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Recent advances in data-intensive computing for science discovery are fueling a dramatic growth in the use of data-intensive iterative computations. The utility computing model introduced by cloud computing, combined with the rich set of cloud infrastructure and storage services, offers a very attractive environment in which scientists can perform data analytics. The challenges to large-scale distributed...
Recent advancements in data intensive computing for science discovery are fueling a dramatic growth in use of data-intensive iterative computations. The utility computing model introduced by cloud computing combined with the rich set of cloud infrastructure services offers a very attractive environment for scientists to perform such data intensive computations. The challenges to large scale distributed...
Many scientific applications suffer from the lack of a unified approach to support the management and efficient processing of large-scale data. The Twister MapReduce Framework, which not only supports the traditional MapReduce programming model but also extends it by allowing iterations, addresses these problems. This paper describes how Twister is applied to several kinds of scientific applications...
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