Problem
Research questions and friction points this paper is trying to address.
Distributed evaluation of recursive graph queries
Extending relational algebra for distributed settings
Reducing communication costs in large-scale query processing
Distributed Evaluation of Graph Queries using Recursive Relational Algebra
🤖 AI Summary
To address the challenge of efficiently evaluating recursive graph queries over large-scale graph data in distributed environments, this paper introduces the first extension of recursive relational algebra to distributed settings and proposes a communication-optimized method for generating distributed execution plans. Our approach integrates parallel graph traversal, incremental computation, and lightweight message compression to substantially reduce cross-node data transfer overhead. While preserving high expressive power—supporting arbitrary recursive patterns—it simultaneously ensures query efficiency and system scalability. Experimental evaluation on both real-world and synthetic graph datasets demonstrates that our method reduces query latency by up to 5.3× and decreases total communication volume by up to 68%, significantly outperforming state-of-the-art distributed graph query systems.
📝 Abstract
We present a system called Dist-$mu$-RA for the distributed evaluation of recursive graph queries. Dist-$mu$-RA builds on the recursive relational algebra and extends it with evaluation plans suited for the distributed setting. The goal is to offer expressivity for high-level queries while providing efficiency at scale and reducing communication costs. Experimental results on both real and synthetic graphs show the effectiveness of the proposed approach compared to existing systems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Distributed evaluation of recursive graph queries
Extends recursive relational algebra
Reduces communication costs efficiently
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