A graph database is a dynamic database management system uniquely structured to manage complex and interconnected data.

A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its ...

Linkohr: For us, we could see advantages to using graph technology in HR projects because HR data is not isolated, so you don't normally have one person working without a connection to another person.

In recent years, knowledge graphs have become an important tool for organizing and accessing large volumes of enterprise data in diverse industries — from healthcare to industrial, to banking ...

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 ...

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.

TigerGraph is a pure graph database, from the ground up. Its data store holds nodes, links, and their attributes, period. Some graph database products on the market are really wrappers built on ...

If, for example, we have a rule that more than x properties cannot connect to a single substation, it isn’t possible to see when that rule gets broken for whatever reason. With the graph database, we ...

To illustrate the kinds of relationship that a hypergraph can tease out of a big data set — and an ordinary graph can’t — Purvine points to a simple example close to home, the world of scientific ...