Saptarshi Guha on Hadoop, R

[This article was first published on Revolutions, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Saptarshi Guha (author of the Rhipe package) joins the likes of Ebay, Yahoo, Twitter and Facebook and as one of just 37 presenters at the Hadoop World conference. (Revolution Analytics is proud to sponsor Saptarshi's presence at this event, which take place in New York on October 12.) He'll be talking about using R and Hadoop to analyze Voice-over-IP network data for Quality of Service, as explained in this abstract:

RHIPE is an R package that integrates the R environment for statistics and data analysis with the Hadoop distributed computing framework. With RHIPE, the user can store and compute with large and complex data sets using R functions and programming idioms. In this talk, I will demonstrate the use of RHIPE to analyze 190Gb of VoIP network data for QoS. The jitter between two consecutive packets is the deviation of the real inter-arrival time from theoretical. We show jitter follows desired properties and is negligible, which supports the assumption of the measured traffic being close to the offered traffic.

Saptarshi explains more about his talk, and his work with Hadoop and R, in this interview at the Cloudera blog.

Cloudera blog: Purdue University’s Saptarshi Guha Interviewed Regarding Hadoop, R and Hadoop World

To leave a comment for the author, please follow the link and comment on their blog: Revolutions. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)