Schema-Based Query Optimisation for Graph Databases

2024-03-04

arXiv.org

AI Summary

This paper addresses the low execution efficiency of acyclic recursive graph queries in graph databases. We propose a schema-based, semantics-preserving optimization technique. Our core innovation lies in the first systematic integration of structural information inherent in the graph schema into recursive query optimization: leveraging type inference and schema analysis, we automatically derive type constraints on nodes and edges to enable schema-augmented rewriting of recursive queries. Within a formal semantic framework, we prove the soundness and completeness of our approach, thereby rigorously guaranteeing query equivalence. Experimental evaluation demonstrates significant performance improvements for acyclic recursive queries across real-world domains—including social networks and life sciences—while preserving correctness. The method achieves both substantial speedups and strict semantic fidelity, bridging a critical gap between optimization and formal correctness in recursive graph querying.

Technology Category

• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Recursive graph queries are increasingly popular for extracting information from interconnected data found in various domains such as social networks, life sciences, and business analytics. Graph data often come with schema information that describe how nodes and edges are organized. We propose a type inference mechanism that enriches recursive graph queries with relevant structural information contained in a graph schema. We show that this schema information can be useful in order to improve the performance when evaluating acylic recursive graph queries. Furthermore, we prove that the proposed method is sound and complete, ensuring that the semantics of the query is preserved during the schema-enrichment process.

Problem

• Optimize recursive graph queries
• Utilize schema information for performance
• Ensure query semantics preservation

Innovation

• Schema-based query optimization
• Type inference mechanism
• Acyclic recursive graph queries