Map-reduce-merge: simplified relational data processing on large clusters

By Hung-chih Yang, A. Dasdan, Ruey-Lung Hsiao, D. S. Parker. ACM SIGMOD Conference, 2007

875 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/4e84dd37e9898d9655b55d99678da12eeda06615

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In Computer Networks and Communications

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Citations in the corpus

Scientific Cloud Computing: Early Definition and Experience (2008, methodology): “The Map-Reduce-Merge [59] method evolves the MapReduce paradigm by adding a “merge” operation.”
Mars: A MapReduce Framework on graphics processors (2008, background): “[7] added the merge operation to MapReduce for relational databases.”
FlumeJava: easy, efficient data-parallel pipelines (2010, background): “Map-Reduce-Merge [ 19 ] extends the MapReduce model by adding an additional Merge step, making it possible to express additional types of computations, such as relational algebra, in a single execution.”
SCOPE: easy and efficient parallel processing of massive data sets (2008, methodology): “The extensible commands provides the same functionality as the map-reduce model described in [5] and the operations map, reduce, and merge described in [12].”
Hadoop++ (2010, background): “Over the past three years MapReduce has attained considerable interest from both the database and systems research community [7, 13, 23, 15, 16, 12, 3, 20, 8, 19, 22, 14, 4, 9, 6].”
A comparison of join algorithms for log processing in MaPreduce (2010, neutral): “Our results provide insights that are unique to the MapReduce platform and offer guidance on when to use a particular join algorithm on this platform.”
MapReduce for Data Intensive Scientific Analyses (2008, background): “[19] adds another phase “merge” to MapReduce style programming, mainly to handle the various join operations in database queries.”
Efficient parallel set-similarity joins using MapReduce (2010, background): “In [30] the authors proposed extending the interface with a new function called “merge” in order to facilitate joins.”
Pig latin: a not-so-foreign language for data processing (2008, background): “A variant of map-reduce that deals well with joins has been proposed [14], but it still does not deal with multistage programs.”
Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud (2011, neutral): “soft’s Dryad [14], or Yahoo!’s Map-Reduce-Merge [6].”
A Survey of Large Scale Data Management Approaches in Cloud Environments (2011, methodology): “In principle, the design of the MapReduce framework has considered the following main principles [56]: • Low-Cost Unreliable Commodity Hardware: Instead of”
Nephele/PACTs: a programming model and execution framework for web-scale analytical processing (2010, background): “An extension to map/reduce with an additional function called merge has been suggested in [20].”