OntoTune: Ontology-Driven Learning for Query Optimization with Convolutional Models

TL;DR

OntoTune introduces an ontology-driven platform that leverages structured knowledge to improve query optimization performance using convolutional models, demonstrating significant gains over default database methods.

Contribution

The paper presents a novel ontology-based approach and embedding method for query optimization, integrating structured knowledge with convolutional learning models.

Findings

Ontology-driven learning improves query performance.

Embedding methods preserve key relationships in ontologies.

Performance gains over default database systems.

Abstract

Query optimization has been studied using machine learning, reinforcement learning, and, more recently, graph-based convolutional networks. Ontology, as a structured, information-rich knowledge representation, can provide context, particularly in learning problems. This paper presents OntoTune, an ontology-based platform for enhancing learning for query optimization. By connecting SQL queries, database metadata, and statistics, the ontology developed in this research is promising in capturing relationships and important determinants of query performance. This research also develops a method to embed ontologies while preserving as much of the relationships and key information as possible, before feeding it into learning algorithms such as tree-based and graph-based convolutional networks. A case study shows how OntoTune's ontology-driven learning delivers performance gains compared with database system default query execution.