As data complexity continues to grow and the demand for real-time insights increases, the move away from traditional relational databases and towards the adoption of graph databases will become vital.

In graphs, keyword search techniques unravel interconnected data points, often representing relationships in social networks, bibliographic databases or web documents.

Another often used word for a graph database is graph analytics, which refers to the process of analyzing data in a graph style with data points acting as relationships and nodes acting as edges.

Unlike RDB, the graph database utilizes graphs linked by points and lines to store and manage each aspect of data, thus enabling analysis of a large volume of data in a short period of time. According ...

Not Ready to Replace Relational? Both graph and relational databases have their own strengths and optimal use cases. While graph databases store data as nodes and edges, relational databases store ...

Native graph database is a specialized database management system designed to efficiently store, manage, and query graph-structured data, where entities (nodes) are connected by relationships (edges).

Relational databases like MySQL and PostgreSQL are good for structured data, while NoSQL databases like MongoDB and Cassandra are better for unstructured data. Graph databases like Neo4j are ideal ...

Forecasting is a fundamentally new capability that is missing from the current purview of generative AI. Here's how Kumo is changing that.

Graph database startup TigerGraph Inc. today announced a major update to its flagship cloud platform with the Savanna release, bringing with it six times faster network deployments and dozens of ...

Keyword search in graphs and relational databases constitutes a pivotal research domain that seeks to bridge the gap between natural language queries and complex data repositories.