Tag: predictive

Teradata updates Teradata-R

The Teradata add-on package for R

teradataR is a package or library that allows R users to easily connect to Teradata, establish data frames (R data formats) to Teradata and to call in-database analytic functions within Teradata.  This allows R users to work within their R console environment while leveraging the in-database functions developed with Teradata Warehouse Miner. This package provides 44 different analytical functions and an additional 20 data connection and R infrastructure functions.  In addition, we’ve added a function that will list the stored procedures within Teradata provide the capability to call functions from R.

  • 20 Functions to enable R infrastructure to operate with Teradata
  • tdConnect – Connect to Teradata via ODBC
  • Td.data.frame – Establish data frame connections to a Teradata table
  • 44 in-database analytical functions callable from R.  Sample of the functions include:
  • Descriptive statistics: Overlap, histogram, frequency, statistics, matrix functions, and values analysis
  • Reorganization functions: join, merge and samples
  • Transformations: bincode, recode, rescale, sigmoid, zscore and null replacement
  • K-Means clustering and Score K-Means
  • Statistical tests: ks, dagostino.pearson, shapiro.wilk, bionomial, and wilcoxon
  • R language features nrow, ncol, min, max, summary, as.dataframe, and dim
  • Tool and R functions that allow users to create their own custom analytic functions that’s callable by R.
  • Teradata Warehouse Miner can capture any analytic stream including UDFs and create a stored procedure
    • Analytic process to create new derived predictive variables can be captured as a stored procedure.
    • Entire process to create or update an analytical data set can be captured as a stored procedure.
    • R function can list all the stored procedures within Teradata.
    • R function can call a stored procedure that runs in-database

TeradataR allows R users to leverage all the benefits of in-database processing with Teradata:

  • Eliminate data movement from Teradata to the R framework for key data intensive tasks.
  • Leverage the speed of Teradata database’s parallel processing to run analytics against big data.
  • Ability to operate within the R console environment.
  • Embed your frequently performed tasks to run in-database.
  • R and TeradataR are free downloads.

Source- http://developer.teradata.com/applications/articles/in-database-analytics-with-teradata-r

This package allows users of R to interact with a Teradata database. R is an open source language for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible. Users can use many statistical functions directly against the Teradata system without having to extract the data into memory.

Enhancements included with this new 1.0.1 release include:

  • teradataR User Guide
  • addition of Mac OS X Package
  • addition of Red Hat Linux Package (added 2/23/12)
  • summary has been enhanced to run faster
  • JDBC support added to allow Windows or Mac users to run the package with JDBC
  • td.data.frame enhanced to allow support for manipulation to add columns and expressions
  • td.data.frame enhanced to use Teradata 14.0 Fastpath Transform Functions (see Appendix B)
  • td.tapply function added to apply a select group of functions to columns of an array

From-http://downloads.teradata.com/download/applications/teradata-r

and

A new R package for Red Hat Linux has been added to the teradataR 1.0.1 release. This new package provides the same functionality as in the previously released Windows and Mac OS X packages, but is built for Red Hat Linux. This version was built and tested on Red Hat Linux 6.2 32-bit. (The R version for Red Hat Linux is 2.14.1)

Installing this package is the same as any normal R package; just extract it into your R library area, or use the install.packagescommand with the file path.

from- http://developer.teradata.com/tag/r

and

With plenty of prolific and enthusiastic developers, the number of packages for R is expected to grow tremendously. Statisticians and analysts using these packages will find innovative ways to use data to answer their research and business questions. And as organizations become more willing to rely on open-source software for mission-critical tasks, R is poised to become an essential tool for analyzing our complex world.

