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Data-intensive computing: Characters, System architectures & Characteristics

Data-intensive computing is a class of parallel computing applications which use a data parallel approach to process large volumes of data typically terabytes or petabytes in size and typically referred to as big data. Computing applications that devote most of their execution time to computational requirements are deemed compute-intensive, whereas…

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Data-intensive computing topic overview

The analysis highlights Characters, System architectures and Characteristics as prominent areas in the source structure around Data-intensive computing.

Related topics
64
Source areas
6
Connected nodes
70
Extracted relationships
97
Concept neighborhoods
39
Bridge connections
70

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.

System architectures · 30 topics
Introduction · 11 topics
Data-parallelism · 7 topics
Overview · 6 topics
Approach · 5 topics
Characteristics · 5 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.

Suggested research paths

A focused starting point derived from the topic graph, ranked independently of the source article order.

Start with these areas

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

Introduction

Data-parallelism

Approach

Characteristics

System architectures

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-intensive computing connects Entity context

The extracted context around Data-intensive computing shows recurring relationship patterns in the source. For example, Data-intensive computing → Apache Hadoop, Avro, Chukwa, GFS, Google, Google MapReduce, Hadoop, Hadoop MapReduce, HBase, HDFS, Hive, Java, MapReduce, Pig, SQL-like, The, The Apache Software Foundation, The Hadoop, The Hadoop MapReduce, These Another extracted example is Data-intensive computing → An IDC, Compute-intensive, Data-intensive, EMC Corporation, I/O, In, Internet, Internet’s, Parallel, Such, The, This, Vinton Cerf, World Wide Web. Use these groups to spot repeated connection types before inspecting the individual relationships.

Data-intensive computing

Top relations

related to Hadoop · 21
Data-intensive computing → Apache Hadoop, Avro, Chukwa, GFS, Google, Google MapReduce, Hadoop, Hadoop MapReduce, HBase, HDFS, Hive, Java, MapReduce, Pig, SQL-like, The, The Apache Software Foundation, The Hadoop, The Hadoop MapReduce, These
related to Introduction · 14
Data-intensive computing → An IDC, Compute-intensive, Data-intensive, EMC Corporation, I/O, In, Internet, Internet’s, Parallel, Such, The, This, Vinton Cerf, World Wide Web
related to MapReduce · 12
Data-intensive computing → For, Google, In, Map, MapReduce, Reduce, Since, The, The Map, The MapReduce, The Reduce, These Map
related to HPCC · 10
Data-intensive computing → Custom, ECL, High-Performance Computing Cluster, HPCC, LexisNexis, LexisNexis Risk Solutions, Linux, The, The ECL, The HPCC
related to Data-parallelism · 9
Data-intensive computing → Areas, Computer, Data-parallelism, Foundation, Information, NSF, The, The US National Science, Web
related to System architectures · 9
Data-intensive computing → Facebook, Google, Hadoop, However, LexisNexis, LexisNexis Risk Solutions, MapReduce, Several, Yahoo
related to Characteristics · 8
Data-intensive computing → Data-intensive, InfiniBand, Large-scale, Newer, Several, The, This, To
related to Approach · 4
Data-intensive computing → Data-intensive, In, These, This
is a · 1
Data-intensive computing → class of parallel computing applications which use a data parallel approach to process large volumes of data typically terabytes or petabytes in size and typically referred to a…

Important terminology

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

Important terminology

data processing computing data-intensive applications parallel system mapreduce hadoop distributed programming nodes execution architecture cluster typically performance systems language large

