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In computing, a graph database is a database that uses graph structures for semantic queries with nodes, edges and properties to represent and store data. A key concept of the system is the graph (or edge or relationship), which directly relates data items in the store. The relationships allow data in the store to be linked together directly, and in many cases retrieved with one operation. This contrasts with conventional relational databases, where links between data are stored in the data, and queries search for this data within the store and use the join concept to collect the related data. Graph databases, by design, allow simple and fast retrieval of complex hierarchical structures that are difficult to model in relational systems. Graph databases are similar to 1970s network model databases in that both represent general graphs, but network-model databases operate at a lower level of abstraction and lack easy traversal over a chain of edges. The underlying storage mechanism of graph databases can vary. Some depend on a relational engine and store the graph data in a table. Others use a key-value store or document-oriented database for storage, making them inherently NoSQL structures. Mostgraph databases based on non-relational storage engines also add the concept of tags or properties, which are essentially relationships having a pointer to another document. This allows data elements to be categorized for easy retrieval en masse. Retrieving data from a graph database requires a query language other than SQL, which was designed for relational databases and does not elegantly handle traversing a graph. no single graph query language has been universally adopted in the same way as SQL was for relational databases, and there are a wide variety of systems, most often tightly tied to one product. Some standardization efforts have occurred, leading to multi-vendor query languages like Gremlin, SPARQL, and Cypher. In addition to having query language interfaces, some graph databases are accessed through application programming interfaces (APIs). (Source: Wikipedia, the free encyclopedia, see http://en.wikipedia.org/wiki/Graph_database)

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  • In computing, a graph database is a database that uses graph structures for semantic queries with nodes, edges and properties to represent and store data. A key concept of the system is the graph (or edge or relationship), which directly relates data items in the store. The relationships allow data in the store to be linked together directly, and in many cases retrieved with one operation. This contrasts with conventional relational databases, where links between data are stored in the data, and queries search for this data within the store and use the join concept to collect the related data. Graph databases, by design, allow simple and fast retrieval of complex hierarchical structures that are difficult to model in relational systems. Graph databases are similar to 1970s network model databases in that both represent general graphs, but network-model databases operate at a lower level of abstraction and lack easy traversal over a chain of edges. The underlying storage mechanism of graph databases can vary. Some depend on a relational engine and store the graph data in a table. Others use a key-value store or document-oriented database for storage, making them inherently NoSQL structures. Mostgraph databases based on non-relational storage engines also add the concept of tags or properties, which are essentially relationships having a pointer to another document. This allows data elements to be categorized for easy retrieval en masse. Retrieving data from a graph database requires a query language other than SQL, which was designed for relational databases and does not elegantly handle traversing a graph. no single graph query language has been universally adopted in the same way as SQL was for relational databases, and there are a wide variety of systems, most often tightly tied to one product. Some standardization efforts have occurred, leading to multi-vendor query languages like Gremlin, SPARQL, and Cypher. In addition to having query language interfaces, some graph databases are accessed through application programming interfaces (APIs). (Source: Wikipedia, the free encyclopedia, see http://en.wikipedia.org/wiki/Graph_database)
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  • Base de dades orientada a grafs
  • Base de datos orientada a grafos
  • Base de données orientée graphe
  • Base di dati a grafo
  • Graph database
  • Graph database
  • Graphdatenbank
  • پایگاه داده‌های گراف
  • Grafowa baza danych
  • Graph database
  • Графын өгөгдлийн сан
  • بنکەدراوەی گراف
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