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Currently, MapReduce is the most commonly used data processing programming model.
For NDVI and EVI, we used data processing steps similar to that of temperature: downloading in HDF format from the USGS LPDAAC website [ 31], extracting relevant data layers with MATLAB [ 30] tools, converting from 0.05 degree to 0.25 degree resolution, and accounting for missing data.
The convolution effect on used data processing algorithms: (b --nanoparticle projection, (c)-- nanoparticle volume at minimum b --nanoparticleparticle volume at Laplacian b --nanoparticle
For example, one of the most frequently used data processing tools in computational proteomics, Max Quant uses an experimental design file to assign raw data files to the experiment to which they belong [ 21].
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The features of these signals were obtained using data processing techniques with an identification process developed for the identification of the terrain type.
Using data processing techniques, we classify these methods into three categories: parameterization of one-dimensional variables, computation of bi-normal random variables, and numerical integration.
However it is possible either within the application or by using data processing analysis tools such as aggregation framework [100], MapReduce [101, 102], Hadoop [14, 19] etc.
The two main techniques are used in OMA, one uses data processing in time domain, the second in frequency domain.
Big Data applications use data processing frameworks such as Hadoop MapReduce, which access storage in large data chunks (64 MB HDFS blocks,) therefore exhibiting evident sequentiality.
The data was analyzed using data processing software SPSS (version 21.0, 2012).
The bilayer bilayer distances were measured using data processing in Spider (Frank et al., 1996).
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