The Top Statisticians in the World

 

 

 

 

 

 

http://en.wikipedia.org/wiki/John_Tukey

 

John Tukey

From Wikipedia, the free encyclopedia
John Tukey

John Wilder Tukey
Born June 16, 1915
New Bedford, Massachusetts, USA
Died July 26, 2000 (aged 85)
New Brunswick, New Jersey
Residence United States
Nationality American
Fields Mathematician
Institutions Bell Labs
Princeton University
Alma mater Brown University
Princeton University
Doctoral advisor Solomon Lefschetz
Doctoral students Frederick Mosteller
Kai Lai Chung
Known for FFT algorithm
Box plot
Coining the term ‘bit’
Notable awards Samuel S. Wilks Award (1965)
National Medal of Science (USA) in Mathematical, Statistical, and Computational Sciences (1973)
Shewhart Medal (1976)
IEEE Medal of Honor (1982)
Deming Medal (1982)
James Madison Medal (1984)
Foreign Member of the Royal Society(1991)

John Wilder Tukey ForMemRS[1] (June 16, 1915 – July 26, 2000) was an American statistician.

Contents

[hide]

[edit]Biography

Tukey was born in New Bedford, Massachusetts in 1915, and obtained a B.A. in 1936 and M.Sc.in 1937, in chemistry, from Brown University, before moving to Princeton University where he received a Ph.D. in mathematics.[2]

During World War II, Tukey worked at the Fire Control Research Office and collaborated withSamuel Wilks and William Cochran. After the war, he returned to Princeton, dividing his time between the university and AT&T Bell Laboratories.

Among many contributions to civil society, Tukey served on a committee of the American Statistical Association that produced a report challenging the conclusions of the Kinsey Report,Statistical Problems of the Kinsey Report on Sexual Behavior in the Human Male.

He was awarded the IEEE Medal of Honor in 1982 “For his contributions to the spectral analysis of random processes and the fast Fourier transform (FFT) algorithm.”

Tukey retired in 1985. He died in New Brunswick, New Jersey on July 26, 2000.

[edit]Scientific contributions

His statistical interests were many and varied. He is particularly remembered for his development with James Cooley of the Cooley–Tukey FFT algorithm. In 1970, he contributed significantly to what is today known as the jackknife estimation—also termed Quenouille-Tukey jackknife. He introduced the box plot in his 1977 book,”Exploratory Data Analysis“.

Tukey’s range test, the Tukey lambda distributionTukey’s test of additivity and Tukey’s lemma all bear his name. He is also the creator of several little-known methods such as the trimean andmedian-median line, an easier alternative to linear regression.

In 1974, he developed, with Jerome H. Friedman, the concept of the projection pursuit.[3]

http://en.wikipedia.org/wiki/Ronald_Fisher

Sir Ronald Aylmer Fisher FRS (17 February 1890 – 29 July 1962) was an English statistician,evolutionary biologisteugenicist and geneticist. Among other things, Fisher is well known for his contributions to statistics by creating Fisher’s exact test and Fisher’s equationAnders Hald called him “a genius who almost single-handedly created the foundations for modern statistical science”[1] while Richard Dawkins named him “the greatest biologist since Darwin“.[2]

 

contacts.xls

http://en.wikipedia.org/wiki/William_Sealy_Gosset

William Sealy Gosset (June 13, 1876–October 16, 1937) is famous as a statistician, best known by his pen name Student and for his work on Student’s t-distribution.

Born in CanterburyEngland to Agnes Sealy Vidal and Colonel Frederic Gosset, Gosset attendedWinchester College before reading chemistry and mathematics at New College, Oxford. On graduating in 1899, he joined the Dublin brewery of Arthur Guinness & Son.

Guinness was a progressive agro-chemical business and Gosset would apply his statistical knowledge both in the brewery and on the farm—to the selection of the best yielding varieties ofbarley. Gosset acquired that knowledge by study, trial and error and by spending two terms in 1906–7 in the biometric laboratory of Karl Pearson. Gosset and Pearson had a good relationship and Pearson helped Gosset with the mathematics of his papers. Pearson helped with the 1908 papers but he had little appreciation of their importance. The papers addressed the brewer’s concern with small samples, while the biometrician typically had hundreds of observations and saw no urgency in developing small-sample methods.

