increased efficiency in query processing - Enhanced accuracy in result distribution - Improved overall performance of machine learning models
Potential Commercial Applications
Optimizing Database Query Performance for Enhanced Data Analysis
Original Abstract Submitted
Examples described herein provide a computer-implemented method that includes training a machine learning model. The model is trained by generating a set of training queries using at least one of a query workload and relationships between tables in a database, building a query graph for each of the set of training queries, computing, for each training query of the set of training queries, a selectivity based at least in part on the query graph, and building, based at least in part on the set of training queries, an initial join result distribution as a collection of query graphs.