Source-http://www.teradatamagazine.com/v09n03/Connections/R-you-ready/

From the user guide-

http://downloads.teradata.com/download/applications/teradata-r

teradataR allows R users to easily connect to Teradata, establish td data frames (virtual R data frames) to
Teradata and to call in-database analytic functions within Teradata. This allows R users to work within their R
console environment while leveraging the in-database functions

A Function List
teradataR-package  Allow access to Teradata via R
as.data.frame.td.data.frame Convert td data frame to a data frame
as.td.data.frame Coerce to a td data frame
dim.td.data.frame Dimensions of a td data frame
hist.td.data.frame Histograms
Is.td.data.frame Is an Object a Teradata Data Frame
Is.td.expression Is an Object a Teradata Expression
mean.td.data.frame Arithmetic Mean
median.td.data.frame Median Value
min.td.data.frame Minima
predict.kmeans Kmeans Model Prediction
print.td.data.frame Show contents of a td data frame
sum.td.data.frame Sum of column
summary.td.data.frame Summary of Teradata Data Frame
Td.bincode Create Table of Bincode Values
Td.binomial Binomial Test
Td.binomialsign Binomial Sign Test
Td.call.sp Locate and call stored procedure
Td.cor Correlation Matrix
Td.cov Covariance Matrix
Td.dagostino.pearson D’Agostino Pearson Test
Td.data.frame Teradata Data Frames
Td.f.oneway One way F Test
Td.factanal Factor Analysis
Td.freq Frequency Analysis
Td.hist Histograms
Td.join Join Tables in Teradata
Td.kmeans K-Means Clustering
Td.ks Kolmogorov Smirnov Test
Td.lilliefors Lilliefors Test
Td.merge Merge Rows of Teradata Tables
Td.mode Mode Value of Column
Td.mwnkw Mann-Whitney/Kruskal Wallis Test
Td.nullreplace Replace Null Values
Td.overlap Overlap
Td.quantiles Quantile Values
Td.rank Rank
Td.recode Recode
Td.rescale Rescale Values of Column
Td.sample Sample Rows
Td.shapiro.wilk Shapiro Wilk
Td.sigmoid Sigmoid Transformation
Td.smirnov Smirnov Test
Td.solve Solve a system of equations
Td.stats General Statistics
Td.t.paired T Test Paired
Td.t.unpaired T Test Unpaired
Td.t.unpairedi T Test – Unpaired Indicator
Td.values Values
Td.wilcoxon Wilcoxon Test
Td.zscore Zscore Transformation
tdClose Close connection
tdConnect Connect to Teradata database
tdMetadataDB Set metadata database
tdQuery Query Teradata Database
teradataR Allow access to Teradata via R
[.td.data.frame Extract Teradata Data Frame
[<-.td.data.frame Replace value of Teradata Data Frame

Facebook and R

Part 1 How do people at Facebook use R?

tamar Rosenn, Facebook

Itamar conveyed how Facebook’s Data Team used R in 2007 to answer two questions about new users: (i) which data points predict whether a user will stay? and (ii) if they stay, which data points predict how active they’ll be after three months?

For the first question, Itamar’s team used recursive partitioning (via the rpartpackage) to infer that just two data points are significantly predictive of whether a user remains on Facebook: (i) having more than one session as a new user, and (ii) entering basic profile information.

For the second question, they fit the data to a logistic model using a least angle regression approach (via the lars package), and found that activity at three months was predicted by variables related to three classes of behavior: (i) how often a user was reached out to by others, (ii) frequency of third party application use, and (iii) what Itamar termed “receptiveness” — related to how forthcoming a user was on the site.

source-http://www.dataspora.com/2009/02/predictive-analytics-using-r/

and cute graphs like the famous

https://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919

 

and

studying baseball on facebook

https://www.facebook.com/notes/facebook-data-team/baseball-on-facebook/10150142265858859

by counting the number of posts that occurred the day after a team lost divided by the total number of wins, since losses for great teams are remarkable and since winning teams’ fans just post more.