Another researcher at Guinness had previously published a paper containing trade secrets of the Guinness brewery. To prevent further disclosure of confidential information, Guinness prohibited its employees from publishing any papers regardless of the contained information. However, after pleading with the brewery and explaining that his mathematical and philosophical conclusions were of no possible practical use to competing brewers, he was allowed to publish them, but under a pseudonym (“Student”), to avoid difficulties with the rest of the staff.[1] Thus his most famous achievement is now referred to as Student’s t-distribution, which might otherwise have been Gosset’s t-distribution.

Where to complain for internet crime?

So did you get tricked or hacked, or phished or someone broke into your system.

What to do?

//

From

https://mail.google.com/support/bin/answer.py?hl=en&answer=190735

Impersonation

If you believe someone has created a Gmail address in an attempt to impersonate your identity, you may wish to file a report with the Internet Crime Complaint Center (www.ic3.gov), a partnership between the Federal Bureau of Investigation and the National White Collar Crime Center.

In addition, we recommend contacting your state’s Office of Consumer Protection.

Gmail is unable to participate in mediations involving third parties regarding impersonation. To read the Gmail Terms of Use, please visit: http://gmail.google.com/gmail/help/terms_of_use.html.

 

AND

 

http://www.ic3.gov/default.aspx

Welcome to IC3

The Internet Crime Complaint Center (IC3) is a partnership between theFederal Bureau of Investigation (FBI), the National White Collar Crime Center (NW3C), and the Bureau of Justice Assistance (BJA).

IC3’s mission is to serve as a vehicle to receive, develop, and refer criminal complaints regarding the rapidly expanding arena of cyber crime. The IC3 gives the victims of cyber crime a convenient and easy-to-use reporting mechanism that alerts authorities of suspected criminal or civil violations. For law enforcement and regulatory agencies at the federal, state, local and international level, IC3 provides a central referral mechanism for complaints involving Internet related crimes. read more >>

Filing a Complaint with IC3

IC3 accepts online Internet crime complaints from either the person who believes they were defrauded or from a third party to the complainant. We can best process your complaint if we receive accurate and complete information from you. Therefore, we request that you provide the following information when filing a complaint:

  • Your name
  • Your mailing address
  • Your telephone number
  • The name, address, telephone number, and Web address, if available, of the individual or organization you believe defrauded you.
  • Specific details on how, why, and when you believe you were defrauded.
  • Any other relevant information you believe is necessary to support your complaint.

File a Complaint Now>>

 

#Rstats for Business Intelligence

This is a short list of several known as well as lesser known R ( #rstats) language codes, packages and tricks to build a business intelligence application. It will be slightly Messy (and not Messi) but I hope to refine it someday when the cows come home.

It assumes that BI is basically-

a Database, a Document Database, a Report creation/Dashboard pulling software as well unique R packages for business intelligence.

What is business intelligence?

Seamless dissemination of data in the organization. In short let it flow- from raw transactional data to aggregate dashboards, to control and test experiments, to new and legacy data mining models- a business intelligence enabled organization allows information to flow easily AND capture insights and feedback for further action.

BI software has lately meant to be just reporting software- and Business Analytics has meant to be primarily predictive analytics. the terms are interchangeable in my opinion -as BI reports can also be called descriptive aggregated statistics or descriptive analytics, and predictive analytics is useless and incomplete unless you measure the effect in dashboards and summary reports.

Data Mining- is a bit more than predictive analytics- it includes pattern recognizability as well as black box machine learning algorithms. To further aggravate these divides, students mostly learn data mining in computer science, predictive analytics (if at all) in business departments and statistics, and no one teaches metrics , dashboards, reporting  in mainstream academia even though a large number of graduates will end up fiddling with spreadsheets or dashboards in real careers.

Using R with

1) Databases-

I created a short list of database connectivity with R here at https://rforanalytics.wordpress.com/odbc-databases-for-r/ but R has released 3 new versions since then.

