Google's MapReduce programming model - Revisited

By Ralf Lämmel. Science of Computer Programming, 2007

585 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/9173b9161d72801ff3cea2d8eede3658df391345

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Citations in the corpus

PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations (2009, background): “The typical framework is as follows [29]: (a) the map stage sequentially passes over the input file and outputs (key, value) pairs; (b) the shuffling stage groups of all values by key, (c) the reduce stage processes the values with the same key and outputs the final result.”
Map-reduce-merge: simplified relational data processing on large clusters (2007, background): “However, [ 14 ] pointed out that the reduce function output [v3] can be in dierent type from its input [”
Parallel K-Means Clustering Based on MapReduce (2009, background): “MapReduce [4,5,6,7] is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks.”
Sindice.com: a document-oriented lookup index for open linked data (2008, methodology): “…optimising data processing by adjusting our indexing algorithms to better utiliselocalityofdataintheparallelarchitecture(Deanand Ghemawat,2004;Lämmel,2008);improvingthecrawling schedulerwithexistingalgorithmsforsourcescheduling (Aggarwaletal.,2001;Pantetal.,2004)andduplicate…”
DOULION: counting triangles in massive graphs with a coin (2009, background): “More details about MAPREDUCE and HADOOP can be found in [19].”
A Survey on Distributed Machine Learning (2019, background): “Furthermore, the paradigm of map and reduce operations is not ideal to support the data ow of iterative tasks since it essentially restricts it to a tree-structure [86] Apache Spark has been developed in response to this challenge.”
GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries (2012, background): “The general framework is as follows [23]: (a) the map stage reads the input file and outputs (key, value) pairs; (b) the shuffling stage sorts the output and distributes them to reducers; (c) the reduce stage processes the values with the same key and outputs another (key, value) pairs which become ”