Uplifting the Superpowers of Worst-Case-Optimal Join Algorithms

2026-08-04

AI Summary

This work addresses the inefficiency of existing worst-case optimal (WCO) join algorithms in handling filtering conditions during graph query evaluation, which typically rely on costly pre- or post-processing steps. The paper introduces the first approach that natively integrates filtering operations into the WCO join framework by leveraging an extended Ring index structure. This design enables efficient support for complex pattern matching over property graphs and queries expressed in languages such as SPARQL and GQL, while preserving index compactness. Experimental results demonstrate that the proposed method significantly outperforms state-of-the-art baseline systems on filtered graph queries, with negligible additional storage overhead.

Abstract

Worst-case-optimal (wco) join algorithms have demonstrated their power -- in both theory and practice -- to efficiently solve complex Basic Graph Patterns (BGPs). Modern graph query languages, such as SPARQL and GQL, have BGPs at their core, but also have a wide range of other features, including filters (aka.\ selections). Such conditions are typically handled via pre- or post-filtering, before or after processing the BGPs. In this paper we show how to uplift wco join algorithms so as to incorporate such filtering natively, improving efficiency. We demonstrate the superiority of this approach by extending the \textit{Ring} -- a compact index that provides wco resolution of BGPs within almost no extra space on top of the graph -- so as to handle property graphs using our new techniques while retaining compactness. We implement this extension and experimentally show that it outperforms various baseline systems.

Problem

• worst-case-optimal join
• graph query
• filtering
• property graph
• Basic Graph Patterns

Innovation

• worst-case-optimal join
• native filtering
• Ring index
• property graphs
• query optimization