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The community are currently aware of the added-value that could be derived from processing and analyzing big datasets.
Analyzing big datasets and extracting pattern are valuable in many applications such as web click stream, network traffic monitoring, wireless sensor network etc.
Techniques for analyzing big datasets often proceed by identifying patterns of co-occurrence or correlation through principal component analysis (PCA) [ 4], multidimensional scaling (MDS) [ 5], etc.
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In fact, by exploiting a variety of learning methods to analyze big datasets, the final purpose is to extract valuable information from massive amounts of data in the form of deep insight or commercial benefits.
Apache Spark stands as a powerful framework to process and to analyze big datasets by using machine learning algorithms such as FP-growth, this algorithm is a powerful technique for association rules extraction.
Although many algorithms can identify a DE subnetwork, most of them are very slow to analyze big datasets.
In the era of genomics, it is extremely important to use appropriate tools to analyze big datasets, and this study is a perfect example of how to achieve a high-resolution genetic analysis from the genomes of more than 3,000 isolates.
It's experimentation, not analyzing big observational datasets that allows a firm to understand whether a relationship is merely correlational or might be reliably predictive because reflects an underlying causal mechanism.
It can effectively process queries and analyze large datasets, achieving in minutes what would have previously taken days.
The ability to analyze large datasets.
In recent years, powerful computational techniques to analyze big data have emerged, making possible the analysis of massive datasets.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com