 

But mostly at

https://www.facebook.com/data?sk=notes and https://www.facebook.com/data?v=app_4949752878

 

and creating new packages

1. jjplot (not much action here!)

https://r-forge.r-project.org/scm/viewvc.php/?root=jjplot

though

I liked the promise of JJplot at

http://pleasescoopme.com/2010/03/31/using-jjplot-to-explore-tipping-behavior/

2. ising models

https://github.com/slycoder/Rflim

https://www.facebook.com/note.php?note_id=10150359708746212

3. R pipe

https://github.com/slycoder/Rpipe

 

even the FB interns are cool

http://brenocon.com/blog/2009/02/comparison-of-data-analysis-packages-r-matlab-scipy-excel-sas-spss-stata/

 

Part 2 How do people with R use Facebook?

Using the API at https://developers.facebook.com/tools/explorer

and code mashes from

 

http://romainfrancois.blog.free.fr/index.php?post/2012/01/15/Crawling-facebook-with-R

http://applyr.blogspot.in/2012/01/mining-facebook-data-most-liked-status.html

but the wonderful troubleshooting code from http://www.brocktibert.com/blog/2012/01/19/358/

which needs to be added to the code first

 

and using network package

>access_token=”XXXXXXXXXXXX”

Annoyingly the Facebook token can expire after some time, this can lead to huge wait and NULL results with Oauth errors

If that happens you need to regenerate the token

What we need
> require(RCurl)
> require(rjson)
> download.file(url=”http://curl.haxx.se/ca/cacert.pem&#8221;, destfile=”cacert.pem”)

Roman’s Famous Facebook Function (altered)

> facebook <- function( path = “me”, access_token , options){
+ if( !missing(options) ){
+ options <- sprintf( “?%s”, paste( names(options), “=”, unlist(options), collapse = “&”, sep = “” ) )
+ } else {
+ options <- “”
+ }
+ data <- getURL( sprintf( “https://graph.facebook.com/%s%s&access_token=%s&#8221;, path, options, access_token ), cainfo=”cacert.pem” )
+ fromJSON( data )
+ }

 

Now getting the friends list
> friends <- facebook( path=”me/friends” , access_token=access_token)
> # extract Facebook IDs
> friends.id <- sapply(friends$data, function(x) x$id)
> # extract names
> friends.name <- sapply(friends$data, function(x) iconv(x$name,”UTF-8″,”ASCII//TRANSLIT”))
> # short names to initials
> initials <- function(x) paste(substr(x,1,1), collapse=””)
> friends.initial <- sapply(strsplit(friends.name,” “), initials)

This matrix can take a long time to build, so you can change the value of N to say 40 to test your network. I needed to press the escape button to cut short the plotting of all 400 friends of mine.
> # friendship relation matrix
> N <- length(friends.id)
> friendship.matrix <- matrix(0,N,N)
> for (i in 1:N) {
+ tmp <- facebook( path=paste(“me/mutualfriends”, friends.id[i], sep=”/”) , access_token=access_token)
+ mutualfriends <- sapply(tmp$data, function(x) x$id)
+ friendship.matrix[i,friends.id %in% mutualfriends] <- 1
+ }

 

Plotting using Network package in R (with help from the  comments at http://applyr.blogspot.in/2012/01/mining-facebook-data-most-liked-status.html)

> require(network)

>net1<- as.network(friendship.matrix)

> plot(net1, label=friends.initial, arrowhead.cex=0)

(Rgraphviz is tough if you are on Windows 7 like me)

but there is an alternative igraph solution at https://github.com/sciruela/facebookFriends/blob/master/facebook.r

 

After all that-..talk.. a graph..of my Facebook Network with friends initials as labels..

 

Opinion piece-

I hope plans to make the Facebook R package get fulfilled (just as the twitteR  package led to many interesting analysis)

and also Linkedin has an API at http://developer.linkedin.com/apis

I think it would be interesting to plot professional relationships across social networks as well. But I hope to see a LinkedIn package (or blog code) soon.

As for jjplot, I had hoped ggplot and jjplot merged or atleast had some kind of inclusion in the Deducer GUI. Maybe a Google Summer of Code project if people are busy!!

Also the geeks at Facebook.com can think of giving something back to the R community, as Google generously does with funding packages like RUnit, Deducer and Summer of Code, besides sponsoring meet ups etc.

