Data-hungry AI applications are fed complex information, and that's where graph databases and knowledge graphs play a crucial role.

With the rapid development of artificial intelligence (AI) technology, the graph database market is experiencing unprecedented growth, with an annual growth rate approaching 25%. Graph databases are ...

One technology experiencing a kind of renaissance is the graph database, which has existed for years but is finding new relevance in current cybersecurity contexts.

You can think of a graph database as a set of interconnected circles (nodes) and each node represents a person, a product, a place or ‘thing’ that we want to build into our data universe.

Unlike other databases which store their data in rows, columns or key-value pairs a graph database stores all information in a network of nodes and edges. Edges manifest the connection between ...

The rise of graph databases is closely related to AI's demand for data processing. AI technology requires vast amounts of structured and unstructured data, which must not only be input into ...

Why graph databases are becoming mainstream and their connection to AI, ML and big data use cases.

As graph database adoption accelerates, new data infrastructures will emerge to eliminate many of the scale struggles of graph database models. Written by eWEEK content and product recommendations ...

A lot of the graph use cases are really graph database use cases, so it could be that is what people are using. There's Neo4j, Titan -- now acquired by DataStax -- and Gaffer.

Interest in graph databases has exploded during the first five months of the year, as the product category threatens to lap other database types in popularity, according to figures from DB-Engines.com ...