Latest R Journal

Including juicy stuff on using a cluster of Apple Machines for grid computing , seasonality forecasting (Yet Another Package For Time Series )

But I kind of liked Sumo too-

https://code.google.com/p/sumo/

Sumo is a fully-functional web application template that exposes an authenticated user’s R session within java server pages.

Sumo: An Authenticating Web Application with an Embedded R Session by Timothy T. Bergsma and Michael S. Smith Abstract Sumo is a web application intended as a template for developers. It is distributed as a Java ‘war’ file that deploys automatically when placed in a Servlet container’s ‘webapps’
directory. If a user supplies proper credentials, Sumo creates a session-specific Secure Shell connection to the host and a user-specific R session over that connection. Developers may write dynamic server pages that make use of the persistent R session and user-specific file space.

and for Apple fanboys-

We created the xgrid package (Horton and Anoke, 2012) to provide a simple interface to this distributed computing system. The package facilitates use of an Apple Xgrid for distributed processing of a simulation with many independent repetitions, by simplifying job submission (or grid stuffing) and collation of results. It provides a relatively thin but useful layer between R and Apple’s ‘xgrid’ shell command, where the user constructs input scripts to be run remotely. A similar set of routines, optimized for parallel estimation of JAGS (just another Gibbs sampler) models is available within the runjags package (Denwood, 2010). However, with the exception of runjags, none of the previously mentioned packages support parallel computation over an Apple Xgrid.

Hmm I guess parallel computing enabled by Wifi on mobile phones would be awesome too ! So would be anything using iOS . See the rest of the R Journal at http://journal.r-project.org/current.html

RJournal_2012-1

Interview James G Kobielus IBM Big Data

Here is an interview with James G Kobielus, who is the Senior Program Director, Product Marketing, Big Data Analytics Solutions at IBM. Special thanks to Payal Patel Cudia of IBM’s communication team,for helping with the logistics for this.

Ajay -What are the specific parts of the IBM Platform that deal with the three layers of Big Data -variety, velocity and volume

James-Well first of all, let’s talk about the IBM Information Management portfolio. Our big data platform addresses the three layers of big data to varying degrees either together in a product , or two out of the three or even one of the three aspects. We don’t have separate products for the variety, velocity and volume separately.

Let us define these three layers-Volume refers to the hundreds of terabytes and petabytes of stored data inside organizations today. Velocity refers to the whole continuum from batch to real time continuous and streaming data.

Variety refers to multi-structure data from structured to unstructured files, managed and stored in a common platform analyzed through common tooling.

For Volume-IBM has a highly scalable Big Data platform. This includes Netezza and Infosphere groups of products, and Watson-like technologies that can support petabytes volume of data for analytics. But really the support of volume ranges across IBM’s Information Management portfolio both on the database side and the advanced analytics side.

For real time Velocity, we have real time data acquisition. We have a product called IBM Infosphere, part of our Big Data platform, that is specifically built for streaming real time data acquisition and delivery through complex event processing. We have a very rich range of offerings that help clients build a Hadoop environment that can scale.

Our Hadoop platform is the most real time capable of all in the industry. We are differentiated by our sheer breadth, sophistication and functional depth and tooling integrated in our Hadoop platform. We are differentiated by our streaming offering integrated into the Hadoop platform. We also offer a great range of modeling and analysis tools, pretty much more than any other offering in the Big Data space.

Attached- Jim’s slides from Hadoop World

Ibm big data hadoop summit 2012 james kobielus final 6-13-12(1)

Ajay- Any plans for Mahout for Hadoop

Jim- I cant speak about product plans. We have plans but I cant tell you anything more. We do have a feature in Big Insights called System ML, a library for machine learning.

Ajay- How integral are acquisitions for IBM in the Big Data space (Netezza,Cognos,SPSS etc). Is it true that everything that you have in Big Data is acquired or is the famous IBM R and D contributing here . (see a partial list of IBM acquisitions at at http://www.ibm.com/investor/strategy/acquisitions.wss )

Jim- We have developed a lot on our own. We have the deepest R and D of anybody in the industry in all things Big Data.

For example – Watson has Big Insights Hadoop at its core. Apache Hadoop is the heart and soul of Big Data (see http://www-01.ibm.com/software/data/infosphere/hadoop/ ). A great deal that makes Big Insights so differentiated is that not everything that has been built has been built by the Hadoop community.