 

(note – this is part of the research for the upcoming book ” R for Business Analytics”)

 

ps-

but didnt get time to download all my posts using R code at

https://gist.github.com/1634662#

or do specific Facebook Page analysis using R at

http://tonybreyal.wordpress.com/2012/01/06/r-web-scraping-r-bloggers-facebook-page-to-gain-further-information-about-an-authors-r-blog-posts-e-g-number-of-likes-comments-shares-etc/

Updated-

 #access token from https://developers.facebook.com/tools/explorer
access_token="AAuFgaOcVaUZAssCvL9dPbZCjghTEwwhNxZAwpLdZCbw6xw7gARYoWnPHxihO1DcJgSSahd67LgZDZD"
require(RCurl)
 require(rjson)
# download the file needed for authentication http://www.brocktibert.com/blog/2012/01/19/358/
download.file(url="http://curl.haxx.se/ca/cacert.pem", destfile="cacert.pem")
# http://romainfrancois.blog.free.fr/index.php?post/2012/01/15/Crawling-facebook-with-R
facebook <- function( path = "me", access_token = token, options){
if( !missing(options) ){
options <- sprintf( "?%s", paste( names(options), "=", unlist(options), collapse = "&", sep = "" ) )
} else {
options <- ""
}
data <- getURL( sprintf( "https://graph.facebook.com/%s%s&access_token=%s", path, options, access_token ), cainfo="cacert.pem" )
fromJSON( data )
}

 # see http://applyr.blogspot.in/2012/01/mining-facebook-data-most-liked-status.html

# scrape the list of friends
friends <- facebook( path="me/friends" , access_token=access_token)
# extract Facebook IDs
friends.id <- sapply(friends$data, function(x) x$id)
# extract names 
friends.name <- sapply(friends$data, function(x)  iconv(x$name,"UTF-8","ASCII//TRANSLIT"))
# short names to initials 
initials <- function(x) paste(substr(x,1,1), collapse="")
friends.initial <- sapply(strsplit(friends.name," "), initials)

# friendship relation matrix
#N <- length(friends.id)
N <- 200
friendship.matrix <- matrix(0,N,N)
for (i in 1:N) {
  tmp <- facebook( path=paste("me/mutualfriends", friends.id[i], sep="/") , access_token=access_token)
  mutualfriends <- sapply(tmp$data, function(x) x$id)
  friendship.matrix[i,friends.id %in% mutualfriends] <- 1
}
require(network)
net1<- as.network(friendship.matrix)
plot(net1, label=friends.initial, arrowhead.cex=0)

Created by Pretty R at inside-R.org

Teradata Analytics

A recent announcement showing Teradata partnering with KXEN and Revolution Analytics for Teradata Analytics.

http://www.teradata.com/News-Releases/2012/Teradata-Expands-Integrated-Analytics-Portfolio/

The Latest in Open Source Emerging Software Technologies
Teradata provides customers with two additional open source technologies – “R” technology from Revolution Analytics for analytics and GeoServer technology for spatial data offered by the OpenGeo organization – both of which are able to leverage the power of Teradata in-database processing for faster, smarter answers to business questions.

In addition to the existing world-class analytic partners, Teradata supports the use of the evolving “R” technology, an open source language for statistical computing and graphics. “R” technology is gaining popularity with data scientists who are exploiting its new and innovative capabilities, which are not readily available. The enhanced “R add-on for Teradata” has a 50 percent performance improvement, it is easier to use, and its capabilities support large data analytics. Users can quickly profile, explore, and analyze larger quantities of data directly in the Teradata Database to deliver faster answers by leveraging embedded analytics.

Teradata has partnered with Revolution Analytics, the leading commercial provider of “R” technology, because of customer interest in high-performing R applications that deliver superior performance for large-scale data. “Our innovative customers understand that big data analytics takes a smart approach to the entire infrastructure and we will enable them to differentiate their business in a cost-effective way,” said David Rich, chief executive officer, Revolution Analytics. “We are excited to partner with Teradata, because we see great affinity between Teradata and Revolution Analytics – we embrace parallel computing and the high performance offered by multi-core and multi-processor hardware.”

and

The Teradata Data Lab empowers business users and leading analytic partners to start building new analytics in less than five minutes, as compared to waiting several weeks for the IT department’s assistance.

