Learning to Speed Up Query Planning in Graph Databases

TL;DR

This paper introduces a learning-based framework to accelerate query planning in graph databases, significantly improving query processing speed while maintaining accuracy, applicable to various query reasoners following the Threshold Algorithm approach.

Contribution

The paper proposes a novel Learning to Plan (L2P) framework that learns search control knowledge to speed up query planning in graph databases, specifically instantiated for the STAR reasoner.

Findings

Significant speedup in query planning with negligible accuracy loss.

Effective learning of search control knowledge for the STAR reasoner.

Improved performance demonstrated on benchmark knowledge graphs.

Abstract

Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security. However, the growing size of real-world data graphs poses severe challenges for graph databases to meet the response-time requirements of the applications. Planning the computational steps of query processing - Query Planning - is central to address these challenges. In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries.We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach. First, we define a generic search space over candidate query plans, and identify target search trajectories (query plans) corresponding to the training queries by performing an expensive search. Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans. We provide a concrete instantiation of our L2P framework for STAR, a state-of-the-art graph query reasoner. Our experiments on benchmark knowledge graphs including DBpedia, YAGO, and Freebase show that using the query plans generated by the learned search control knowledge, we can significantly improve the speed of STAR with negligible loss in accuracy.