Scanning a training image using a large template size brings about a high dimensionality of the extracted patterns; a preprocessing step would therefore be required to reduce data dimension as much as possible and select part of the features optimally; running clustering algorithms on such a high-dimensional dataset demands for a large computational burden and time.
Motivation: New generation sequencing technologies producing increasingly complex datasets demand new efficient and specialized sequence analysis algorithms.
These massive sequencing datasets demand high-performance computational resources, rapid data transfer, large-scale data storage, and competent data analysts.
The chief challenge is that any dynamical spatio-temporal model (DSTM) of fMRI datasets demands many parameters to describe what is also a large number of observed variables which, nonetheless, enjoy a great deal of spatial redundancy [3, 5, 37].
Albeit, we usually need to estimate a few parameters only, the huge amount of data to be processed for such estimation in video datasets demands a high computational effort.
Naturally, such a dataset would demand efficient algorithms.
Other than these experimental datasets, on-demand animations of Twitter hashtag co-occurrence and diffusion (retweet and mention) networks can be generated with our tool via the Truthy service (truthy.indiana.edu/movies).edu/movies
Summary: Mass spectrometry-based proteomics stands to gain from additional analysis of its data, but its large, complex datasets make demands on speed and memory usage requiring special consideration from scripting languages.
Nevertheless, the Kolmogorov-Smironov Test is with respect to entire distributions in Fig. 6, so the conclusion that this particular dataset does not demand the existence of an h-site is robust.
To identify the transport flows with the greatest potential for modal shift, transport demand datasets are analyzed (see Fig. 1).