“The Data Lab within the Teradata database provides the perfect foundation to enable self-service predictive analytics with KXEN InfiniteInsight,” said John Ball, chief executive officer, KXEN. “Teradata technologies, combined with KXEN’s automated modeling capabilities and in-database scoring, put the power of predictive analytics and data mining directly into the hands of business users. This powerful combination helps our joint customers accelerate insight by delivering top-quality models in orders of magnitude faster than traditional approaches.”

Read more at

http://www.sacbee.com/2012/03/06/4315500/teradata-expands-integrated-analytics.html

Interview Prof Benjamin Alamar , Sports Analytics

Here is an interview with Prof Benjamin Alamar, founding editor of the Journal of Quantitative Analysis in Sport, a professor of sports management at Menlo College and the Director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA.

Ajay – The movie Moneyball recently sparked out mainstream interest in analytics in sports.Describe the role of analytics in sports management

Benjamin- Analytics is impacting sports organizations on both the sport and business side.
On the Sport side, teams are using analytics, including advanced data management, predictive anlaytics, and information systems to gain a competitive edge. The use of analytics results in more accurate player valuations and projections, as well as determining effective strategies against specific opponents.
On the business side, teams are using the tools of analytics to increase revenue in a variety of ways including dynamic ticket pricing and optimizing of the placement of concession stands.
Ajay-  What are the ways analytics is used in specific sports that you have been part of?

Benjamin- A very typical first step for a team is to utilize the tools of predictive analytics to help inform their draft decisions.

Ajay- What are some of the tools, techniques and software that analytics in sports uses?
Benjamin- The tools of sports analytics do not differ much from the tools of business analytics. Regression analysis is fairly common as are other forms of data mining. In terms of software, R is a popular tool as is Excel and many of the other standard analysis tools.
Ajay- Describe your career journey and how you became involved in sports management. What are some of the tips you want to tell young students who wish to enter this field?

Benjamin- I got involved in sports through a company called Protrade Sports. Protrade initially was a fantasy sports company that was looking to develop a fantasy game based on advanced sports statistics and utilize a stock market concept instead of traditional drafting. I was hired due to my background in economics to develop the market aspect of the game.

There I met Roland Beech (who now works for the Mavericks) and Aaron Schatz (owner of footballoutsiders.com) and learned about the developing field of sports statistics. I then changed my research focus from economics to sports statistics and founded the Journal of Quantitative Analysis in Sports. Through the journal and my published research, I was able to establish a reputation of doing quality, useable work.

For students, I recommend developing very strong data management skills (sql and the like) and thinking carefully about what sort of questions a general manager or coach would care about. Being able to demonstrate analytic skills around actionable research will generally attract the attention of pro teams.

About-

Benjamin Alamar, Professor of Sport Management, Menlo College

Benjamin Alamar

Professor Benjamin Alamar is the founding editor of the Journal of Quantitative Analysis in Sport, a professor of sports management at Menlo College and the Director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA. He has published academic research in football, basketball and baseball, has presented at numerous conferences on sports analytics. He is also a co-creator of ESPN’s Total Quarterback Rating and a regular contributor to the Wall Street Journal. He has consulted for teams in the NBA and NFL, provided statistical analysis for author Michael Lewis for his recent book The Blind Side, and worked with numerous startup companies in the field of sports analytics. Professor Alamar is also an award winning economist who has worked academically and professionally in intellectual property valuation, public finance and public health. He received his PhD in economics from the University of California at Santa Barbara in 2001.

