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Data model: History, Standards & Products

A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner.

Language: English [EN]
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Data model topic overview

The analysis highlights History, Standards and Products as prominent areas in the source structure around Data model. 1 topic appears in more than one source area, which can help identify connections that are less obvious in a linear reading.

Related topics
129
Source areas
5
Connected nodes
135
Extracted relationships
160
Concept neighborhoods
65
Bridge connections
135

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.

Related models · 36 topics
History · 26 topics
Overview · 24 topics
Types · 23 topics
Topics · 21 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

Types

Topics

Related models

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 Data model connects Entity context

The extracted context around Data model shows recurring relationship patterns in the source. For example, Data model → According, Charles Bachman, CODASYL, Codd, Edgar, General Electric, IDS, In, Integrated Data Store, IS, IT, Kent, Leondes, MIS, One, The, Their, They, This, Towards Another extracted example is Data model → Book, Book Volume, Conventions, Data Model Patterns, David, Developing High Quality Data, Dorset House Publishers, Hay, Inc, John Wiley, Len Silverston, Matthew West, Modeling Volume, Models Morgan Kaufmann, New York, Paul Agnew, Sons, The Data Model Resource, Thought, Universal Patterns. Use these groups to spot repeated connection types before inspecting the individual relationships.

Data model

Top relations

related to history · 22
Data model → According, Charles Bachman, CODASYL, Codd, Edgar, General Electric, IDS, In, Integrated Data Store, IS, IT, Kent, Leondes, MIS, One, The, Their, They, This, Towards
related to Further reading · 20
Data model → Book, Book Volume, Conventions, Data Model Patterns, David, Developing High Quality Data, Dorset House Publishers, Hay, Inc, John Wiley, Len Silverston, Matthew West, Modeling Volume, Models Morgan Kaufmann, New York, Paul Agnew, Sons, The Data Model Resource, Thought, Universal Patterns
related to Information model · 13
Data model → According, An Information, Building Information Model, Facility Information Model, In, It, Lee, More, Plant Information Model, Such, The, Typically, Within
related to The role of data models · 13
Data model → According, Business, Data, Entity, For, Fowler, However, If, The, These, They, This, West
related to overview · 10
Data model → At, Data, Managing, So, Sometimes, The, They, This, XDM, XML
related to Data organization · 9
Data model → Another, ANSI, Data, Ideally, In, It, Presumably, Such, While
related to Three perspectives · 9
Data model → ANSI, Conceptual, CPUs, For, In, Logical, Physical, This, XML
related to Entity–relationship model · 8
Data model → An, ERD, ERM, ERMs, Like DSD's, The, The E-R, There
related to Data structure diagram · 6
Data model → Data, DSD, DSDs, ER, In DSDs, The
related to Generic data model · 6
Data model → For, Generic, Invariably, The, They, This

Important terminology

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

Important terminology

data model models information database modeling structure entity used conceptual system entities object systems relationships may design language example relational

Data model relationships Subject–Predicate–Object triples

TTTA extracted 160 structured relationships around Data model. Examples in this analysis include Data model → is a → abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities and Data model → is a → abstraction of the design concept used in the implementation of databases. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Data modelis aabstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities0.90text
Data modelis aabstraction of the design concept used in the implementation of databases0.90text
Data modelis aabstraction that defines how the stored symbols relate to the real world0.90text
entitiesinstance offor example concepts0.80text
attributesinstance offor example concepts0.80text
relationsinstance offor example concepts0.80text
or tablesinstance offor example concepts0.80text
relational databasesinstance ofand integrity aspects of the data stored in data management systems0.80text
artificial neural networks that can autonomously create implicit models of data.Data structureA data structure is a way of storing data in a computer so that it can be used efficientlyinstance ofwhole by eliminating unnecessary data redundancies and by relating data structures with relationships.A different approach is to use adaptive systems0.80text
artificial neural networks that can autonomously create implicit models of datainstance ofwhole by eliminating unnecessary data redundancies and by relating data structures with relationships.A different approach is to use adaptive systems0.80text
classinstance ofSuch object models are usually defined using concepts0.80text
messageinstance ofSuch object models are usually defined using concepts0.80text

Related concept clusters Concept neighborhoods

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

  • Data model
    • Model
    • Models
    • Structure
    • Database
    • Modeling
    • Information
    • Systems
    • System
    • Example
    • Object
    • Conceptual
    • Entity
  • data model
    • Model
    • Models
    • Structure
    • Entity
    • Database
    • Modeling
    • Information
    • Conceptual
    • May
    • Systems
    • System
    • Example
  • abstract model
    • Entity
    • Properties
    • Objects
    • Information
    • Conceptual
    • Structure
    • May
    • Database
    • Relationships
    • Entities
    • Example
    • Object
  • data
    • Model
    • Models
    • Structure
    • Database
    • Modeling
    • Information
    • Systems
    • System
    • Conceptual
    • Entity
    • Used
    • Design
  • data modeling
    • Model
    • Models
    • Structure
    • Conceptual
    • Object
    • Information
    • Database
    • Modeling
    • Systems
    • System
    • Business
    • Relationship
  • database design
    • Design
    • System
    • Relational
    • Used
    • Modeling
    • Requirements
    • Model
    • Structured
    • Models
    • Business
    • Relationship
    • Language
  • modeling language
    • Modeling
    • Conceptual
    • Object
    • System
    • Information
    • Properties
    • Architecture
    • Concepts
    • Business
    • Relationship
    • Domain
    • Systems
  • data structure
    • Model
    • Models
    • Structure
    • Database
    • Modeling
    • Information
    • Systems
    • System
    • Conceptual
    • Often
    • Relational
    • Entity

Connections between topic areas Semantic bridges

For Data model, one of the stronger structural bridges in this analysis connects Data model with Related models. 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
Data model — Related models · splits 99 ⟂ 37
Data model — History · splits 109 ⟂ 27
Data model — Overview · splits 111 ⟂ 25
Data model — Types · splits 112 ⟂ 24
Data model — Topics · splits 114 ⟂ 22

Map overview Semantic statistics

Data model

Nodes136
Edges135
Triples160
Avg. degree1.99
Density0.014706
Components1

Source & methodology

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

Source: Wikipedia — Data model · EN edition · Analysis: TopicsToTalkAbout

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