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.

Fraud detection and identity authentication are the two main use cases that Aerospike sees customers using the graph database to build. Fraud detection, where connections to known fraudulent entities ...

New techniques make graph databases a powerful tool for grounding large language models in private data.

TigerGraph today rolled out a new deal that allows customers to store up to 50 GB of data in a distributed graph database running on-premise, matching what it already offered in the cloud. The company ...

Aerospike is expanding its database capabilities with its Aerospike Graph database, which brings graph data model capabilities.

Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points — that’s the 30-second explanation, according to Alicia Frame.

Diffbot ingests and parses the entire web into a knowledge graph - a database you can query. Image: Diffbot First off, you have to crawl the web.

Automotive giant Daimler is using Neo4j's graph database technology in its HR department. ZDNet spoke to Jochen Linkohr, the manager of HR IT at Daimler, to find out more.

Graph databases, based on mathematics known for three centuries, are starting to yield value for businesses beyond Facebook and Twitter.

AnzoGraph, on the other hand, is designed as an OLAP graph database. Cambridge Semantics actually says “Complement your OLTP graph database engine with OLAP” on the main web page for AnzoGraph.