The RODBC package remains the package of choice for connecting to SQL Databases.

http://cran.r-project.org/web/packages/RODBC/RODBC.pdf

Details on creating DSN and connecting to Databases are given at  https://rforanalytics.wordpress.com/odbc-databases-for-r/

For document databases like MongoDB and CouchDB

( what is the difference between traditional RDBMS and NoSQL if you ever need to explain it in a cocktail conversation http://dba.stackexchange.com/questions/5/what-are-the-differences-between-nosql-and-a-traditional-rdbms

Basically dispensing with the relational setup, with primary and foreign keys, and with the additional overhead involved in keeping transactional safety, often gives you extreme increases in performance

NoSQL is a kind of database that doesn’t have a fixed schema like a traditional RDBMS does. With the NoSQL databases the schema is defined by the developer at run time. They don’t write normal SQL statements against the database, but instead use an API to get the data that they need.

instead relating data in one table to another you store things as key value pairs and there is no database schema, it is handled instead in code.)

I believe any corporation with data driven decision making would need to both have atleast one RDBMS and one NoSQL for unstructured data-Ajay. This is a sweeping generic statement 😉 , and is an opinion on future technologies.

  • Use RMongo

From- http://tommy.chheng.com/2010/11/03/rmongo-accessing-mongodb-in-r/

http://plindenbaum.blogspot.com/2010/09/connecting-to-mongodb-database-from-r.html

Connecting to a MongoDB database from R using Java

http://nsaunders.wordpress.com/2010/09/24/connecting-to-a-mongodb-database-from-r-using-java/

Also see a nice basic analysis using R Mongo from

http://pseudofish.com/blog/2011/05/25/analysis-of-data-with-mongodb-and-r/

For CouchDB

please see https://github.com/wactbprot/R4CouchDB and

http://digitheadslabnotebook.blogspot.com/2010/10/couchdb-and-r.html

  • First install RCurl and RJSONIO. You’ll have to download the tar.gz’s if you’re on a Mac. For the second part, we’ll need to installR4CouchDB,

2) External Report Creating Software-

Jaspersoft- It has good integration with R and is a certified Revolution Analytics partner (who seem to be the only ones with a coherent #Rstats go to market strategy- which begs the question – why is the freest and finest stats software having only ONE vendor- if it was so great lots of companies would make exclusive products for it – (and some do -see https://rforanalytics.wordpress.com/r-business-solutions/ and https://rforanalytics.wordpress.com/using-r-from-other-software/)

From

http://www.jaspersoft.com/sites/default/files/downloads/events/Analytics%20-Jaspersoft-SEP2010.pdf

we see

http://jasperforge.org/projects/rrevodeployrbyrevolutionanalytics

RevoConnectR for JasperReports Server

RevoConnectR for JasperReports Server RevoConnectR for JasperReports Server is a Java library interface between JasperReports Server and Revolution R Enterprise’s RevoDeployR, a standardized collection of web services that integrates security, APIs, scripts and libraries for R into a single server. JasperReports Server dashboards can retrieve R charts and result sets from RevoDeployR.

http://jasperforge.org/plugins/esp_frs/optional_download.php?group_id=409

 

Using R and Pentaho
Extending Pentaho with R analytics”R” is a popular open source statistical and analytical language that academics and commercial organizations alike have used for years to get maximum insight out of information using advanced analytic techniques. In this twelve-minute video, David Reinke from Pentaho Certified Partner OpenBI provides an overview of R, as well as a demonstration of integration between R and Pentaho.
and from
R and BI – Integrating R with Open Source Business
Intelligence Platforms Pentaho and Jaspersoft
David Reinke, Steve Miller
Keywords: business intelligence
Increasingly, R is becoming the tool of choice for statistical analysis, optimization, machine learning and
visualization in the business world. This trend will only escalate as more R analysts transition to business
from academia. But whereas in academia R is often the central tool for analytics, in business R must coexist
with and enhance mainstream business intelligence (BI) technologies. A modern BI portfolio already includes
relational databeses, data integration (extract, transform, load – ETL), query and reporting, online analytical
processing (OLAP), dashboards, and advanced visualization. The opportunity to extend traditional BI with
R analytics revolves on the introduction of advanced statistical modeling and visualizations native to R. The
challenge is to seamlessly integrate R capabilities within the existing BI space. This presentation will explain
and demo an initial approach to integrating R with two comprehensive open source BI (OSBI) platforms –
Pentaho and Jaspersoft. Our efforts will be successful if we stimulate additional progress, transparency and
innovation by combining the R and BI worlds.
The demonstration will show how we integrated the OSBI platforms with R through use of RServe and
its Java API. The BI platforms provide an end user web application which include application security,
data provisioning and BI functionality. Our integration will demonstrate a process by which BI components
can be created that prompt the user for parameters, acquire data from a relational database and pass into
RServer, invoke R commands for processing, and display the resulting R generated statistics and/or graphs
within the BI platform. Discussion will include concepts related to creating a reusable java class library of
commonly used processes to speed additional development.