We have built additions out of the necessity for security, modeling, monitoring, and governance capabilities into BigInsights to make it truly enterprise ready. That is one example of where we have leveraged open source and we have built our own tools and technologies and layered them on top of the open source code.

Yes of course we have done many strategic acquisitions over the last several years related to Big Data Management and we continue to do so. This quarter we have done 3 acquisitions with strong relevance to Big Data. One of them is Vivisimo (http://www-03.ibm.com/press/us/en/pressrelease/37491.wss ).

Vivisimo provides federated Big Data discovery, search and profiling capabilities to help you figure out what data is out there,what is relevance of that data to your data science project- to help you answer the question which data should you bring in your Hadoop Cluster.

We also did Varicent , which is more performance management and we did TeaLeaf , which is a customer experience solution provider where customer experience management and optimization is one of the hot killer apps for Hadoop in the cloud. We have done great many acquisitions that have a clear relevance to Big Data.

Netezza already had a massively parallel analytics database product with an embedded library of models called Netezza Analytics, and in-database capabilties to massively parallelize Map Reduce and other analytics management functions inside the database. In many ways, Netezza provided capabilities similar to that IBM had provided for many years under the Smart Analytics Platform (http://www-01.ibm.com/software/data/infosphere/what-is-advanced-analytics/ ) .

There is a differential between Netezza and ISAS.

ISAS was built predominantly in-house over several years . If you go back a decade ago IBM acquired Ascential Software , a product portfolio that was the heart and soul of IBM InfoSphere Information Manager that is core to our big Data platform. In addition to Netezza, IBM bought SPSS two years back. We already had data mining tools and predictive modeling in the InfoSphere portfolio, but we realized we needed to have the best of breed, SPSS provided that and so IBM acquired them.

Cognos– We had some BI reporting capabilities in the InfoSphere portfolio that we had built ourselves and also acquired for various degrees from prior acquisitions. But clearly Cognos was one of the best BI vendors , and we were lacking such a rich tool set in our product in visualization and cubing and so for that reason we acquired Cognos.

There is also Unica – which is a marketing campaign optimization which in many ways is a killer app for Hadoop. Projects like that are driving many enterprises.

Ajay- How would you rank order these acquisitions in terms of strategic importance rather than data of acquisition or price paid.

Jim-Think of Big Data as an ecosystem that has components that are fitted to particular functions for data analytics and data management. Is the database the core, or the modeling tool the core, or the governance tools the core, or is the hardware platform the core. Everything is critically important. We would love to hear from you what you think have been most important. Each acquisition has helped play a critical role to build the deepest and broadest solution offering in Big Data. We offer the hardware, software, professional services, the hosting service. I don’t think there is any validity to a rank order system.

Ajay-What are the initiatives regarding open source that Big Data group have done or are planning?

Jim- What we are doing now- We are very much involved with the Apache Hadoop community. We continue to evolve the open source code that everyone leverages.. We have built BigInsights on Apache Hadoop. We have the closest, most up to date in terms of version number to Apache Hadoop ( Hbase,HDFS, Pig etc) of all commercial distributions with our BigInsights 1.4 .

We have an R library integrated with BigInsights . We have a R library integrated with Netezza Analytics. There is support for R Models within the SPSS portfolio. We already have a fair amount of support for R across the portfolio.

Ajay- What are some of the concerns (privacy,security,regulation) that you think can dampen the promise of Big Data.

Jim- There are no showstoppers, there is really a strong momentum. Some of the concerns within the Hadoop space are immaturity of the technology, the immaturity of some of the commercial offerings out there that implement Hadoop, the lack of standardization for formal sense for Hadoop.

There is no Open Standards Body that declares, ratifies the latest version of Mahout, Map Reduce, HDFS etc. There is no industry consensus reference framework for layering these different sub projects. There are no open APIs. There are no certifications or interoperability standards or organizations to certify different vendors interoperability around a common API or framework.

