Supercomputers Unleash Big Data's Power - InformationWeek

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6/24/2015
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Lisa Morgan
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Supercomputers Unleash Big Data's Power

Here are six reasons why established companies, and even startups, are using supercomputing resources, and why your IT organization may want to consider such options to meet your big data and business analytics needs.
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The Dataset Fits Into Memory 
If a dataset is terascale or you're combining multiple terascale datasets, the data may too large to fit into memory. One way to deal with it is to break it down into smaller pieces and analyze the pieces individually. While it's possible to do many kinds of analysis on the fragments of data, more types of analysis can be done when all of the data is available in memory and the analyses can be executed faster. It is also possible to ask more types of questions, expand the scope of discovery, and do more correlations.
'If I want the right answer to a question, bringing in all the needed data, bringing it in fully, being able to query it fully, best enables me to come to the right conclusions,' said JICS director Tony Mezzacappa in an interview.
One example is identifying fraudulent healthcare claims, which involves massive amounts of data. By bringing all the data into memory, it's possible to discover patterns that are not apparent if the claims are viewed in isolation, according to Ken Gilbert, director of Business Analytics at the University of Tennessee office of research and economic development, in an interview.
(Image: OpenIcons via Pixabay)

The Dataset Fits Into Memory

If a dataset is terascale or you're combining multiple terascale datasets, the data may too large to fit into memory. One way to deal with it is to break it down into smaller pieces and analyze the pieces individually. While it's possible to do many kinds of analysis on the fragments of data, more types of analysis can be done when all of the data is available in memory and the analyses can be executed faster. It is also possible to ask more types of questions, expand the scope of discovery, and do more correlations.

"If I want the right answer to a question, bringing in all the needed data, bringing it in fully, being able to query it fully, best enables me to come to the right conclusions," said JICS director Tony Mezzacappa in an interview.

One example is identifying fraudulent healthcare claims, which involves massive amounts of data. By bringing all the data into memory, it's possible to discover patterns that are not apparent if the claims are viewed in isolation, according to Ken Gilbert, director of Business Analytics at the University of Tennessee office of research and economic development, in an interview.

(Image: OpenIcons via Pixabay)

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