Posts Tagged ‘ Big Data ’

Big Data Analytics to Revolutionize Services

March 6, 2012
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Revolution Analytics' CEO Dave Rich was interviewed by Wikibon's David Vellante and SiliconAngle's John Furrier at the Strata 2012 conference last week. Given Dave's background at Accenture Analytics, the conversation naturally turned to impact of predictive analytics and R on business services. (See the video after the jump, below.) Bret Latmore of SiliconANGLE provides highlights of the interview, including...

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Webinar tomorrow: Big-data statistics with Revolution R with IBM Netezza

February 28, 2012
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As explained in detail by Michele Chambers at the IBM Netezza blog, there are two keys to getting fast performance with statistical analysis on massive data sets with R: Massive parallelization: break the problem down into small pieces, and run them in parallel Bring the R engine to the data (not the other way around), to avoid data transfer...

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R integrated throughout the enterprise analytics stack

February 27, 2012
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The past couple of years have seen a dramatic growth in the use of the R language in the enterprise. R has always been pervasive in academia for research and teaching in statistics and data science, and as new graduates trained in R have migrated to the workplace the demand for R in corporations has become more and more...

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RHadoop updated: improved performance and more control

February 27, 2012
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Revolution Analytics' open-source RHadoop project, which provides integration between R and Hadoop, has been updated with the release of version 1.2 of the "rmr" package. New in this version: support for binary I/O formats, which improves on the text-only interfact by allowing use of faster and more space-efficient data formats like R's native serialization format. This version also improves...

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Big data seminar in London on 1 March 2012

February 20, 2012
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Big data seminar in London on 1 March 2012

Removable disk packs in 1975Source: Wikipedia, via Deutsche Fotothek  David Chan from City University is organising an interdisciplinary symposium on tackling the ‘Big Data’ challenge on 1 March 2012.It is an open seminar trying to bring ...

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Forbes: Top 20 influencers in Big Data

February 3, 2012
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Haydn Shaughnessy at The Forbes blog provides a list of the "Top 20 Influencers in Big Data", and I'm humbled to report that yours truly is listed there at #2. It's an instantaneous ranking based on the social-media tracking tool Traakr, but it's still great to be listed alongside writers for SiliconAngle, GigaOM, and KDNuggets (and even Mashable!). I...

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Norman Nie on two big problems with Big Data

December 23, 2011
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Revolution Analytics CEO Norman Nie sat down with Cassimir Medford from Business Agility to talk about the problems business today face with respect to Big Data. The two big problems identified: finding adequately trained personnel and locating the right tools. Norman traces the problem of finding skilled practitioners to work with Big Data to the US educational system: The...

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EMC survey differentiates BI and Data Science

December 15, 2011
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EMC survey differentiates BI and Data Science

EMC last week published the results of a survey of 462 IT decision makers who self-identified as either a data scientist or business intelligence professional (plus 35 invitees who were attendees at the EMC Data Scientist Summity and/or Kaggle competitors). There's a nice summary of the conclusions at the EMC blog, (where data scientists are described as "The New...

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RHadoop update: new tools for Hadoop map-reduce tasks in R

December 13, 2011
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The open-source RHadoop project to integrate R and Hadoop continues apace, with a new version of the rmr package released this week. Changes in this version improve performance when storing and retrieving R objects from Hadoop with a native serialization process, support for equijoins (a MapReduce-style merge) and some new higher-level R functions to make writing map-reduce tasks simpler....

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Why we need to deal with big data in R

November 22, 2011
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Responding to the birth rates analysis in the post earlier this week on big-data analysis with Revolution R Enterprise, Luis Apiolaza asks at the Quantum Forests blog, do we really need to deal with big data in R? My basic question is why would I want to deal with all those 100 million records directly in R? Wouldn’t it...

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