If you know Java- try http://ramanareddyg.blog.com/2010/07/03/integrating-r-and-pentaho-data-integration/

 

and I like this list by two venerable powerhouses of the BI Open Source Movement

http://www.openbi.com/demosarticles.html

Open Source BI as disruptive technology

http://www.openbi.biz/articles/osbi_disruption_openbi.pdf

Open Source Punditry

TITLE AUTHOR COMMENTS
Commercial Open Source BI Redux Dave Reinke & Steve Miller An review and update on the predictions made in our 2007 article focused on the current state of the commercial open source BI market. Also included is a brief analysis of potential options for commercial open source business models and our take on their applicability.
Open Source BI as Disruptive Technology Dave Reinke & Steve Miller Reprint of May 2007 DM Review article explaining how and why Commercial Open Source BI (COSBI) will disrupt the traditional proprietary market.

Spotlight on R

TITLE AUTHOR COMMENTS
R You Ready for Open Source Statistics? Steve Miller R has become the “lingua franca” for academic statistical analysis and modeling, and is now rapidly gaining exposure in the commercial world. Steve examines the R technology and community and its relevancy to mainstream BI.
R and BI (Part 1): Data Analysis with R Steve Miller An introduction to R and its myriad statistical graphing techniques.
R and BI (Part 2): A Statistical Look at Detail Data Steve Miller The usage of R’s graphical building blocks – dotplots, stripplots and xyplots – to create dashboards which require little ink yet tell a big story.
R and BI (Part 3): The Grooming of Box and Whiskers Steve Miller Boxplots and variants (e.g. Violin Plot) are explored as an essential graphical technique to summarize data distributions by categories and dimensions of other attributes.
R and BI (Part 4): Embellishing Graphs Steve Miller Lattices and logarithmic data transformations are used to illuminate data density and distribution and find patterns otherwise missed using classic charting techniques.
R and BI (Part 5): Predictive Modelling Steve Miller An introduction to basic predictive modelling terminology and techniques with graphical examples created using R.
R and BI (Part 6) :
Re-expressing Data
Steve Miller How do you deal with highly skewed data distributions? Standard charting techniques on this “deviant” data often fail to illuminate relationships. This article explains techniques to re-express skewed data so that it is more understandable.
The Stock Market, 2007 Steve Miller R-based dashboards are presented to demonstrate the return performance of various asset classes during 2007.
Bootstrapping for Portfolio Returns: The Practice of Statistical Analysis Steve Miller Steve uses the R open source stats package and Monte Carlo simulations to examine alternative investment portfolio returns…a good example of applied statistics using R.
Statistical Graphs for Portfolio Returns Steve Miller Steve uses the R open source stats package to analyze market returns by asset class with some very provocative embedded trellis charts.
Frank Harrell, Iowa State and useR!2007 Steve Miller In August, Steve attended the 2007 Internation R User conference (useR!2007). This article details his experiences, including his meeting with long-time R community expert, Frank Harrell.
An Open Source Statistical “Dashboard” for Investment Performance Steve Miller The newly launched Dashboard Insight web site is focused on the most useful of BI tools: dashboards. With this article discussing the use of R and trellis graphics, OpenBI brings the realm of open source to this forum.
Unsexy Graphics for Business Intelligence Steve Miller Utilizing Tufte’s philosophy of maximizing the data to ink ratio of graphics, Steve demonstrates the value in dot plot diagramming. The R open source statistical/analytics software is showcased.
I think that the report generation package Brew would also qualify as a BI package, but large scale implementation remains to be seen in
a commercial business environment
  • brew: Creating Repetitive Reports
 brew: Templating Framework for Report Generation

brew implements a templating framework for mixing text and R code for report generation. brew template syntax is similar to PHP, Ruby's erb module, Java Server Pages, and Python's psp module. http://bit.ly/jINmaI
  • Yarr- creating reports in R
to be continued ( when I have more time and the temperature goes down from 110F in Delhi, India)

