A graph is a network of things, called nodes, with their relationships expressed as links, called edges. The nodes in a graph database can be tagged with properties, which are additional information ...

Imagine your database of choice blown out of the water by a startup emerging from stealth. TigerGraph may have done just that for graph databases.

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

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.

By combining ontology and large language model-driven techniques, engineers can build a knowledge graph that is easily queried and updatable.

Graph databases offer a more efficient way to model relationships and networks than relational (SQL) databases or other kinds of NoSQL databases (document, wide column, and so on). Lately many ...

We had a chance to speak with TigerGraph's incoming head of product R&D, and it spurred some thoughts on where we thought graph databases should go.

This can make it quite slow as opposed to a graph database that is densely connected and easily queried. As sensors become more widely used in wearables such as Google Glass, the demand for graph ...

Banks, miners and police forces in Australia are among those using graph databases to provide the context and data relationships needed for more accurate and trustworthy AI, moving projects from ...