NoSQL Graph Databases: an overview

TL;DR

This paper provides an overview of NoSQL graph databases, highlighting their unique features, comparing different systems, and analyzing two prominent examples, AllegroGraph and Neo4j, focusing on their models and functionalities.

Contribution

It offers a comprehensive overview of NoSQL graph databases, detailing their characteristics, differences, and an in-depth comparison of AllegroGraph and Neo4j.

Findings

AllegroGraph uses RDF model; Neo4j employs labeled property graph.

Differences in data modeling and query capabilities among systems.

Insights into the suitability of each system for various applications.

Abstract

Graphs are the most suitable structures for modeling objects and interactions in applications where component inter-connectivity is a key feature. There has been increased interest in graphs to represent domains such as social networks, web site link structures, and biology. Graph stores recently rose to prominence along the NoSQL movement. In this work we will focus on NOSQL graph databases, describing their peculiarities that sets them apart from other data storage and management solutions, and how they differ among themselves. We will also analyze in-depth two different graph database management systems - AllegroGraph and Neo4j that uses the most popular graph models used by NoSQL stores in practice: the resource description framework (RDF) and the labeled property graph (LPG), respectively.