Jaspersoft 4.1 launched

http://www.jaspersoft.com/events/jaspersoft-41-webinar-north-america

one more webinar, but a newer software and a changing paradigm.

———————————————————————————————————————————————

Webinar: Introducing Jaspersoft 4.1- North America

 Date: June 8, 2011

Time: 10:00 AM PT/1:00 PM ET
Duration: 60 Minutes
Language: English

Self-service Business Intelligence helps individuals and organizations respond quickly to operational and strategic decisions.  A driving factor for the success of self-service BI is an intuitive and integrated UI that spans reporting, dashboards, and data analysis. Additionally, the rise of the cloud and big-data environments presents new opportunities for organizations that can analyze and respond to these emerging data sources.

In this webinar, see how the new Jaspersoft Business Intelligence Suite 4.1 release achieves all this and more.
Register now and learn how you can now take advantage of:

  • Modern self-service BI for reporting and analysis: powerful, unified capabilities for both ad hoc reporting and analysis tasks.
  • Flexible insight into any data source: a seamless analytics UI for both data stored within relational, OLAP, and big data stores.
  • Maximum performance—now designed to take advantage of 64-bit processors.

    Join the webinar and explore the world’s most advanced and affordable BI suite through live demos, interactive Q&A and more.

the upcoming Jaspersoft 4.1 Webinar and Demo. Here’s a preview of what you’ll see:

  • Unified drag-and-drop UI for reporting and analytics. Jaspersoft 4.1 delivers powerful, unified drag-and-drop usability to both ad hoc reporting and analysis tasks while supporting both in-memory and OLAP engines. That means easier, more powerful analysis for business users and a faster route to self-service BI.
  • Insight against any data source. The analytics UI provides easier, more powerful analysis across relational, OLAP, and Big Data stores.
  • High performance with native 64-bit installer support. With Jaspersoft 4.1, you can also scale BI applications to new performance heights, thanks to full support for today’s powerful 64-bit processors.

Join us for the webinar and see how quickly and easily BI Builders like you can deliver true, self-service BI, combining reports, dashboards, and analytics, against virtually any data source — all through a single, web-based UI.

What is a White Paper?

Christine and Jimmy Wales
Image via Wikipedia

As per Jimmy Wales and his merry band at Wiki (pedia not leaky-ah)- The emphasis is mine

What is the best white paper you have read in the past 15 years.

Categories are-

  • Business benefits: Makes a business case for a certain technology or methodology.
  • Technical: Describes how a certain technology works.
  • Hybrid: Combines business benefits with technical details in a single document.
  • Policy: Makes a case for a certain political solution to a societal or economic challenge.
——————————————————————————————————————————————————



white paper is an authoritative report or guide that helps solve a problem. White papers are used to educate readers and help people make decisions, and are often requested and used in politics, policy, business, and technical fields. In commercial use, the term has also come to refer to documents used by businesses as a marketing or sales tool. Policy makers frequently request white papers from universities or academic personnel to inform policy developments with expert opinions or relevant research.

Government white papers

In the Commonwealth of Nations, “white paper” is an informal name for a parliamentary paper enunciating government policy; in the United Kingdom these are mostly issued as “Command papers“. White papers are issued by the government and lay out policy, or proposed action, on a topic of current concern. Although a white paper may on occasion be a consultation as to the details of new legislation, it does signify a clear intention on the part of a government to pass new law. White Papers are a “…. tool of participatory democracy … not [an] unalterable policy commitment.[1] “White Papers have tried to perform the dual role of presenting firm government policies while at the same time inviting opinions upon them.” [2]

