Data-intensive computing

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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: 63
Source areas: 6
Connected nodes: 69
Extracted relationships: 79
Related term clusters: 39
Bridge connections: 69

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 · 29 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.

Start with your topic. Discover where to go next.

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

Data-intensive computing

6Parallel computing · Data parallel · Terabytes

7Internet · World Wide Web · Unstructured information

22Computing platform · Task parallelism · I/O bound

6Relational database management systems · MapReduce · Hadoop

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

For the semantics nerds

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

Advanced semantic analysis

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 Apache Software Foundation, The Hadoop, The Hadoop MapReduce, ZooKeeper Another extracted example is Data-intensive computing → An IDC, Compute-intensive, Data-intensive, EMC Corporation, I/O, Internet, Internet’s, Parallel, Vinton Cerf, World Wide Web. Use these groups to spot repeated connection types before inspecting the individual relationships.

Top relations

related to Hadoop · 19

Data-intensive computing → Apache Hadoop, Avro, Chukwa, GFS, Google, Google MapReduce, Hadoop, Hadoop MapReduce, HBase, HDFS, Hive, Java, MapReduce, Pig, SQL-like, The Apache Software Foundation, The Hadoop, The Hadoop MapReduce, ZooKeeper

related to Introduction · 10

Data-intensive computing → An IDC, Compute-intensive, Data-intensive, EMC Corporation, I/O, Internet, Internet’s, Parallel, Vinton Cerf, World Wide Web

related to HPCC · 9

Data-intensive computing → Custom, ECL, High-Performance Computing Cluster, HPCC, LexisNexis, LexisNexis Risk Solutions, Linux, The ECL, The HPCC

related to MapReduce · 9

Data-intensive computing → Google, Map, MapReduce, Reduce, Since, The Map, The MapReduce, The Reduce, These Map

related to Data-parallelism · 8

Data-intensive computing → Areas, Computer, Data-parallelism, Foundation, Information, NSF, The US National Science, Web

related to System architectures · 8

Data-intensive computing → Facebook, Google, Hadoop, LexisNexis, LexisNexis Risk Solutions, MapReduce, Several, Yahoo

related to Characteristics · 5

Data-intensive computing → Data-intensive, InfiniBand, Large-scale, Newer, Several

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…

related to Approach · 1

Data-intensive computing → Data-intensive

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 79 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.

Subject	Predicate	Object	Confidence	Src
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…	0.90	text
InfiniBand allow data to be stored in a separate repository	instance of	Newer technologies	0.80	text
provide performance comparable to collocated data.The programming model utilized	instance of	Newer technologies	0.80	text
sorting.A focus on reliability	instance of	The programming abstraction and language tools allow the processing to be expressed in terms of data flows and transformations incorporating new dataflow programming languages a…	0.80	text
availability	instance of	The programming abstraction and language tools allow the processing to be expressed in terms of data flows and transformations incorporating new dataflow programming languages a…	0.80	text
data cleansing	instance of	The Thor platform is a cluster whose purpose is to be a data refinery for processing massive volumes of raw data for applications	0.80	text
hygiene	instance of	The Thor platform is a cluster whose purpose is to be a data refinery for processing massive volumes of raw data for applications	0.80	text
extract	instance of	The Thor platform is a cluster whose purpose is to be a data refinery for processing massive volumes of raw data for applications	0.80	text
transform	instance of	The Thor platform is a cluster whose purpose is to be a data refinery for processing massive volumes of raw data for applications	0.80	text
load	instance of	The Thor platform is a cluster whose purpose is to be a data refinery for processing massive volumes of raw data for applications	0.80	text
Data-intensive computing	related to Approach	Data-intensive	0.60	section
Data-intensive computing	related to Characteristics	Several	0.60	section

Related concept clusters Related term clusters

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 ⟂ 30

Data-intensive computing — Introduction · splits 58 ⟂ 12

Data-intensive computing — Data-parallelism · splits 62 ⟂ 8

Data-intensive computing — Overview · splits 63 ⟂ 7

Data-intensive computing — Approach · splits 64 ⟂ 6

Data-intensive computing — Characteristics · splits 64 ⟂ 6

Map overview Semantic statistics

Data-intensive computing

Nodes70

Edges69

Triples79

Avg. degree1.97

Density0.028571

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

Data-intensive computing: Characters, System architectures & Characteristics

Data-intensive computing 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

Introduction

Data-parallelism

Approach

Characteristics

System architectures

For the semantics nerds

Advanced semantic analysis

How Data-intensive computing connects Entity context

Data-intensive computing

Top relations

Important terminology

Important terminology

Data-intensive computing relationships Subject–Predicate–Object triples

Related concept clusters Related term clusters

Connections between topic areas Semantic bridges

Map overview Semantic statistics

Data-intensive computing

Source & methodology