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Big data: Characters, Applications, Research & Technology

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing software. Data with many entries (rows) offers greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.

Language: English [EN]
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Big data topic overview

The analysis highlights Characters, Applications, Research and Technology as prominent areas in the source structure around Big data.

Related topics
238
Source areas
9
Connected nodes
247
Extracted relationships
285
Concept neighborhoods
71
Bridge connections
247

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.

Overview · 58 topics
Applications · 46 topics
Critique · 35 topics
Technologies · 25 topics
Case studies · 24 topics
Architecture · 21 topics
Research activities · 14 topics
Definition · 12 topics
Characteristics · 3 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

Definition

Characteristics

Architecture

Technologies

Applications

Case studies

Research activities

Critique

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 Big data connects Entity context

The extracted context around Big data shows recurring relationship patterns in the source. For example, Big data → American Association, American Statistical Association, Big Data Meets Social, Big Data Meets Survey, BigSurv, Compared, Craig Hill, Data Science, EP, Fellows, In, Journal, Mitofsky Innovators Award, Public Opinion Research, Royal Statistical Society, Science, Sciences, Since, Social Science Computer Review, There Another extracted example is Big data → Additionally, Agent-based, Alemany Oliver, As, Chris Anderson's, Even, Fed, Finally, Harvard Business Review, If, In, Mark Graham, Matzat, Much, Reips, Snijders, To, Vayre. Use these groups to spot repeated connection types before inspecting the individual relationships.

Big data

Top relations

related to Survey science · 21
Big data → American Association, American Statistical Association, Big Data Meets Social, Big Data Meets Survey, BigSurv, Compared, Craig Hill, Data Science, EP, Fellows, In, Journal, Mitofsky Innovators Award, Public Opinion Research, Royal Statistical Society, Science, Sciences, Since, Social Science Computer Review, There
related to Critiques of the big data paradigm · 18
Big data → Additionally, Agent-based, Alemany Oliver, As, Chris Anderson's, Even, Fed, Finally, Harvard Business Review, If, In, Mark Graham, Matzat, Much, Reips, Snijders, To, Vayre
related to Architecture · 17
Big data → As, Avro, Big, Commercial, DBC, For, GB, Hard, JSON, PB, RDBMS, Since, Systems, Teradata, Teradata Corporation, WinterCorp, XML
has application · 16
Big data → According, Between, Big, Dell, Developed, EMC, HP, IBM, In, Microsoft, Oracle Corporation, SAP, Software AG, The, There, This
related to Critiques of big data execution · 15
Big data → BI, Big, By, Catherine Tucker, Critical Questions, In, Integration, Ramsey, Recent, Researcher, The, This, Ulf-Dietrich Reips, Users, Uwe Matzat
related to Research activities · 15
Big data → American Society, Amir Esmailpour, Artificial Intelligence Laboratory, Big Data Initiative, Encrypted, Engineering Education, Gautam Siwach, In March, March, MIT Computer Science, Moreover, Tackling, The White House, They, UNH Research Group
see also · 14
Big data → Analysis, Aspect, Big, Database, Ethics, EU, Field, Large, Marketing, Organization, Origins, Software, Technological, Type
related to Technology · 11
Big data → Amazon, As, August, Facebook, Google, Hadoop, June, Linux, Linux-based, TB, The
related to COVID-19 · 9
Big data → China, COVID-19, During, Early, Governments, Israel, Significant, South Korea, Taiwan
related to Healthcare · 9
Big data → Big, Human, Some, The, Then, There, This, While, With

Important terminology

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

Important terminology

data big information large analysis research use may used systems sets many processing analytics new using billion one challenges needed

Big data relationships Subject–Predicate–Object triples

TTTA extracted 285 structured relationships around Big data. Examples in this analysis include Big data → is a → National Security Agency and Big data → is a → buzzword and a. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Big datais aNational Security Agency0.90text
Big datais abuzzword and a0.90text
mobile devicesinstance ofand environmental research.The size and number of available data sets have grown rapidly as data is collected by devices0.80text
cheapinstance ofand environmental research.The size and number of available data sets have grown rapidly as data is collected by devices0.80text
numerous information-sensing Internet of things devicesinstance ofand environmental research.The size and number of available data sets have grown rapidly as data is collected by devices0.80text
aerialinstance ofand environmental research.The size and number of available data sets have grown rapidly as data is collected by devices0.80text
health careinstance ofAdvancements in big data analysis offer cost-effective opportunities to improve decision-making in critical development areas0.80text
employmentinstance ofAdvancements in big data analysis offer cost-effective opportunities to improve decision-making in critical development areas0.80text
economic productivityinstance ofAdvancements in big data analysis offer cost-effective opportunities to improve decision-making in critical development areas0.80text
crimeinstance ofAdvancements in big data analysis offer cost-effective opportunities to improve decision-making in critical development areas0.80text
securityinstance ofAdvancements in big data analysis offer cost-effective opportunities to improve decision-making in critical development areas0.80text
and natural disasterinstance ofAdvancements in big data analysis offer cost-effective opportunities to improve decision-making in critical development areas0.80text

Related concept clusters Concept neighborhoods

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

  • Big data
    • Data
    • Analysis
    • Information
    • Research
    • Use
    • Large
    • Analytics
    • Many
    • Sets
    • Challenges
    • Needed
    • Also
  • big data
    • Data
    • Analysis
    • Information
    • Research
    • Use
    • Large
    • Analytics
    • Many
    • Sets
    • Challenges
    • Needed
    • Also
  • data sets
    • Analysis
    • Software
    • Use
    • Analytics
    • Many
    • Information
    • Research
    • Collected
    • Sets
    • Large
    • Often
    • Needed
  • capturing data
    • Analysis
    • Information
    • Use
    • Research
    • Sets
    • Large
    • Systems
    • Many
    • Used
    • Also
    • Analytics
    • Needed
  • data storage
    • Analysis
    • Information
    • Use
    • Research
    • Sets
    • Large
    • Systems
    • Many
    • Used
    • Also
    • Analytics
    • Needed
  • data analysis
    • Large
    • New
    • Sets
    • Big
    • Many
    • Analysis
    • Data
    • Information
    • Challenges
    • Often
    • Analytics
    • Needed
  • information privacy
    • Internet
    • Management
    • Large
    • Collected
    • Technology
    • Uses
    • One
    • Software
    • Development
    • Petabytes
    • Billion
    • Also
  • predictive analytics
    • Large
    • Business
    • Sets
    • Systems
    • Software
    • Information
    • Use
    • Big
    • System
    • Billion
    • Needed
    • Using

Connections between topic areas Semantic bridges

For Big data, one of the stronger structural bridges in this analysis connects Big data with Overview. 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
Big data — Overview · splits 189 ⟂ 59
Big data — Applications · splits 201 ⟂ 47
Big data — Critique · splits 212 ⟂ 36
Big data — Technologies · splits 222 ⟂ 26
Big data — Case studies · splits 223 ⟂ 25
Big data — Architecture · splits 226 ⟂ 22
Big data — Research activities · splits 233 ⟂ 15
Big data — Definition · splits 235 ⟂ 13
Big data — Characteristics · splits 244 ⟂ 4

Map overview Semantic statistics

Big data

Nodes248
Edges247
Triples285
Avg. degree1.99
Density0.008065
Components1

Source & methodology

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

Source: Wikipedia — Big data · EN edition · Analysis: TopicsToTalkAbout

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