Prof Alamar is a speaker at Predictive Analytics World, San Fransisco and is doing a workshop there

http://www.predictiveanalyticsworld.com/sanfrancisco/2012/agenda.php#day2-17

2:55-3:15pm

All level tracks Track 1: Sports Analytics
Case Study: NFL, MLB, & NBA
Competing & Winning with Sports Analytics

The field of sports analytics ties together the tools of data management, predictive modeling and information systems to provide sports organization a competitive advantage. The field is rapidly developing based on new and expanded data sources, greater recognition of the value, and past success of a variety of sports organizations. Teams in the NFL, MLB, NBA, as well as other organizations have found a competitive edge with the application of sports analytics. The future of sports analytics can be seen through drawing on these past successes and the developments of new tools.

You can know more about Prof Alamar at his blog http://analyticfootball.blogspot.in/ or journal at http://www.degruyter.com/view/j/jqas. His detailed background can be seen at http://menlo.academia.edu/BenjaminAlamar/CurriculumVitae

Predictive Models Ain’t Easy to Deploy

 

This is a guest blog post by Carole Ann Matignon of Sparkling Logic. You can see more on Sparkling Logic at http://my.sparklinglogic.com/

Decision Management is about combining predictive models and business rules to automate decisions for your business. Insurance underwriting, loan origination or workout, claims processing are all very good use cases for that discipline… But there is a hiccup… It ain’t as easy you would expect…

What’s easy?

If you have a neat model, then most tools would allow you to export it as a PMML model – PMML stands for Predictive Model Markup Language and is a standard XML representation for predictive model formulas. Many model development tools let you export it without much effort. Many BRMS – Business rules Management Systems – let you import it. Tada… The model is ready for deployment.

What’s hard?

The problem that we keep seeing over and over in the industry is the issue around variables.

Those neat predictive models are formulas based on variables that may or may not exist as is in your object model. When the variable is itself a formula based on the object model, like the min, max or sum of Dollar amount spent in Groceries in the past 3 months, and the object model comes with transaction details, such that you can compute it by iterating through those transactions, then the problem is not “that” big. PMML 4 introduced some support for those variables.

The issue that is not easy to fix, and yet quite frequent, is when the model development data model does not resemble the operational one. Your Data Warehouse very likely flattened the object model, and pre-computed some aggregations that make the mapping very hard to restore.

It is clearly not an impossible project as many organizations do that today. It comes with a significant overhead though that forces modelers to involve IT resources to extract the right data for the model to be operationalized. It is a heavy process that is well justified for heavy-duty models that were developed over a period of time, with a significant ROI.

This is a show-stopper though for other initiatives which do not have the same ROI, or would require too frequent model refresh to be viable. Here, I refer to “real” model refresh that involves a model reengineering, not just a re-weighting of the same variables.

For those initiatives where time is of the essence, the challenge will be to bring closer those two worlds, the modelers and the business rules experts, in order to streamline the development AND deployment of analytics beyond the model formula. The great opportunity I see is the potential for a better and coordinated tuning of the cut-off rules in the context of the model refinement. In other words: the opportunity to refine the strategy as a whole. Very ambitious? I don’t think so.

About Carole Ann Matignon

http://my.sparklinglogic.com/index.php/company/management-team

Carole-Ann Matignon Print E-mail

Carole-Ann MatignonCarole-Ann Matignon – Co-Founder, President & Chief Executive Officer

She is a renowned guru in the Decision Management space. She created the vision for Decision Management that is widely adopted now in the industry.  Her claim to fame is managing the strategy and direction of Blaze Advisor, the leading BRMS product, while she also managed all the Decision Management tools at FICO (business rules, predictive analytics and optimization). She has a vision for Decision Management both as a technology and a discipline that can revolutionize the way corporations do business, and will never get tired of painting that vision for her audience.  She speaks often at Industry conferences and has conducted university classes in France and Washington DC.

She started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication. At Cleversys (acquired by Kurt Salmon & Associates), she also conducted strategic consulting gigs around change management.

