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Multicore, GPU and FPGA are not well suited for processing large data sets.
MapReduce is a popular programming model for processing large data sets.
Google's MapReduce programming model serves for processing large data sets in a massively parallel manner.
MapReduce, a parallel programming model for processing large data sets, greatly simplifies the complexity of distributed parallel programming.
Advantages of fingerprints are that they are fast to compute and well suited for processing large data sets.
Map-reduce is a programming technique for processing large data sets with a parallel, distributed algorithm on a cluster.
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For those of you unfamiliar with Hadoop, "[It's] fundamentally infrastructure software for storing and processing large data sets," according to Mike Gualtieri, a Forrester analyst who covers this space.
In addition, computational requirements for processing large hyperspectral data sets might be prohibitive and a method for selecting a data subset is therefore sought [33].
Feature selection is important and necessary especially for processing large scale data.
In this research, we focus on Hadoop framework and Memcached, which are distributed model frameworks for processing large scale data intensive applications for different purposes.
Consequently, bioinformatics components of systems biology pipelines are subject of intense research oriented on improving their accuracy in interpreting and analyzing raw NGS data, as well as on the development of effective computing strategies for processing large amounts of data.
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