In Canada, a white paper “is considered to be a policy document, approved by Cabinet, tabled in the House of Commons and made available to the general public.”[3] A Canadian author notes that the “provision of policy information through the use of white and green papers can help to create an awareness of policy issues among parliamentarians and the public and to encourage an exchange of information and analysis. They can also serve as educational techniques”.[4]

“White Papers are used as a means of presenting government policy preferences prior to the introduction of legislation”; as such, the “publication of a White Paper serves to test the climate of public opinion regarding a controversial policy issue and enables the government to gauge its probable impact”.[5]

By contrast, green papers, which are issued much more frequently, are more open ended. These green papers, also known as consultation documents, may merely propose a strategy to be implemented in the details of other legislation or they may set out proposals on which the government wishes to obtain public views and opinion.

White papers published by the European Commission are documents containing proposals for European Union action in a specific area. They sometimes follow a green paper released to launch a public consultation process.

For examples see the following:

 Commercial white papers

Since the early 1990s, the term white paper has also come to refer to documents used by businesses and so-called think tanks as marketing or sales tools. White papers of this sort argue that the benefits of a particular technologyproduct or policy are superior for solving a specific problem.

These types of white papers are almost always marketing communications documents designed to promote a specific company’s or group’s solutions or products. As a marketing tool, these papers will highlight information favorable to the company authorizing or sponsoring the paper. Such white papers are often used to generate sales leads, establish thought leadership, make a business case, or to educate customers or voters.

There are four main types of commercial white papers:

  • Business benefits: Makes a business case for a certain technology or methodology.
  • Technical: Describes how a certain technology works.
  • Hybrid: Combines business benefits with technical details in a single document.
  • Policy: Makes a case for a certain political solution to a societal or economic challenge.

Resources

  • Stelzner, Michael (2007). Writing White Papers: How to capture readers and keep them engaged. Poway, California: WhitePaperSource Publishing. pp. 214. ISBN 9780977716937.
  • Bly, Robert W. (2006). The White Paper Marketing Handbook. Florence, Kentucky: South-Western Educational Publishing. pp. 256. ISBN 9780324300826.
  • Kantor, Jonathan (2009). Crafting White Paper 2.0: Designing Information for Today’s Time and Attention Challenged Business Reader. Denver,Colorado: Lulu Publishing. pp. 167.ISBN 9780557163243.

Forecasting World Events Team

a large and diverse panel of forecasters, including substantial representation from government, academia, “think tanks,” and industry. Here are a few other details concerning your fellow participants:
  • At this time, over 600 people are being invited to participate. Please note that we expect that new participants will be joining the panel on a rolling basis for years to come.
  • Around 85% of these 600+ participants have at least a Bachelor’s degree, and over 60% of them have advanced degrees.
  • In terms of background training, participants represent a range of academic fields. Around 40% report a Social-Behavioral Science background, but there is also significant representation from those with backgrounds in Business (15%), the Humanities (13%), Engineering (12%), and the Natural Sciences (10%), among others.
  • The average participant age is 43 years-old, with a standard deviation of 15 years.
  • The panel’s gender composition is 75% men / 25% women, and this closely mirrors the gender ratio for all FWE registrants.
  • In addition to participation from individuals overseas, we are pleased to have eligible participants representing 44 of the 50 United States.
We are currently scheduled to begin the core forecasting study in late summer, a few months later than we initially anticipated. In the meantime, we will be readying our web-based forecasting environment and assembling our initial set of forecasting questions. As our formal launch date approaches, we will be contacting you with a link to the forecasting website and any other information you’ll need to get started. Between now and then we may reach out to you with other related announcements.
Finally, registration remains open, and we encourage you to “spread the word” by sharing our registration homepage link with your friends and colleagues.
Thanks once again for your interest in Forecasting World Events. We look forward to you joining us this summer.
Sincerely,
The Forecasting World Events Team
E-mail is not a secure form of communication.

The confidentiality of this message cannot be guaranteed.

ps- above message was from this new contest. Enter at your initiative. Buyer Beware!.