While playing with advanced software components, she found a passion for technology and joined ILOG (acquired by IBM). She developed a growing interest in Optimization as well as Business Rules. At ILOG, she coined the term BRMS while brainstorming with her Sales counterpart. She led the Presales organization for Telecom in the Americas up until 2000 when she joined Blaze Software (acquired by Brokat Technologies, HNC Software and finally FICO).

Her 360-degree experience allowed her to gain appreciation for all aspects of a software company, giving her a unique perspective on the business. Her technical background kept her very much in touch with technology as she advanced.

Oracle launches its version of R #rstats

From-

http://www.oracle.com/us/corporate/press/1515738

Integrates R Statistical Programming Language into Oracle Database 11g

News Facts

Oracle today announced the availability of Oracle Advanced Analytics, a new option for Oracle Database 11g that bundles Oracle R Enterprise together with Oracle Data Mining.
Oracle R Enterprise delivers enterprise class performance for users of the R statistical programming language, increasing the scale of data that can be analyzed by orders of magnitude using Oracle Database 11g.
R has attracted over two million users since its introduction in 1995, and Oracle R Enterprise dramatically advances capability for R users. Their existing R development skills, tools, and scripts can now also run transparently, and scale against data stored in Oracle Database 11g.
Customer testing of Oracle R Enterprise for Big Data analytics on Oracle Exadata has shown up to 100x increase in performance in comparison to their current environment.
Oracle Data Mining, now part of Oracle Advanced Analytics, helps enable customers to easily build and deploy predictive analytic applications that help deliver new insights into business performance.
Oracle Advanced Analytics, in conjunction with Oracle Big Data Appliance, Oracle Exadata Database Machine and Oracle Exalytics In-Memory Machine, delivers the industry’s most integrated and comprehensive platform for Big Data analytics.

Comprehensive In-Database Platform for Advanced Analytics

Oracle Advanced Analytics brings analytic algorithms to data stored in Oracle Database 11g and Oracle Exadata as opposed to the traditional approach of extracting data to laptops or specialized servers.
With Oracle Advanced Analytics, customers have a comprehensive platform for real-time analytic applications that deliver insight into key business subjects such as churn prediction, product recommendations, and fraud alerting.
By providing direct and controlled access to data stored in Oracle Database 11g, customers can accelerate data analyst productivity while maintaining data security throughout the enterprise.
Powered by decades of Oracle Database innovation, Oracle R Enterprise helps enable analysts to run a variety of sophisticated numerical techniques on billion row data sets in a matter of seconds making iterative, speed of thought, and high-quality numerical analysis on Big Data practical.
Oracle R Enterprise drastically reduces the time to deploy models by eliminating the need to translate the models to other languages before they can be deployed in production.
Oracle R Enterprise integrates the extensive set of Oracle Database data mining algorithms, analytics, and access to Oracle OLAP cubes into the R language for transparent use by R users.
Oracle Data Mining provides an extensive set of in-database data mining algorithms that solve a wide range of business problems. These predictive models can be deployed in Oracle Database 11g and use Oracle Exadata Smart Scan to rapidly score huge volumes of data.
The tight integration between R, Oracle Database 11g, and Hadoop enables R users to write one R script that can run in three different environments: a laptop running open source R, Hadoop running with Oracle Big Data Connectors, and Oracle Database 11g.
Oracle provides single vendor support for the entire Big Data platform spanning the hardware stack, operating system, open source R, Oracle R Enterprise and Oracle Database 11g.
To enable easy enterprise-wide Big Data analysis, results from Oracle Advanced Analytics can be viewed from Oracle Business Intelligence Foundation Suite and Oracle Exalytics In-Memory Machine.

