Allow data analysts to federate data sets without having to create and run complex queries that join combinations of tables together, as in the relational database model.Use cases for graph databasesĬurrent use cases for graph databases include the following: It is expected that over time, knowledge graphs and property graphs will merge and the architectural distinctions between these two types of graph databases will fade away. Historically, graph databases have been divided into two categories - property graphs that simply support nodes and edges, and knowledge graphs like the one above that can focus on the semantic aspects of data and store information in triples. Generally speaking, indexing strategies for both types are similar. (It turns out to be Julie, Bob's wife.) Types of graph databases The subject in row 3 is Bob himself, so whoever is the subject in row 4 will be the other person who listens to rock music. In the second query, the results will return :RockMusic in rows 3 and 4. While there are a number of different strategies that graph databases may use for storing triples, most use an index that abbreviates the three primary fields to and identify :RockMusic as the object. In a triple store, the first field in the database holds the URI for the subject, the second field holds the URI for the predicate and the third field holds a URI for the object. Each subject, predicate or object is represented by a unique resource identifier ( URI). That's because this type of database uses a special index that stores information about nodes, edges and the relationship between them in groups of three.Ī triple, which may also be referred to as an assertion, has three main fields: a subject, a predicate and an object. Traditionally classified as a type of NoSQL database, graph databases are sometimes referred to as triple stores. The concept behind graphing a database is often credited to 18th-century mathematician Leonhard Euler. Graph databases are also useful for working with data in business disciplines that involve complex relationships and dynamic schema, such as supply chain management, identifying the source of an IP telephony issue and creating 'customers who bought this also looked at.' recommendation engines.

Graph databases are well-suited for analyzing interconnections, which is why there has been a lot of interest in using graph databases to mine data from social media.