The lack of standardization is troubling in this whole market. That creates risks for users because users are adopting multiple Hadoop products. There are lots of Hadoop deployments in the corporate world built around Apache Hadoop (purely open source). There may be no assurance that these multiple platforms will interoperate seamlessly. That’s a huge issue in terms of just magnifying the risk. And it increases the need for the end user to develop their own custom integrated code if they want to move data between platforms, or move map-reduce jobs between multiple distributions.

Also governance is a consideration. Right now Hadoop is used for high volume ETL on multi structured and unstructured data sources, or Hadoop is used for exploratory sand boxes for data scientists. These are important applications that are a majority of the Hadoop deployments . Some Hadoop deployments are stand alone unstructured data marts for specific applications like sentiment analysis like.

Hadoop is not yet ready for data warehousing. We don’t see a lot of Hadoop being used as an alternative to data warehouses for managing the single version of truth of system or record data. That day will come but there needs to be out there in the marketplace a broader range of data governance mechanisms , master data management, data profiling products that are mature that enterprises can use to make sure their data inside their Hadoop clusters is clean and is the single version of truth. That day has not arrived yet.

One of the great things about IBM’s acquisition of Vivisimo is that a piece of that overall governance picture is discovery and profiling for unstructured data , and that is done very well by Vivisimo for several years.

What we will see is vendors such as IBM will continue to evolve security features inside of our Hadoop platform. We will beef up our data governance capabilities for this new world of Hadoop as the core of Big Data, and we will continue to build up our ability to integrate multiple databases in our Hadoop platform so that customers can use data from a bit of Hadoop,some data from a bit of traditional relational data warehouse, maybe some noSQL technology for different roles within a very complex Big Data environment.

That latter hybrid deployment model is becoming standard across many enterprises for Big Data. A cause for concern is when your Big Data deployment has a bit of Hadoop, bit of noSQL, bit of EDW, bit of in-memory , there are no open standards or frameworks for putting it all together for a unified framework not just for interoperability but also for deployment.

There needs to be a virtualization or abstraction layer for unified access to all these different Big Data platforms by the users/developers writing the queries, by administrators so they can manage data and resources and jobs across all these disparate platforms in a seamless unified way with visual tooling. That grand scenario, the virtualization layer is not there yet in any standard way across the big data market. It will evolve, it may take 5-10 years to evolve but it will evolve.

So, that’s the concern that can dampen some of the enthusiasm for Big Data Analytics.

About-

You can read more about Jim at http://www.linkedin.com/pub/james-kobielus/6/ab2/8b0 or

follow him on Twitter at http://twitter.com/jameskobielus

You can read more about IBM Big Data at http://www-01.ibm.com/software/data/bigdata/

Interview Alain Chesnais Chief Scientist Trendspottr.com

Here is a brief interview with Alain Chesnais ,Chief Scientist Trendspottr.com. It is a big honor to interview such a legend in computer science, and I am grateful to both him and Mark Zohar for taking time to write these down.

Ajay- Describe your career from your student days to being the President of ACM (Association of Computing Machinery http://www.acm.org/ ). How can we increase the interest of students in STEM education, particularly in view of the shortage of data scientists.

Alain- I’m trying to sum up a career of over 35 years. This may be a bit long winded…

I started my career in CS when I was in high school in the early 70’s. I was accepted in the National Science Foundation’s Science Honors Program in 9th grade and the first course I took was a Fortran programming course at Columbia University. This was on an IBM 360 using punch cards.

The next year my high school got a donation from DEC of a PDP-8E mini computer. I ended up spending a lot of time in the machine room all through high school at a time when access to computers wasn’t all that common. I went to college in Paris and ended up at l’Ecole Normale Supérieure de Cachan in the newly created Computer Science department.

My first job after finishing my graduate studies was as a research assistant at the Centre National de la Recherche Scientifique where I focused my efforts on modelling the behaviour of distributed database systems in the presence of locking. When François Mitterand was elected president of France in 1981, he invited Nicholas Negroponte and Seymour Papert to come to France to set up the Centre Mondial Informatique. I was hired as a researcher there and continued on to become director of software development until it was closed down in 1986. I then started up my own company focusing on distributed computer graphics. We sold the company to Abvent in the early 90’s.

After that, I was hired by Thomson Digital Image to lead their rendering team. We were acquired by Wavefront Technologies in 1993 then by SGI in 1995 and merged with Alias Research. In the merged company: Alias|wavefront, I was director of engineering on the Maya project. Our team received an Oscar in 2003 for the creation of the Maya software system.

