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request MapReduce for Data Intensive Scientific Analyses



Mapreduce for data intensive scientific analyses
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Abstract:Most scientific data analyses comprise analyzing voluminous data collected from various instruments. Efficient parallel/concurrent algorithms and frameworks are the key to meeting the scalability and performance requirements entailed in such scientific data

request paper Analyzing the impact of social media on social movements: A computational study on Twitter and the Occupy Wall Street movement



Analyzing the impact of social media on social movements: a computational study on Twitter and the Occupy Wall Street movement
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Abstract:The extensive use of digital social media by social movement actors is an emerging trend that restructures the communication dynamics of social protest, and it is widely credited with contributing to the successful mobilizations of recent movements (eg,

request paper Anti eavesdropping space time network coding for cooperative communications



Anti-eavesdropping space-time network coding for cooperative communications
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Abstract:Due to the broadcast nature of wireless medium, wireless transmissions can be overheard by any undesired receivers with eavesdropping capability within source transmission range. A novel physical layer approach for secure wireless cooperative

MapReduce online.
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MapReduce is a popular framework for data-intensive distributed computing of batch jobs. To simplify fault tolerance, many implementations of MapReduce materialize the entire output of each map and reduce task before it can be consumed. In this paper, we propose a

MapReduce : Simplified data processing on large clusters
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MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all

Improving MapReduce performance in heterogeneous environments.
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MapReduce is emerging as an important programming model for large-scale data-parallel applications such as web indexing, data mining, and scientific simulation. Hadoop is an open-source implementation of MapReduce enjoying wide adoption and is often used for

Airavat: Security and privacy for MapReduce .
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21 ? Output of the computation is also an information channel Output 1 million if Peter bought Vi*gra Peter Meg Reduce Map Data Chris Page 22. Airavat mechanisms 22 Prevent leaks through storage channels like network connections, files Reduce Map Mandatory access control

Map - reduce for machine learning on multicore
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We are at the beginning of the multicore era. Computers will have increasingly many cores (processors), but there is still no good programming framework for these architectures, and thus no simple and unified way for machine learning to take advantage of the potential

Job scheduling for multi-user mapreduce clusters
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Sharing a MapReduce cluster between users is attractive because it enables statistical multiplexing (lowering costs) and allows users to share a common large data set. However, we find that traditional scheduling algorithms can perform very poorly in MapReduce due to

Reining in the Outliers in Map - Reduce Clusters using Mantri.
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Experience from an operational Map - Reduce cluster reveals that outliers significantly prolong job completion. e causes for outliers include run-time contention for processor, memory and other resources, disk failures, varying bandwidth and congestion along network

Optimizing MapReduce for multicore architectures
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MapReduce is a programming model for data-parallel programs originally intended for data centers. MapReduce simplifies parallel programming, hiding synchronization and task management. These properties make it a promising programming model for future

MapReduce : A major step backwards
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On January a Database Column reader asked for our views on new distributed database research efforts, and well begin here with our views on MapReduce . This is a good time to discuss it, since the recent trade press has been filled with news of the revolution of so

Pairwise document similarity in large collections with MapReduce
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This paper presents a MapReduce algorithm for computing pairwise document similarity in large document collections. MapReduce is an attractive framework because it allows us to decompose the inner products involved in computing document similarity into separate

Handling Data Skew in MapReduce .
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MapReduce systems have become popular for processing large data sets and are increasingly being used in e-science applications. In contrast to simple application scenarios like word count, e-science applications involve complex computations which pose

See spot run: using spot instances for mapreduce workflows.
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MapReduce is a scalable and fault tolerant framework, patented by Google, for computing embarrassingly parallel reductions. Hadoop is an open-source implementation of Google MapReduce that is made available as a web service to cloud users by the Amazon Web

Map - reduce meets wider varieties of applications
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Recent studies and industry practices build data-center-scale computer systems to meet the high storage and processing demands of data-intensive and compute-intensive applications, such as web searches. The Map - Reduce programming model is one of the

Mapreduce : Distributed computing for machine learning
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We use Hadoop, an open-source implementation of Googles distributed file system and the MapReduce framework for distributed data processing, on modestly-sized compute clusters to evaluate its efficacy for standard machine learning tasks. We show benchmark

Experiences with MapReduce , an abstraction for large-scale computation
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002 874 Mar, 06 17 834 097 Average map tasks per job 411 Unique map / reduce combinations 144 Average reduce tasks per job 1.9 Average worker deaths per job 232 Average worker machines 941 Output data written (TB) 756 Intermediate data (TB) 1 571 Input dataThe MapReduce model uses a barrier between the Map and Reduce stages. This provides simplicity in both programming and implementation. However, in many situations, this barrier hurts performance because it is overly restrictive. Hence, we develop a method to break the

A study of skew in mapreduce applications
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This paper presents a study of skew highly variable task runtimes in MapReduce applications. We describe various causes and manifestations of skew as observed in real world Hadoop applications. Runtime task distributions from these applications demonstrate

Towards energy efficient mapreduce
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Energy considerations are important for Internet datacenters operators, and MapReduce is a common Internet datacenter application. In this work, we use the energy efficiency of MapReduce as a new perspective for increasing Internet datacenter productivity. We offer a

Clustering very large multi-dimensional datasets with MapReduce .
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Given a very large moderate-to-high dimensionality dataset, how could one cluster its points For datasets that dont fit even on a single disk, parallelism is a first class option. In this paper we explore MapReduce for clustering this kind of data. The main questions are (a)Cloud infrastructures enable the efficient parallel execution of data-intensive tasks such as entity resolution on large datasets. We investigate challenges and possible solutions of using the MapReduce programming model for parallel entity resolution using Sorting -SOFTWARE SALES SERVICE-https://www.engpaper.net--