PathDB: A system for evaluating regular path queries

2025-07-02

AI Summary

To address the low evaluation efficiency and poor scalability of Regular Path Queries (RPQs) in dynamic and complex graph database scenarios, this paper designs and implements a Java-based in-memory graph database system. Methodologically, it introduces Closed Path Algebra as a unified semantic foundation, adopts a modular three-tier architecture—comprising parser, logical plan, and physical plan layers—and integrates automaton-guided graph traversal with hierarchical execution plan optimization. Experimental results demonstrate that, compared to baseline approaches such as DFS and BFS, the system achieves significant reductions in query execution time. Moreover, it supports flexible dynamic path pattern matching, exhibits strong scalability across varying graph sizes and query complexities, and maintains high adaptability for real-world deployment. The contributions include: (1) a novel algebraic framework for RPQ semantics; (2) a modular, extensible system architecture; and (3) optimized execution strategies that jointly enhance both performance and expressiveness.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic 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

PathDB is a Java-based graph database designed for in-memory data loading and querying. By utilizing Regular Path Queries (RPQ) and a closed path algebra, PathDB processes paths through its three main components: the parser, the logical plan, and the physical plan. This modular design allows for targeted optimizations and modifications without impacting overall functionality. Benchmark experiments illustrate PathDB's execution times and flexibility in handling dynamic and complex path queries, compared to baseline methods like Depth-First Search (DFS) and Breadth-First Search (BFS) guided by an automaton, highlighting its optimizations that contribute to its performance.

Problem

• Evaluating regular path queries efficiently
• Optimizing in-memory graph database performance
• Comparing PathDB with DFS and BFS methods

Innovation

• Java-based in-memory graph database system
• Modular design with parser and plan components
• Optimized Regular Path Queries via path algebra