Since then I’ve worked at various companies, most recently focusing on social media and Big Data issues associated with it. Mark Zohar and I worked together at SceneCaster in 2007 where we developed a Facebook app that allowed users to create their own 3D scenes and share them with friends via Facebook without requiring a proprietary plugin. In December 2007 it was the most popular app in its category on Facebook.

Recently Mark approached me with a concept related to mining the content of public tweets to determine what was trending in real time. Using math similar to what I had developed during my graduate studies to model the performance of distributed databases in the presence of locking, we built up a real time analytics engine that ranks the content of tweets as they stream in. The math is designed to scale linearly in complexity with the volume of data that we analyze. That is the basis for what we have created for TrendSpottr.

In parallel to my professional career, I have been a very active volunteer at ACM. I started out as a member of the Paris ACM SIGGRAPH chapter in 1985 and volunteered to help do our mailings (snail mail at the time). After taking on more responsibilities with the chapter, I was elected chair of the chapter in 1991. I was first appointed to the SIGGRAPH Local Groups Steering Committee, then became ACM Director for Chapters. Later I was successively elected SIGGRAPH Vice Chair, ACM SIG Governing Board (SGB) Vice Chair for Operations, SGB Chair, ACM SIGGRAPH President, ACM Secretary/Treasurer, ACM Vice President, and finally, in 2010, I was elected ACM President. My term as ACM President has just ended on July 1st. Vint Cerf is our new President. I continue to serve on the ACM Executive Committee in my role as immediate Past President.

(Note- About ACM
ACM, the Association for Computing Machinery www.acm.org, is the world’s largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field’s challenges. )

Ajay- What sets Trendspotter apart from other startups out there in terms of vision in trying to achieve a more coherent experience on the web.

Alain- The Basic difference with other approaches that we are aware of is that we have developed an incremental solution that calculates the results on the fly as the data streams in. Our evaluators are based on solid mathematical foundations that have proven their usefulness over time. One way to describe what we do is to think of it as signal processing where the tweets are the signal and our evaluators are like triggers that tell you what elements of the signal have the characteristics that we are filtering for (velocity and acceleration). One key result of using this approach is that our unit cost per tweet analyzed does not go up with increased volume. Using more traditional data analysis approaches involving an implicit sort would imply a complexity of N*log(N), where N is the volume of tweets being analyzed. That would imply that the cost per tweet analyzed would go up with the volume of tweets. Our approach was designed to avoid that, so that we can maintain a cap on our unit costs of analysis, no matter what volume of data we analyze.

Ajay- What do you think is the future of big data visualization going to look like? What are some of the technologies that you are currently bullish on?

Alain- I see several trends that would have deep impact on Big Data visualization. I firmly believe that with large amounts of data, visualization is key tool for understanding both the structure and the relationships that exist between data elements. Let’s focus on some of the key things that are pushing in this direction:

the volume of data that is available is growing at a rate we have never seen before. Cisco has measured an 8 fold increase in the volume of IP traffic over the last 5 years and predicts that we will reach the zettabyte of data over IP in 2016
more of the data is becoming publicly available. This isn’t only on social networks such as Facebook and twitter, but joins a more general trend involving open research initiatives and open government programs
the desired time to get meaningful results is going down dramatically. In the past 5 years we have seen the half life of data on Facebook, defined as the amount of time that half of the public reactions to any given post (likes, shares., comments) take place, go from about 12 hours to under 3 hours currently
our access to the net is always on via mobile device. You are always connected.
the CPU and GPU capabilities of mobile devices is huge (an iPhone has 10 times the compute power of a Cray-1 and more graphics capabilities than early SGI workstations)

Put all of these observations together and you quickly come up with a massive opportunity to analyze data visually on the go as it happens no matter where you are. We can’t afford to have to wait for results. When something of interest occurs we need to be aware of it immediately.

Ajay- What are some of the applications we could use Trendspottr. Could we predict events like Arab Spring, or even the next viral thing.

Alain- TrendSpottr won’t predict what will happen next. What it *will* do is alert you immediately when it happens. You can think of it like a smoke detector. It doesn’t tell that a fire will take place, but it will save your life when a fire does break out.

