Graph database

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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: 38
Related term clusters: 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.

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Explore different angles and find fresh ideas to shape your next piece of content.

Graph database

6Database · Graph (data structure) · Semantic query

29NoSQL · Network model · Abstraction (computer science)

12Scalability · SAP HANA · In-memory database

6Edgar F. Codd · Relational model · Relational databases

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.

Background

Graph models

Properties

Database index

Applications

Graph query-programming languages

For the semantics nerds

You can skip this section if you’re here for content ideas and keyword inspiration.

Advanced semantic analysis

How Graph database connects Entity context

The extracted context around Graph database shows recurring relationship patterns in the source. For example, Graph database → Apache TinkerPop, AQL, ArangoDB, ArangoDB Query Language, Cypher, GQL, ISO, Neo4j, Query Language, RDF, SQL-like Another extracted example is Graph database → Direct, Graph. Use these groups to spot repeated connection types before inspecting the individual relationships.

Top relations

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 Index-free adjacency · 2

Graph database → Direct, Graph

related to Properties · 2

Graph database → Graph, Graphs

related to Storage · 2

Graph database → NoSQL, Others

related to background · 1

Graph database → Based

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 38 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.

Subject	Predicate	Object	Confidence	Src
IBM's IMS supported tree-like structures in its hierarchical model	instance of	navigational databases	0.80	text
but the strict tree structure could be circumvented with virtual records.Graph structures could be represented in network model databases from the late 1960s	instance of	navigational databases	0.80	text
Neo4j	instance of	commercial graph databases with ACID guarantees	0.80	text
Oracle Spatial	instance of	commercial graph databases with ACID guarantees	0.80	text
Graph became available.In the 2010s	instance of	commercial graph databases with ACID guarantees	0.80	text
commercial ACID graph databases that could be scaled horizontally became available	instance of	commercial graph databases with ACID guarantees	0.80	text
relational database or document-oriented database	instance of	and other models	0.80	text
Amazon Neptune	instance of	cloud-based graph databases	0.80	text
Neo4j AuraDB became available	instance of	cloud-based graph databases	0.80	text
people	instance of	such as a node or an edge.Nodes represent entities or instances	0.80	text
businesses	instance of	such as a node or an edge.Nodes represent entities or instances	0.80	text
accounts	instance of	such as a node or an edge.Nodes represent entities or instances	0.80	text

Related concept clusters Related term clusters

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

Triples38

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

Graph database: History, Applications & Products

Graph database topic overview

What this topic covers Research coverage

Key facts & relationships

Start with your topic. Discover where to go next.

Questions to explore

Explore all related topics Closing gaps

Overview

History

Background

Graph models

Properties

Applications

Comparison with relational databases

Graph query-programming languages

For the semantics nerds

Advanced semantic analysis

How Graph database connects Entity context

Graph database

Top relations

Important terminology

Important terminology

Graph database relationships Subject–Predicate–Object triples

Related concept clusters Related term clusters

Connections between topic areas Semantic bridges

Map overview Semantic statistics

Graph database

Source & methodology