Results 11 to 20 of about 10,119 (225)
Procedia Computer Science, 2013 AbstractRecent innovations in Big Data have enabled major strides forward in our ability to glean important insights from massive amounts of data, and to use these insights to make better decisions. Underlying many of these innovations is a computational paradigm known as MapReduce, which enables computational processes to be scaled up to very large ...
Garcia, Christopher
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Formal derivation of distributed MapReduce [PDF]
, 2014 MapReduce is a powerful distributed data processing model that is currently adopted in a wide range of domains to efficiently handle large volumes of data, i.e., cope with the big data surge.
Salehi Fathabadi, Asieh +9 more
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Garbage collection auto-tuning for Java MapReduce on Multi-Cores [PDF]
, 2011 MapReduce has been widely accepted as a simple programming pattern that can form the basis for efficient, large-scale, distributed data processing. The success of the MapReduce pattern has led to a variety of implementations for different computational ...
Brown, G. +7 more
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Mammoth : gearing Hadoop towards memory-intensive MapReduce applications [PDF]
, 2015 The MapReduce platform has been widely used for large-scale data processing and analysis recently. It works well if the hardware of a cluster is well configured.
Hai Jin +15 more
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Enhanced Failure Detection Mechanism in MapReduce [PDF]
, 2012 The popularity of MapReduce programming model has increased interest in the research community for its improvement. Among the other directions, the point of fault tolerance, concretely the failure detection issue seems to be a crucial one, but that until
Pérez-Hernández, Mar'Ia S. +6 more
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REST-MapReduce: An Integrated Interface but Differentiated Service
Journal of Applied Mathematics, 2014 With the fast deployment of cloud computing, MapReduce architectures are becoming the major technologies for mobile cloud computing. The concept of MapReduce was first introduced as a novel programming model and implementation for a large set of ...
Jong-Hyuk Park +3 more
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ACM Transactions on Architecture and Code Optimization, 2013 The prevalence of chip multiprocessors opens opportunities of running data-parallel applications originally in clusters on a single machine with many cores. MapReduce, a simple and elegant programming model to program large-scale clusters, has recently been shown a promising alternative to harness the multicore platform.
Rong Chen 0001, Haibo Chen 0001
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Practical scalable image analysis and indexing using Hadoop [PDF]
, 2012 The ability to handle very large amounts of image data is important for image analysis, indexing and retrieval applications. Sadly, in the literature, scalability aspects are often ignored or glanced over, especially with respect to the intricacies of ...
Hare, Jonathon S. +5 more
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On using MapReduce to scale algorithms for Big Data analytics: a case study
Journal of Big Data, 2019 Introduction Many data analytics algorithms are originally designed for in-memory data. Parallel and distributed computing is a natural first remedy to scale these algorithms to “Big algorithms” for large-scale data.
Phongphun Kijsanayothin +2 more
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Cloudgene: A graphical execution platform for MapReduce programs on private and public clouds
BMC Bioinformatics, 2012 Background The MapReduce framework enables a scalable processing and analyzing of large datasets by distributing the computational load on connected computer nodes, referred to as a cluster.
Schönherr Sebastian +5 more
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