Typical uses for TrendSpottr are

thought leadership by tracking content that your readership is interested in via TrendSpottr you can be seen as a thought leader on the subject by being one of the first to share trending content on a given subject. I personally do this on my Facebook page (http://www.facebook.com/alain.chesnais) and have seen my klout score go up dramatically as a result
brand marketing to be able to know when something is trending about your brand and take advantage of it as it happens.
competitive analysis to see what is being said about two competing elements. For instance, searching TrendSpottr for “Obama OR Romney” gives you a very good understanding about how social networks are reacting to each politician. You can also do searches like “$aapl OR $msft OR $goog” to get a sense of what is the current buzz for certain hi tech stocks.
understanding your impact in real time to be able to see which of the content that you are posting is trending the most on social media so that you can highlight it on your main page. So if all of your content is hosted on common domain name (ourbrand.com), searching for ourbrand.com will show you the most active of your site’s content. That can easily be set up by putting a TrendSpottr widget on your front page

Ajay- What are some of the privacy guidelines that you keep in mind- given the fact that you collect individual information but also have government agencies as potential users.

Alain- We take privacy very seriously and anonymize all of the data that we collect. We don’t keep explicit records of the data we collected through the various incoming streams and only store the aggregate results of our analysis.

About–

Alain Chesnais is immediate Past President of ACM, elected for the two-year term beginning July 1, 2010.Chesnais studied at l’Ecole Normale Supérieure de l’Enseignement Technique and l’Université de Paris where he earned a Maîtrise de Mathematiques, a Maitrise de Structure Mathématique de l’Informatique, and a Diplôme d’Etudes Approfondies in Compuer Science. He was a high school student at the United Nations International School in New York, where, along with preparing his International Baccalaureate with a focus on Math, Physics and Chemistry, he also studied Mandarin Chinese.Chesnais recently founded Visual Transitions, which specializes in helping companies move to HTML 5, the newest standard for structuring and presenting content on the World Wide Web. He was the CTO of SceneCaster.com from June 2007 until April 2010, and was Vice President of Product Development at Tucows Inc. from July 2005 – May 2007. He also served as director of engineering at Alias|Wavefront on the team that received an Oscar from the Academy of Motion Picture Arts and Sciences for developing the Maya 3D software package.

Prior to his election as ACM president, Chesnais was vice president from July 2008 – June 2010 as well as secretary/treasurer from July 2006 – June 2008. He also served as president of ACM SIGGRAPH from July 2002 – June 2005 and as SIG Governing Board Chair from July 2000 – June 2002.

As a French citizen now residing in Canada, he has more than 20 years of management experience in the software industry. He joined the local SIGGRAPH Chapter in Paris some 20 years ago as a volunteer and has continued his involvement with ACM in a variety of leadership capacities since then.

About Trendspottr.com

http://trendspottr.com/aboutus.php

TrendSpottr is a real-time viral search and predictive analytics service that identifies the most timely and trending information for any topic or keyword. Our core technology analyzes real-time data streams and spots emerging trends at their earliest acceleration point — hours or days before they have become “popular” and reached mainstream awareness.

TrendSpottr serves as a predictive early warning system for news and media organizations, brands, government agencies and Fortune 500 companies and helps them to identify emerging news, events and issues that have high viral potential and market impact. TrendSpottr has partnered with HootSuite, DataSift and other leading social and big data companies.

Revolution R Enterprise 6.0 launched!

Just got the email-more software is good news!

Revolution R Enterprise 6.0 for 32-bit and 64-bit Windows and 64-bit Red Hat Enterprise Linux (RHEL 5.x and RHEL 6.x) features an updated release of the RevoScaleR package that provides fast, scalable data management and data analysis: the same code scales from data frames to local, high-performance .xdf files to data distributed across a Windows HPC Server cluster or IBM Platform Computing LSF cluster. RevoScaleR also allows distribution of the execution of essentially any R function across cores and nodes, delivering the results back to the user.

