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 ...

For example, graph databases – a type of NoSQL database – are increasingly seen as essential to the modern mix of databases that organisations need to address their data needs.

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

The appetite for connected data is fueling a shift from traditional relational databases to interconnected graph-based models.

Graph database startup TigerGraph Inc. today announced a major update to its flagship cloud platform with the Savanna release, bringing with it six times faster network deployments and dozens of ...

The use of knowledge graphs stored as graph databases provides the ideal platform for building and maintaining these knowledge graphs as data complexity continues to grow.

By embracing graph databases, organizations can unlock the full potential of their data, reduce costs and time, become more efficient and gain a competitive advantage.

Relational databases are so entrenched and ubiquitous that we reflexively use them for new application requirements. However, graph databases are better for applications with specific processing ...

One example would be making connections between data collected from a patient’s various doctors and pharmacies. Why Graph Analytics is Important for Healthcare Hospitals deal with stockpiles of ...

The addition of vectors provides context to the graph database for enhanced search and supports generative AI and large language models.