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Cypher is a declarative graph query language that allows for expressive and efficient data querying in a property graph. [1]Cypher was largely an invention of Andrés Taylor while working for Neo4j, Inc. (formerly Neo Technology) in 2011. [2]
Neo4j is a graph database management system (GDBMS) developed by Neo4j Inc. The data elements Neo4j stores are nodes , edges connecting them, and attributes of nodes and edges.
GraphQL: an open-source data query and manipulation language for APIs. Dgraph implements modified GraphQL language called DQL (formerly GraphQL+-) Gremlin: a graph programming language that is a part of Apache TinkerPop open-source project [49] SPARQL: a query language for RDF databases that can retrieve and manipulate data stored in RDF format
Queries are therefore able to first project a sub-graph of the graph input into the query, and then extract the data values associated with that subgraph. Data values can also be processed by functions, including aggregation functions, leading to the projection of computed values which render the information held in the projected graph in ...
Cypher is a query language for the Neo4j graph database; DMX is a query language for data mining models; Datalog is a query language for deductive databases; F-logic is a declarative object-oriented language for deductive databases and knowledge representation. FQL enables you to use a SQL-style interface to query the data exposed by the Graph API.
Download QR code; Print/export ... Basic SQL queries, simple joins [31] and CONNECT BY joins. ... Couchbase, Neo4j, ArangoDB and others.
These steps are sufficient to provide general purpose computing and what is typically required to express the common motifs of any graph traversal query. Given that Gremlin is a language, an instruction set, and a virtual machine, it is possible to design another traversal language that compiles to the Gremlin traversal machine (analogous to ...
Graph databases: Graph databases are designed to represent and query data in the form of graphs. They are effective for handling relationships and network-type data. Examples: Neo4j, Amazon Neptune. In-memory databases: In-memory databases store data in the system's main memory rather than on disk. This allows for faster data access and retrieval.