As data complexity continues to grow and the demand for real-time insights increases, the move away from traditional relational databases and towards the adoption of graph databases will become vital.

The appetite for connected data is fueling a shift from traditional relational databases to interconnected graph-based models. Newsletters Games Share a News Tip Featured ...

While graph databases store data as nodes and edges, relational databases store data in tables of rows and columns joined to other tables via key columns, Selz explained. “Where the data model is ...

Neo Technology, creators of Neo4j, the world's leading graph database, today announced the general availability of Neo4j 2.1, featuring built-in ETL that makes it easier to bring data in from ...

In graphs, keyword search techniques unravel interconnected data points, often representing relationships in social networks, bibliographic databases or web documents.

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.

SAP HANA Cloud now offers more context-aware AI solutions on its single, multi-model platform that brings together vector, graph, text, spatial, and relational data natively. Rather than sending data ...

Oracle Corp. (ORCL) is a large multinational technology company that primarily offers cloud computing, database software, and ...

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 graph below shows the total number of publications each year in Keyword Search in Graphs and Relational Databases. References [1] An Efficient Keywords Search in Temporal Social Networks .