Results 11 to 20 of about 12,223 (205)
MapReduce network enabled algorithms for classification based on association rules [PDF]
, 2011 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional ...
Hammoud, Suhel
core +7 more sources
MapReduce based RDF assisted distributed SVM for high throughput spam filtering [PDF]
, 2013 This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityElectronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis.
Caruana, Godwin
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High performance Monte Carlo computation for finance risk data analysis [PDF]
, 2013 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Finance risk management has been playing an increasingly important role in the finance sector, to analyse finance data and to prevent any potential crisis ...
Zhao, Yu
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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
core +2 more sources
Behavioral simulations in MapReduce [PDF]
Proceedings of the VLDB Endowment, 2010 In many scientific domains, researchers are turning to large-scale behavioral simulations to better understand real-world phenomena. While there has been a great deal of work on simulation tools from the high-performance computing community, behavioral simulations remain challenging to program and automatically scale in parallel environments.
Guozhang Wang +7 more
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2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015 MapReduce is a commonly used framework for executing data-intensive jobs on distributed server clusters. We introduce a variant implementation of MapReduce, namely "Coded MapReduce", to substantially reduce the inter-server communication load for the shuffling phase of MapReduce, and thus accelerating its execution.
Songze Li +2 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
core +1 more source
Computing resources sensitive parallelization of neural neworks for large scale diabetes data modelling, diagnosis and prediction [PDF]
, 2011 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Diabetes has become one of the most severe deceases due to an increasing number of diabetes patients globally.
Qi, Hao
core +7 more sources
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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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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