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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.
The above examples are a simple illustration of a basic relationship query. They condense the idea of relational models' query complexity that increases with the total amount of data. In comparison, a graph database query is easily able to sort through the relationship graph to present the results.
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.
Consequently, while these databases excel at basic CRUD operations and key-based lookups, their suitability for complex queries involving joins or non-indexed filtering varies depending on the database type—document, key–value, wide-column, or graph—and the specific implementation.
Now the system can automatically answer more complex queries and analytics that look for the connection of a particular location with a product category. The development effort for this query is omitted. Executing a semantic query is conducted by walking the network of information and finding matches (also called Data Graph Traversal).
The problem of listing all answers to a non-Boolean conjunctive query has been studied in the context of enumeration algorithms, with a characterization (under some computational hardness assumptions) of the queries for which enumeration can be performed with linear time preprocessing and constant delay between each solution.
The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time. View selection is NP-Complete . Many approaches to the problem have been explored, including greedy algorithms , randomized search, genetic algorithms and A* search algorithm .