Query Optimization Techniques In Graph Databases

TL;DR

This paper surveys various query optimization techniques in graph databases, emphasizing their adaptation to the unique features of graph data like dynamic structures and interconnected relationships.

Contribution

It provides a comprehensive overview of existing optimization methods tailored for graph databases, highlighting their specific features and challenges.

Findings

Identifies key features influencing query optimization in GDBs

Reviews techniques adapted from traditional databases and graph theory

Discusses challenges in optimizing dynamic and interconnected graph data

Abstract

Graph databases (GDB) have recently been arisen to overcome the limits of traditional databases for storing and managing data with graph-like structure. Today, they represent a requirement for many applications that manage graph-like data, like social networks. Most of the techniques, applied to optimize queries in graph databases, have been used in traditional databases, distribution systems... or they are inspired from graph theory. However, their reuse in graph databases should take care of the main characteristics of graph databases, such as dynamic structure, highly interconnected data, and ability to efficiently access data relationships. In this paper, we survey the query optimization techniques in graph databases. In particular, we focus on the features they have introduced to improve querying graph-like data.