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V-Hadoop: Virtualized Hadoop using containers
2016 IEEE 15th International Symposium on Network Computing and Applications (NCA), 2016MapReduce is a popular programming model used to process large amounts of data by exploiting parallelism. Open-source implementations of MapReduce such as Hadoop are generally best suited for large, homogeneous clusters of commodity machines. However, many businesses cannot afford to invest in such infrastructure and others are reluctant to use cloud ...
Srihari Radhakrishnan +2 more
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Hadoop superlinear scalability
Communications of the ACM, 2015The perpetual motion of parallel performance.
Neil J. Gunther +2 more
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, 2018 Articolul dat relevă o scurtă introducere în frameworkul Apache Hadoop - framework predestinat lucrului cu masive de informație, oferind o performanță și flexibilitate deosebită. Este descrisă premisa apariției acestuia.
MADIUDIN, Radu
core +3 more sources
, 2009 You've heard the hype about Hadoop: it runs petabyte--scale data mining tasks insanely fast, it runs gigantic tasks on clouds for absurdly cheap, it's been heavily committed to by tech giants like IBM, Yahoo!, and the Apache Project, and it's completely ...
Venner, J
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2011
This chapter introduces the MapReduce solution for distributed computation. It explains the fundamentals of MapReduce and describes in which scenarios it can be applied (basically, processing of massive data by easily parallelizable algorithms). Also, this chapter gives an overview of the open source project Hadoop, an implementation of MapReduce.
Luis Rodero-Merino, Gilles Fedak
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This chapter introduces the MapReduce solution for distributed computation. It explains the fundamentals of MapReduce and describes in which scenarios it can be applied (basically, processing of massive data by easily parallelizable algorithms). Also, this chapter gives an overview of the open source project Hadoop, an implementation of MapReduce.
Luis Rodero-Merino, Gilles Fedak
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Clustering Protein Structures with Hadoop
2016Machine learning is a widely used technique in structural biology, since the analysis of large conformational ensembles originated from single protein structures (e.g. derived from NMR experiments or molecular dynamics simulations) can be approached by partitioning the original dataset into sensible subsets, revealing important structural and dynamics ...
G Paschina +4 more
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Proceedings of the 40th ACM technical symposium on Computer science education, 2009
The potential benefits of data-intensive scalable computing (DISC) in CS education are considered in the context of a small college with an active student-operated Beowulf cluster initiative. The map-reduce computational model, of great importance in industry, is reviewed, and the Hadoop implementation of that model
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The potential benefits of data-intensive scalable computing (DISC) in CS education are considered in the context of a small college with an active student-operated Beowulf cluster initiative. The map-reduce computational model, of great importance in industry, is reviewed, and the Hadoop implementation of that model
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2014
Recently, I was talking with a friend about possibly using Hadoop to speed up reporting on his company’s “massive” data warehouse of 4TB. (He heads the IT department of one of the biggest real estate companies in the Chicago area.) Although he grudgingly agreed to a possible performance benefit, he asked very confidently, “But what about encrypting our
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Recently, I was talking with a friend about possibly using Hadoop to speed up reporting on his company’s “massive” data warehouse of 4TB. (He heads the IT department of one of the biggest real estate companies in the Chicago area.) Although he grudgingly agreed to a possible performance benefit, he asked very confidently, “But what about encrypting our
openaire +1 more source