Supporting Quotes

“Oracle is committed to meeting the challenges of Big Data analytics. By building upon the analytical depth of Oracle SQL, Oracle Data Mining and the R environment, Oracle is delivering a scalable and secure Big Data platform to help our customers solve the toughest analytics problems,” said Andrew Mendelsohn, senior vice president, Oracle Server Technologies.
“We work with leading edge customers who rely on us to deliver better BI from their Oracle Databases. The new Oracle R Enterprise functionality allows us to perform deep analytics on Big Data stored in Oracle Databases. By leveraging R and its library of open source contributed CRAN packages combined with the power and scalability of Oracle Database 11g, we can now do that,” said Mark Rittman, co-founder, Rittman Mead.
Oracle Advanced Analytics — an option to Oracle Database 11g Enterprise Edition – extends the database into a comprehensive advanced analytics platform through two major components: Oracle R Enterprise and Oracle Data Mining. With Oracle Advanced Analytics, customers have a comprehensive platform for real-time analytic applications that deliver insight into key business subjects such as churn prediction, product recommendations, and fraud alerting.

Oracle R Enterprise tightly integrates the open source R programming language with the database to further extend the database with Rs library of statistical functionality, and pushes down computations to the database. Oracle R Enterprise dramatically advances the capability for R users, and allows them to use their existing R development skills and tools, and scripts can now also run transparently and scale against data stored in Oracle Database 11g.

Oracle Data Mining provides powerful data mining algorithms that run as native SQL functions for in-database model building and model deployment. It can be accessed through the SQL Developer extension Oracle Data Miner to build, evaluate, share and deploy predictive analytics methodologies. At the same time the high-performance Oracle-specific data mining algorithms are accessible from R.

BENEFITS
  • Scalability—Allows customers to easily scale analytics as data volume increases by bringing the algorithms to where the data resides – in the database
  • Performance—With analytical operations performed in the database, R users can take advantage of the extreme performance of Oracle Exadata
  • Security—Provides data analysts with direct but controlled access to data in Oracle Database 11g, accelerating data analyst productivity while maintaining data security
  • Save Time and Money—Lowers overall TCO for data analysis by eliminating data movement and shortening the time it takes to transform “raw data” into “actionable information”
Oracle R Hadoop Connector Gives R users high performance native access to Hadoop Distributed File System (HDFS) and MapReduce programming framework.
This is a  R package
From the datasheet at

Predictive Analytics World Events in 2012

A new line up of Predictive Analytics World and Text Analytics World conferences and workshops are coming March through July, plus see the save-the-dates and call-for-speakers for events in Sept, Oct, and Nov.

CONFERENCE: Predictive Analytics World – San Francisco

March 4-10, 2012 in San Francisco, CA
http://predictiveanalyticsworld.com/sanfrancisco/2012
Discount Code for $150 off: AJAYBP12

CONFERENCE: Text Analytics World – San Francisco
March 6-7, 2012 in San Francisco, CA
http://textanalyticsworld.com/sanfrancisco/2012
Discount Code for $150 off: AJAYBP12

VARIOUS ANALYTICS WORKSHOPS:
A plethora of 1-day workshops are held alongside PAW and TAW
For details see: http://pawcon.com/sanfrancisco/2012/analytics_workshops.php

SEMINAR: Predictive Analytics for Business, Marketing & Web
March 22-23, 2012 in New York City, NY
July 26-27, 2012 in São Paulo, Brazil
Oct 11-12, 2012 in San Francisco
A concentrated training program lead by PAW’s chair, Eric Siegel
http://businessprediction.com

CONFERENCE: Predictive Analytics World – Toronto
April 25-26, 2012 in Toronto, Ontario
http://predictiveanalyticsworld.com/toronto/2012
Discount Code for $150 off: AJAYBP12

CONFERENCE: Predictive Analytics World – Chicago
June 25-26, 2012 in Chicago, IL
http://www.predictiveanalyticsworld.com/chicago/2012/
Discount Code for $150 off: AJAYBP12

 

From Ajay-

CONTEST- If you use the discount code AJAYBP12, you will not only get the $150 off, but you will be entered in a contest to get 2 complementary passes like I did last year . Matt Stromberg won that one

http://www.decisionstats.com/contest-2-free-passes-to-predictive-analytics-world/

 

see last year results-

http://www.decisionstats.com/congrats-to-matt-stromberg-winner-2-free-passes-to-paw-new-york/

%d bloggers like this: