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By multi-model, Franz said its semantic graph database supports ingestion of different JSON documents as well as Resource Description Framework (RDF), or triplestore, data—another World Wide Web ...
Graph databases are the fastest-growing category in all of data management. Here's how to pick one.
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.
Graph databases are proven architectures for storing data with complex relationships. Why aren't more companies using them?
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 ...
A database that can serve graph formats is required for graph analytics. It can be a specialized graph database or a convergent database that supports several data types, including graphs.
For data-centric companies looking to implement these solutions, a graph database running on Dell PowerEdge servers is the optimal offering in terms of performance, efficiency, and scale.
A new semantic-based graph data model has emerged within the enterprise. This data model has all of the advantages of the relational data model, but goes even further in providing for more ...
By combining ontology and large language model-driven techniques, engineers can build a knowledge graph that is easily queried and updatable.
Graph databases offer significant advantages to the healthcare, pharmaceutical, and life sciences sectors due to their ability to model and query complex relationships within data.