Detailed information on what’s new in 6.0 and known issues:
http://www.revolutionanalytics.com/doc/README_RevoEnt_Windows_6.0.0.pdf

and from the manual-lots of function goodies for Big Data

IBM Platform LSF Cluster support [Linux only]. The new RevoScaleR function, RxLsfCluster, allows you to create a distributed compute context for the Platform LSF workload manager.
Azure Burst support added for Microsoft HPC Server [Windows only]. The new RevoScaleR function, RxAzureBurst, allows you to create a distributed compute context to have computations performed in the cloud using Azure Burst
The rxExec function allows distributed execution of essentially any R function across cores and nodes, delivering the results back to the user.
functions RxLocalParallel and RxLocalSeq allow you to create compute context objects for local parallel and local sequential computation, respectively.
RxForeachDoPar allows you to create a compute context using the currently registered foreach parallel backend (doParallel, doSNOW, doMC, etc.). To execute rxExec calls, simply register the parallel backend as usual, then set your compute context as follows: rxSetComputeContext(RxForeachDoPar())
rxSetComputeContext and rxGetComputeContext simplify management of compute contexts.
rxGlm, provides a fast, scalable, distributable implementation of generalized linear models. This expands the list of full-featured high performance analytics functions already available: summary statistics (rxSummary), cubes and cross tabs (rxCube,rxCrossTabs), linear models (rxLinMod), covariance and correlation matrices (rxCovCor),
binomial logistic regression (rxLogit), and k-means clustering (rxKmeans)example: a Tweedie family with 1 million observations and 78 estimated coefficients (categorical data)
took 17 seconds with rxGlm compared with 377 seconds for glm on a quadcore laptop

and easier working with R’s big brother SAS language

RevoScaleR high-performance analysis functions will now conveniently work directly with a variety of external data sources (delimited and fixed format text files, SAS files, SPSS files, and ODBC data connections). New functions are provided to create data source objects to represent these data sources (RxTextData, RxOdbcData, RxSasData, and RxSpssData), which in turn can be specified for the ‘data’ argument for these RevoScaleR analysis functions: rxHistogram, rxSummary, rxCube, rxCrossTabs, rxLinMod, rxCovCor, rxLogit, and rxGlm.

example,

you can analyze a SAS file directly as follows:

# Create a SAS data source with information about variables and # rows to read in each chunk
sasDataFile <- file.path(rxGetOption(“sampleDataDir”),”claims.sas7bdat”)
sasDS <- RxSasData(sasDataFile, stringsAsFactors = TRUE,colClasses = c(RowNum = “integer”),rowsPerRead = 50)

# Compute and draw a histogram directly from the SAS file
rxHistogram( ~cost|type, data = sasDS)
# Compute summary statistics
rxSummary(~., data = sasDS)
# Estimate a linear model
linModObj <- rxLinMod(cost~age + car_age + type, data = sasDS)
summary(linModObj)
# Import a subset into a data frame for further inspection
subData <- rxImport(inData = sasDS, rowSelection = cost > 400,
varsToKeep = c(“cost”, “age”, “type”))
subData

The installation instructions and instructions for getting started with Revolution R Enterprise & RevoDeployR for Windows: http://www.revolutionanalytics.com/downloads/instructions/windows.php

Easter Eggs in #Rstats

Yes.

Cite-http://en.wikipedia.org/wiki/Easter_egg_(media)

A virtual Easter egg is an intentional hidden message, in-joke, or feature in a work such as a computer program, web page, video game, movie, book, or crossword. The term was coined — according to Warren Robinett — by Atari after they were pointed to the secret message left by Robinett in the game Adventure.^[1] It draws a parallel with the custom of the Easter egg hunt observed in many Western nations as well as the last Russian imperial family’s tradition of giving elaborately jeweled egg-shaped creations by Carl Fabergé which contained hidden surprises

In R.

Cite-http://stackoverflow.com/questions/7910270/are-there-any-easter-eggs-in-base-r-or-in-major-packages

I like this

just type

example(readLine)

and these two

on 32 bit R type

memory.limit(4096)

and on any version try four question marks

Perhaps the prettiest eggs are the demos in animation package.

But there is magic in asking for help on internal functions in R

Just type-

?.Internal

and you get the sobering thought that you probably are a R Muggle

Call an Internal Function

Description

.Internal performs a call to an internal code which is built in to the R interpreter.

Only true R wizards should even consider using this function, and only R developers can add to the list of internal functions.

Usage

 .Internal(call)

Arguments

call a call expression

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

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.”

Tag: parallel

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