What is recorded here?
Presented a map-and-reduce programming model and runtime that automatically partitions work, schedules it across clusters and handles machine failures for large data-processing jobs.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the 2004 OSDI paper and its reported Google implementation. It does not claim that the paper invented all parallel functional primitives or represents every later data-processing system.
Read the original work
- Google Research: MapReduce
- Dean and Ghemawat, MapReduce: Simplified Data Processing on Large Clusters
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Jeffrey Dean; Sanjay Ghemawat
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
Research locations and affiliations
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Documented countries / regions: United States
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English catalogue narrative
Dean and Ghemawat: MapReduce
Presented a map-and-reduce programming model and runtime that automatically partitions work, schedules it across clusters and handles machine failures for large data-processing jobs.
Scientific scope
The record is the 2004 OSDI paper and its reported Google implementation. It does not claim that the paper invented all parallel functional primitives or represents every later data-processing system.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Jeffrey Dean; Sanjay Ghemawat
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.