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Most of the existing tools would crawl if such a huge data is presented as input [5].
The visual inspection of such huge data is not a satisfactory procedure for precise and reliable diagnosis as it is time-consuming, error prone and subject to fatigue.
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Consequently, huge data are created on the social network.
Store and analysis of those huge data are not easy and computer scientists bring out a solution called cloud computing.
As literature often advices to use the highest possible Markov order for a given pattern problem (which means m = h - 2 for pattern of size h) it is easy to understand that such a practice could have very detrimental effects on the computed statistics unless huge data are available for estimation purpose.
The particular system chosen for the study, because huge experimental data is available, consistent explanation is timely needed.
Such huge mass of data is usually stored in cloud data centers.
Collecting and parsing huge amounts of data is the lifeblood of the AI ecosystem.
To analyse such a huge amount of data is time consuming and has a high computational complexity.
This huge amount of data is processed on more than 140 computing centers distributed across 34 countries.
Also, a future in which huge amounts of data is collected by many of our household products can easily be viewed as either exciting or terrifying.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com