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Graph database: History, Applications & Products

A graph database (GDB) 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). The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the…

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Graph database topic overview

The analysis highlights History, Applications and Products as prominent areas in the source structure around Graph database.

Related topics
92
Source areas
8
Connected nodes
100
Extracted relationships
58
Concept neighborhoods
44
Bridge connections
100

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

History · 25 topics
Comparison with relational databases · 24 topics
Overview · 23 topics
Applications · 6 topics
Graph models · 6 topics
Graph query-programming languages · 4 topics
Background · 3 topics
Properties · 1 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

History

Background

Graph models

Properties

Applications

Comparison with relational databases

Graph query-programming languages

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Graph database connects Entity context

The extracted context around Graph database shows recurring relationship patterns in the source. For example, Graph database → Collaborative, Creating, Database, Declarative, Graph, Knowledge Graph Management SystemObject, Ordinary, SPARQL, Text-structure, Tree-like, Type, Wikidata, Wikipedia Another extracted example is Graph database → Apache TinkerPop, AQL, ArangoDB, ArangoDB Query Language, Cypher, GQL, ISO, Neo4j, Query Language, RDF, SQL-like. Use these groups to spot repeated connection types before inspecting the individual relationships.

Graph database

Top relations

see also · 13
Graph database → Collaborative, Creating, Database, Declarative, Graph, Knowledge Graph Management SystemObject, Ordinary, SPARQL, Text-structure, Tree-like, Type, Wikidata, Wikipedia
related to Graph query-programming languages · 11
Graph database → Apache TinkerPop, AQL, ArangoDB, ArangoDB Query Language, Cypher, GQL, ISO, Neo4j, Query Language, RDF, SQL-like
related to Properties · 5
Graph database → For, Graph, Graphs, Other, There
related to Storage · 4
Graph database → NoSQL, Others, Some, The
related to background · 2
Graph database → Based, It
related to Index-free adjacency · 2
Graph database → Direct, Graph
related to List of graph databases · 1
Graph database → The

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

graph databases data database relational relationships query nodes edges one store would model language table name properties foaf key storage

Graph database relationships Subject–Predicate–Object triples

TTTA extracted 58 structured relationships around Graph database. Examples in this analysis include IBM's IMS supported tree-like structures in its hierarchical model → instance of → navigational databases and Neo4j → instance of → commercial graph databases with ACID guarantees. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
IBM's IMS supported tree-like structures in its hierarchical modelinstance ofnavigational databases0.80text
but the strict tree structure could be circumvented with virtual records.Graph structures could be represented in network model databases from the late 1960sinstance ofnavigational databases0.80text
Neo4jinstance ofcommercial graph databases with ACID guarantees0.80text
Oracle Spatialinstance ofcommercial graph databases with ACID guarantees0.80text
Graph became available.In the 2010sinstance ofcommercial graph databases with ACID guarantees0.80text
commercial ACID graph databases that could be scaled horizontally became availableinstance ofcommercial graph databases with ACID guarantees0.80text
relational database or document-oriented databaseinstance ofand other models0.80text
Amazon Neptuneinstance ofcloud-based graph databases0.80text
Neo4j AuraDB became availableinstance ofcloud-based graph databases0.80text
peopleinstance ofsuch as a node or an edge.Nodes represent entities or instances0.80text
businessesinstance ofsuch as a node or an edge.Nodes represent entities or instances0.80text
accountsinstance ofsuch as a node or an edge.Nodes represent entities or instances0.80text

Related concept clusters Concept neighborhoods

The concept neighborhoods around Graph database bring nearby vocabulary together. In this analysis, examples include Databases, Graph and Data. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Graph database
    • Databases
    • Graph
    • Data
    • Model
    • Nodes
    • Relational
    • Search
    • Would
    • Query
    • Edges
    • Table
    • Queries
  • graph database
    • Databases
    • Relational
    • Graph
    • Data
    • Store
    • Model
    • Query
    • Nodes
    • Language
    • Search
    • Would
    • Relationship
  • database
    • Relational
    • Graph
    • Store
    • Query
    • Language
    • Search
    • Would
    • Relationship
    • Storage
    • Table
    • Data
    • Structures
  • graph structures
    • Databases
    • Storage
    • Store
    • Data
    • Model
    • Nodes
    • Relational
    • Key
    • Properties
    • Query
    • Edges
    • Queries
  • graph
    • Databases
    • Data
    • Model
    • Nodes
    • Relational
    • Query
    • Edges
    • Queries
    • Relationships
    • Language
    • Store
    • Rdf
  • key–value store
    • Structures
    • Relationships
    • Storage
    • Key
    • Store
    • Abstraction
    • Tables
    • Represented
    • System
    • Relational
    • Table
    • Model
  • document-oriented database
    • Relational
    • Graph
    • Store
    • Query
    • Language
    • Search
    • Would
    • Relationship
    • Storage
    • Table
    • Data
    • Structures
  • relational databases
    • Graph
    • Relational
    • Model
    • Tables
    • Storage
    • Also
    • Table
    • Relationships
    • Records
    • Language
    • Search
    • Store

Connections between topic areas Semantic bridges

For Graph database, one of the stronger structural bridges in this analysis connects Graph database with History. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Graph database — History · splits 75 ⟂ 26
Graph database — Comparison with relational databases · splits 76 ⟂ 25
Graph database — Overview · splits 77 ⟂ 24
Graph database — Graph models · splits 94 ⟂ 7
Graph database — Applications · splits 94 ⟂ 7
Graph database — Graph query-programming languages · splits 96 ⟂ 5
Graph database — Background · splits 97 ⟂ 4

Map overview Semantic statistics

Graph database

Nodes101
Edges100
Triples58
Avg. degree1.98
Density0.019802
Components1

Source & methodology

TTTA analyzes the structure around Graph database to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Applications & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Graph database · EN edition · Analysis: TopicsToTalkAbout

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