For a long time, companies have been using relational databases (DB) to manage data. However, with the increasing use of ...

Neo4j also trumpeted the value of graphs as vector databases used in generative artificial intelligence. AI training requires ...

Neo4j ®, the leading graph database and analytics platform, today unveiled Infinigraph: a new distributed graph architecture now available in Neo4j's self-managed offering. Infinigraph enables Neo4j's ...

Graph database query languages are growing, along with graph databases. They let developers ask complex questions and find relationships.

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.

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

Knowledge Graphs are quickly being adopted because they have the advantages of linking and analyzing vast amounts of interconnected data. The promise of graph technology has been there for a decade.

Everything in a graph database is connected to everything else, thus data is able to be summoned much faster than from a conventional structured database.

Panama Papers graphically demonstrate the power of the graph database Data-mining technology is thrown into the spotlight thanks to the tale of 11.5 million files.

Knowledge graphs are on the rise at enterprises that seek more effective ways to connect the dots between the data world and the business world. Paired with complementary AI technologies such as ...

The graph database is popular with social networks, but there's no reason to limit it to tracking people and their friendships.