Data-intensive computing relationships Subject–Predicate–Object triples

TTTA extracted 97 structured relationships around Data-intensive computing. Examples in this analysis include Data-intensive computing → is a → class of parallel computing applications which use a data parallel approach to process large volumes of data typically terabytes or petabytes in size and typically referred to a… and InfiniBand allow data to be stored in a separate repository → instance of → Newer technologies. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Data-intensive computingis aclass of parallel computing applications which use a data parallel approach to process large volumes of data typically terabytes or petabytes in size and typically referred to a…0.90text
InfiniBand allow data to be stored in a separate repositoryinstance ofNewer technologies0.80text
provide performance comparable to collocated data.The programming model utilizedinstance ofNewer technologies0.80text
sorting.A focus on reliabilityinstance ofThe programming abstraction and language tools allow the processing to be expressed in terms of data flows and transformations incorporating new dataflow programming languages a…0.80text
availabilityinstance ofThe programming abstraction and language tools allow the processing to be expressed in terms of data flows and transformations incorporating new dataflow programming languages a…0.80text
data cleansinginstance ofThe Thor platform is a cluster whose purpose is to be a data refinery for processing massive volumes of raw data for applications0.80text
hygieneinstance ofThe Thor platform is a cluster whose purpose is to be a data refinery for processing massive volumes of raw data for applications0.80text
extractinstance ofThe Thor platform is a cluster whose purpose is to be a data refinery for processing massive volumes of raw data for applications0.80text
transforminstance ofThe Thor platform is a cluster whose purpose is to be a data refinery for processing massive volumes of raw data for applications0.80text
loadinstance ofThe Thor platform is a cluster whose purpose is to be a data refinery for processing massive volumes of raw data for applications0.80text
Data-intensive computingrelated to ApproachData-intensive0.60section
Data-intensive computingrelated to ApproachThis0.60section

Related concept clusters Concept neighborhoods

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

  • Data-intensive computing
    • Data-intensive
    • Data
    • Parallel
    • Applications
    • Systems
    • Processing
    • Large
    • Volumes
    • Typically
    • System
    • Approach
    • Requirements
  • data-intensive computing
    • Data-intensive
    • Data
    • Parallel
    • Applications
    • Systems
    • Typically
    • Processing
    • Large
    • Volumes
    • Distributed
    • System
    • Performance
  • parallel computing
    • Data-intensive
    • Parallel
    • Applications
    • Data
    • Processing
    • Systems
    • Typically
    • System
    • Distributed
    • Cluster
    • Performance
    • Programming
  • data parallel
    • Processing
    • Data-intensive
    • Parallel
    • Typically
    • System
    • Cluster
    • Programming
    • Requirements
    • Volumes
    • Application
    • Nodes
    • Platform
  • big data
    • Processing
    • Data-intensive
    • Parallel
    • Typically
    • Programming
    • Requirements
    • Volumes
    • Nodes
    • Distributed
    • System
    • Analysis
    • Input
  • computing platform
    • Data-intensive
    • Parallel
    • Applications
    • Data
    • Systems
    • Typically
    • Processing
    • Analysis
    • Provides
    • Distributed
    • System
    • Performance
  • parallel programming
    • Language
    • Processing
    • Typically
    • System
    • Systems
    • Cluster
    • Programming
    • Google
    • Application
    • Platform
    • Set
    • Tasks
  • computing clusters
    • Data-intensive
    • Parallel
    • Applications
    • Data
    • Systems
    • Typically
    • Processing
    • Distributed
    • System
    • Performance
    • Algorithms
    • Approach

Connections between topic areas Semantic bridges

For Data-intensive computing, one of the stronger structural bridges in this analysis connects Data-intensive computing with System architectures. 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-intensive computing — System architectures · splits 40 ⟂ 31
Data-intensive computing — Introduction · splits 59 ⟂ 12
Data-intensive computing — Data-parallelism · splits 63 ⟂ 8
Data-intensive computing — Overview · splits 64 ⟂ 7
Data-intensive computing — Approach · splits 65 ⟂ 6
Data-intensive computing — Characteristics · splits 65 ⟂ 6

Map overview Semantic statistics

Data-intensive computing

Nodes71
Edges70
Triples97
Avg. degree1.97
Density0.028169
Components1

Source & methodology

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

Source: Wikipedia — Data-intensive computing · EN edition · Analysis: TopicsToTalkAbout

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