Graph Theory for Computer Science

5Big Data Analytics—Graph Databases and Insights

Nishant Wanjari, Aashka Gupta and Reshma Gulwani*

Department of Information Technology, Ramrao Adik Institute of Technology, Dr. DY Patil Deemed to be University, Nerul, Navi Mumbai, Maharashtra, India

Abstract

In the modern world, we have a vast amount of data, yet comprehending it can be challenging. Big data analytics helps us understand this data better. In big data, relationships between data points are complex. Traditional databases struggle to represent and query such relationships. One powerful tool in this field is graph database. In graphs, data is represented through nodes and their relationships. These databases help us see how different pieces of information are connected, like friends on social media or products bought together. The graph database offers a flexible data model that effectively adapts to changing data structures. Graph databases excel at optimizing relationship queries, which results in excellent speed for tasks like finding the shortest path between nodes, identifying patterns, and analyzing interconnected data. Big data often requires horizontal scalability, and many graph databases, including Neo4j, are designed to expand across multiple nodes. Neo4j is an important graph database management system used for efficiently storing, querying, and managing highly interconnected data. Graph databases facilitate advanced analytics like community detection, centrality analysis, anomaly detection, influence ...