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MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster.
The analysis highlights Applications and Products as prominent areas in the source structure around MapReduce.
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.
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.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
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The extracted context around MapReduce shows recurring relationship patterns in the source. For example, MapReduce → Apache Hive, B-trees, Bigtable, CODASYL, David DeWitt, DeWitt, Greg Jorgensen, HBase, Jorgensen, MapReduce's, Michael Stonebraker, Pig, PigLatin, Sawzall, Stonebraker's, Teradata Another extracted example is MapReduce → At Google, Development, FlumeJava, Google, Google's, MillWheel, Moreover, Percolator, World Wide Web. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
reduce map data key distributed function framework operation output input one system processing parallel value would use google programming values
TTTA extracted 63 structured relationships around MapReduce. Examples in this analysis include MapReduce → is a → programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster.A MapReduce program is comp… and the Combiner function can help to reduce the amount of data written to disk → instance of → Additional modules. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| MapReduce | is a | programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster.A MapReduce program is comp… | 0.90 | text |
| the Combiner function can help to reduce the amount of data written to disk | instance of | Additional modules | 0.80 | text |
| and transmitted over the network | instance of | Additional modules | 0.80 | text |
| Percolator | instance of | Development at Google has since moved on to technologies | 0.80 | text |
| FlumeJava | instance of | Development at Google has since moved on to technologies | 0.80 | text |
| MillWheel that offer streaming operation | instance of | Development at Google has since moved on to technologies | 0.80 | text |
| updates instead of batch processing | instance of | Development at Google has since moved on to technologies | 0.80 | text |
| to allow integrating | instance of | Development at Google has since moved on to technologies | 0.80 | text |
| B-trees | instance of | MapReduce's use of input files and lack of schema support prevents the performance improvements enabled by common database system features | 0.80 | text |
| hash partitioning | instance of | MapReduce's use of input files and lack of schema support prevents the performance improvements enabled by common database system features | 0.80 | text |
| though projects such as Pig | instance of | MapReduce's use of input files and lack of schema support prevents the performance improvements enabled by common database system features | 0.80 | text |
| Hadoop | instance of | may cover use of MapReduce by open source software | 0.80 | text |
The concept neighborhoods around MapReduce bring nearby vocabulary together. In this analysis, examples include Map, Data and Reduce. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For MapReduce, one of the stronger structural bridges in this analysis connects MapReduce 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.
TTTA analyzes the structure around MapReduce to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as 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 — MapReduce · EN edition · Analysis: TopicsToTalkAbout
