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The explosive growth of data in volume, velocity and diversity that are produced by medical applications has contributed to abundance of big data.
Functional data in volume space were projected onto surface vertices in a direction perpendicular to the grey-white matter boundary, and thus converted to surface space.
With the growth of biological data in volume and heterogeneity, web search engines become key tools for researchers.
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The reason a 3D construction model works so well is that it provides an opportunity to make small processors much better at processing huge amounts of data – in volumes that would otherwise necessitate a round-trip to a data center or processor farm.
The absence of reliable longitudinal data in volume-outcome research has been highlighted as a concern when trying to predict, with any certainty, the success of centralising services to improve outcomes.
And startups are booming, as the amount of data increases in volume and speed.
As data grows in volume, issues of scale are hitting the mid-market companies just as they have with large enterprise shops.
As the world of data explodes in volume and complexity, many applications and databases need to manage this influx of queries with high performance.
The data included in Volume IX were collected between 1998 and 2002.
They just can't move data in the volume and velocity that is the norm in today's world.
Why? Next-gen breach detection is solving, in essence, a classic big-data problem: To be effective, these tools need to analyze a great variety of data in high volume, and at great velocity, to determine potential breaches.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com