Graph-Oriented Databases: Technology and Applications

Technology and Applications in the Big Data era

Talking about databases today is essential to understand how data management has evolved from relational systems to new technologies such as graph-oriented databases (GDBs). These emerging solutions are revolutionizing the way companies analyze and connect large volumes of data in a world driven by Big Data and scalability needs.

The evolution of databases

Relational databases, introduced by IBM in the 1970s, became the standard with the development of SQL (Structured Query Language), enabling efficient management of structured data. However, with the arrival of Web 2.0 and the exponential growth of data, the limitations of these systems became evident. This led to the rise of NoSQL databases, designed to address scalability and performance challenges.

What are graph databases?

Graph-oriented databases are a type of NoSQL database that represent data and their relationships through graphs, providing an intuitive and efficient way to explore connections. These databases use vertices (nodes) and edges (relationships), making it possible to identify complex patterns and correlations. Some leading companies, such as Neo4j, are benchmarks in this technology.

Use cases for graph-oriented databases

Companies such as eBay, Walmart, Cisco, and Lufthansa have implemented GDBs for:

• Route planning in e-commerce (eBay).
• Sales analysis and personalized recommendations (Walmart).
• Customized solutions without direct interaction with customer support (Cisco).

Types of graphs

• Undirected graphs: Bidirectional relationships, such as friendships on Facebook.
• Directed graphs: One-way relationships, such as followers on Twitter.
• Weighted graphs: Quantifiable relationships, useful for mathematical operations.
• Labeled graphs: Include labels to define nodes and relationships.
• Property graphs: Combine labels, weights, and additional properties for nodes and relationships.

Applications in auditing and fraud control

In financial auditing, GDBs make it possible to cross-check large volumes of data, carry out comprehensive analyses, and demonstrate complex hypotheses. These tools increase confidence in the information analyzed and provide a more end-to-end approach compared to traditional sampling methods.