Optimizing Navigational Graph Queries

TL;DR

This paper introduces novel optimization techniques for complex navigational graph queries, significantly improving evaluation performance on real-world datasets by constraining intermediate results.

Contribution

It presents the first practical evaluation solution for complex navigational queries, with effective planning and execution strategies that outperform existing methods.

Findings

Several orders of magnitude performance improvement over state-of-the-art techniques

Effective planning and execution of optimization techniques

Successful evaluation on diverse real-world datasets

Abstract

We study the optimization of navigational graph queries, i.e., queries which combine recursive and pattern-matching fragments. Current approaches to their evaluation are not effective in practice. Towards addressing this, we present a number of novel powerful optimization techniques which aim to constrain the intermediate results during query evaluation. We show how these techniques can be planned effectively and executed efficiently towards the first practical evaluation solution for complex navigational queries on real-world workloads. Indeed, our experimental results show several orders of magnitude improvement in query evaluation performance over state-of-the-art techniques on a wide range of queries on diverse datasets.