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DrQuine
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Open Source Big Data - and Parallel Processing
DrQuine   7/23/2014 8:45:56 AM
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Parallel processing is a valuable tool in big data mining. Adding tools that support user friendly parallel processing would make the chip set even more useful.

MClayton200
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Open Source Analytics links and forecast needed
MClayton200   7/18/2014 9:36:58 AM
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Intel has a long history using SAS and its visualization front-end JMP.

Both of those products now support augmentation with the open source R language.

So prototyping the analytics needed to support Hadoop cloud technology is probably underway using SAS/JMP and R.  But there is much more open source software that emulates the MATLAB engineering math tools, which are probably a second part of the mix. I am sure that by now (Its already 11 days since this article was posted) there must be many more web postings on the big data analytics open source catalog of tools and even the likely need of GUI's to supported them in office world.  So how about another update on that catalog and perhaps current global usage trends?  Machine learning algorithms, for example, used by everyone from engineers to marketers and economists could be found in open source.  Many big data blogs are hyping all of these open source tools.  Somebody needs to mine those blogs, talk to Intel, and forecast some scenarios for "the rest of us."  You might be just that forecaster.



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As data rates begin to move beyond 25 Gbps channels, new problems arise. Getting to 50 Gbps channels might not be possible with the traditional NRZ (2-level) signaling. PAM4 lets data rates double with only a small increase in channel bandwidth by sending two bits per symbol. But, it brings new measurement and analysis problems. Signal integrity sage Ransom Stephens will explain how PAM4 differs from NRZ and what to expect in design, measurement, and signal analysis.

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