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This chapter deals with the architectures that are needed or designed to process Big Data and the different techniques that can be adopted for processing vast amounts of data.
Using AI-powered tools capable of processing vast amounts of data and making real-time recommendations, some hospitals and insurers are discovering that they can reduce administrative hours, especially in the areas of regulatory documentation and fraudulent claims.
By processing vast amounts of data, data scientists are now able to solve multi-layered problems.
Processing vast amounts of data may lead to situations in which data controllers know many of the characteristics, behaviors and whereabouts of people.
Pervasive DataRush is less about exploring data than about efficiently processing vast amounts of data in a way that gets the most out of inexpensive hardware.
With the ever-increasing need for processing vast amounts of data, researchers often rely on off-the-shelf machine learning solutions to tackle their problems using so-called black-boxes.
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The notes fly past and the brain is in overdrive, busy processing vast amounts of information on the page.
However, by processing vast amounts of raw data and performing unprecedented large-scale analyses beyond the reach of teams of human experts, they can provide valuable insight into some of the most basic aspects of musical discourse, including the evolution of popular music over the years.
The Watson Solutions includes, Information Management Software, Watson is the first commercially available cognitive computing platform that has the ability to interact in natural language, processing vast amounts of big data, and learning from its interactions with people and computers.
The popularity of NoSQL databases has increased due to the need of (1) processing vast amount of data faster than the relational database management systems by taking the advantage of highly scalable architecture, (2) flexible (schema-free) data structure, and, (3) low latency and high performance.
Modern computers process vast amounts of data that represent various aspects of the world.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com