meeting a week from Saturday

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Sep 23, 2011, 11:42:53 AM9/23/11
to Kansas City R Users Group
Hi R-ers,

A week from this Saturday (Oct 1st) we're going to have our next

2 PM, KC public library plaza branch, large meeting room (by the cow).
You are allowed to buy coffee at the cafe there and bring it in to the

Brian is scheduled to present on ggplot2, but I haven't heard back
from him. Brian, are you ready? I'm going to assume everything is
fine, but everyone else feel free to bring along anything you have to
show or discuss.


Earl F. Glynn

Sep 23, 2011, 10:27:22 PM9/23/11
Thanks for that update. I'm finally scheduled to be in town and hope I can
attend this time.

I plan to attend the webinar described below on Thursday. I'm curious what
item #1 means: "Use of this parallel computing in R is highlighted via the
use of the iterators and foreach packages." I could share anything
interesting from that webinar. [On occasion I sure wish I had access to a
Linux cluster.]


Earl F Glynn
Overland Park, KS


Netezza and Revolution R Enterprise vs. the Cloud and R:
Comparing Performance of Distributed Computing Platforms Using Applications
in Backtesting FINRA's Limit Up/Down Rules
Date: Thursday, September 29, 2011
Time: 11:00 am PT / 2:00 pm ET
Duration: 60 minutes

*Please note: To access this event, please make sure your computer's
"cookies" are enabled and any pop-up blocking software is off.
Yale researchers Michael Kane, PhD and Casey King, PhD considered the FINRA
rules through the lens of historical data analysis. By looking at roughly 24
billion trades from 2008-2010, the Yale researchers studied the efficacy of
the FINRA rules, (as featured in the August 15, 2011 issue of Barrons).
Because of its sheer size a timely analysis of this volume of market data
poses serious computational challenges. In this webinar, three techniques
are compared to meet this challenge.
1. Use of this parallel computing in R is highlighted via the use of the
iterators and foreach packages.
2. The use of the cloud using Amazon Web Services and
3. The use of IBM Netezza analytic appliances integrated with Revolution R
Enterprise, the enterprise-ready distribution of R from Revolution

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