MapReduce Software Framework
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- See: Hadoop YARN, BIGTABLE.
- Michael Stonebraker. http://cacm.acm.org/magazines/2015/1/181612-a-valuable-lesson-and-whither-hadoop/fulltext
- (Dean & Ghemawat, 2010) ⇒ Jeffrey Dean, and Sanjay Ghemawat. (2010). “MapReduce: A Flexible Data Processing Tool.” In: Communications of the ACM Journal, 53(1). doi:10.1145/1629175.1629198
- (Lin & Dyer, 2010) ⇒ Jimmy Lin, and Chris Dyer. (2010). “Data-Intensive Text Processing with MapReduce." Morgan Claypool Publishers doi:10.2200/S00274ED1V01Y201006HLT007 ISBN:1608453421
- MapReduce is a patented software framework introduced by Google to support distributed computing on large data sets on clusters of computers.
- The framework is inspired by map and reduce functions commonly used in functional programming, although their purpose in the MapReduce framework is not the same as their original forms.
- MapReduce libraries have been written in C++, C#, Erlang, Java, Python, Ruby, F#, R and other programming languages.
- (Dean & Ghemawat, 2008) ⇒ Jeffrey Dean, and Sanjay Ghemawat. (2008). “MapReduce: Simplified Data Processing on Large Clusters.” Communications of the ACM 51, no. 1
- (Chu et al., 2006) ⇒ Cheng-Tao Chu, Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, Gary Bradski, Andrew Y. Ng, and Kunle Olukotun. (2006). “Map-Reduce for Machine Learning on Multicore.” In: Proceedings of the 19th International Conference on Neural Information Processing Systems.
- QUOTE: ... We adapt Google's map-reduce  paradigm to demonstrate this parallel speed up technique on a variety of learning algorithms including locally weighted linear regression (LWLR), k-means, logistic regression (LR), naive Bayes (NB), SVM, ICA, PCA, gaussian discriminant analysis (GDA), EM, and backpropagation (NN). ...
- (Dean & Ghemawat, 2004a) ⇒ Jeffrey Dean, and Sanjay Ghemawat. (2004). “System and method for efficient large-scale data processing." US Patent 7,650,331
- (Dean & Ghemawat, 2004b) ⇒ Jeffrey Dean, and Sanjay Ghemawat. (2004). “MapReduce: Simplified Data Processing on Large Clusters.” In: Proceedings of the 6th Conference on Symposium on Operating Systems Design & Implementation (OSDI 2004).
- "Our abstraction is inspired by the map and reduce primitives present in Lisp and many other functional languages." -"MapReduce: Simplified Data Processing on Large Clusters", by Jeffrey Dean and Sanjay Ghemawat; from Google Labs
- "Google's MapReduce Programming Model -- Revisited" — paper by Ralf Lämmel; from Microsoft