用本体知识增强查询优化，让数据库运行更快。
OntoTune: Ontology-Driven Learning for Query Optimization with Convolutional Models
- 构建本体连接查询、元数据与统计信息，捕捉性能关键因素
- 通过嵌入保留本体关系，提升树模型与图卷积网络表现
- 实测优于数据库默认执行策略，适合数据库优化研究者
查询优化已采用机器学习、强化学习及基于图的卷积网络。本体作为结构化、信息丰富的知识表示，能提供上下文，尤其在学习问题中具有优势。本文提出 OntoTune，一个基于本体的平台，用于增强查询优化的学习能力。通过连接 SQL 查询、数据库元数据和统计信息，所构建的本体能够有效捕捉影响查询性能的关系与关键决定因素。研究还提出一种嵌入方法，在保留本体关系和关键信息的前提下，将本体输入树模型与图卷积网络等学习算法。案例研究表明，OntoTune 的本体驱动学习相比数据库系统默认查询执行，显著提升了性能。
原文摘要 · Abstract (English)
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.
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