AsterData still alive;/launches SQL-MapReduce Developer Portal

so apparantly ole client AsterData continues to thrive under gentle touch of Terrific Data

———————————————————————————————————————————————————

Aster Data today launched the SQL-MapReduce Developer Portal, a new online community for data scientists and analytic developers. For your convenience, I copied the release below and it can also be found here. Please let me know if you have any questions or if there is anything else I can help you with.

Sara Korolevich

Point Communications Group for Aster Data

sarak@pointcgroup.com

Office: 602.279.1137

Mobile: 623.326.0881

Teradata Accelerates Big Data Analytics with First Collaborative Community for SQL-MapReduce®

New online community for data scientists and analytic developers enables development and sharing of powerful MapReduce analytics


San Carlos, California – Teradata Corporation (NYSE:TDC) today announced the launch of the Aster Data SQL-MapReduce® Developer Portal. This portal is the first collaborative online developer community for SQL-MapReduce analytics, an emerging framework for processing non-relational data and ultra-fast analytics.

“Aster Data continues to deliver on its unique vision for powerful analytics with a rich set of tools to make development of those analytics quick and easy,” said Tasso Argyros, vice president of Aster Data Marketing and Product Management, Teradata Corporation. “This new developer portal builds on Aster Data’s continuing SQL-MapReduce innovation, leveraging the flexibility and power of SQL-MapReduce for analytics that were previously impossible or impractical.”

The developer portal showcases the power and flexibility of Aster Data’s SQL-MapReduce – which uniquely combines standard SQL with the popular MapReduce distributed computing technology for processing big data – by providing a collaborative community for sharing SQL-MapReduce expert insights in addition to sharing SQL-MapReduce analytic functions and sample code. Data scientists, quantitative analysts, and developers can now leverage the experience, knowledge, and best practices of a community of experts to easily harness the power of SQL-MapReduce for big data analytics.

A recent report from IDC Research, “Taking Care of Your Quants: Focusing Data Warehousing Resources on Quantitative Analysts Matters,” has shown that by enabling data scientists with the tools to harness emerging types and sources of data, companies create significant competitive advantage and become leaders in their respective industry.

“The biggest positive differences among leaders and the rest come from the introduction of new types of data,” says Dan Vesset, program vice president, Business Analytics Solutions, IDC Research. “This may include either new transactional data sources or new external data feeds of transactional or multi-structured interactional data — the latter may include click stream or other data that is a by-product of social networking.”

Vesset goes on to say, “Aster Data provides a comprehensive platform for analytics and their SQL-MapReduce Developer Portal provides a community for sharing best practices and functions which can have an even greater impact to an organization’s business.”

With this announcement Aster Data extends its industry leadership in delivering the most comprehensive analytic platform for big data analytics — not only capable of processing massive volumes of multi-structured data, but also providing an extensive set of tools and capabilities that make it simple to leverage the power of MapReduce analytics. The Aster Data

SQL-MapReduce Developer Portal brings the power of SQL-MapReduce accessible to data scientists, quantitative analysis, and analytic developers by making it easy to share and collaborate with experts in developing SQL-MapReduce analytics. This portal builds on Aster Data’s history of SQL-MapReduce innovations, including:

  • The first deep integration of SQL with MapReduce
  • The first MapReduce support for .NET
  • The first integrated development environment, Aster Data
    Developer Express
  • A comprehensive suite of analytic functions, Aster Data
    Analytic Foundation

Aster Data’s patent-pending SQL-MapReduce enables analytic applications and functions that can deliver faster, deeper insights on terabytes to petabytes of data. These applications are implemented using MapReduce but delivered through standard SQL and business intelligence (BI) tools.

SQL-MapReduce makes it possible for data scientists and developers to empower business analysts with the ability to make informed decisions, incorporating vast amounts of data, regardless of query complexity or data type. Aster Data customers are using SQL-MapReduce for rich analytics including analytic applications for social network analysis, digital marketing optimization, and on-the-fly fraud detection and prevention.

“Collaboration is at the core of our success as one of the leading providers, and pioneers of social software,” said Navdeep Alam, director of Data Architecture at Mzinga. “We are pleased to be one of the early members of The Aster Data SQL-MapReduce Developer Portal, which will allow us the ability to share and leverage insights with others in using big data analytics to attain a deeper understanding of customers’ behavior and create competitive advantage for our business.”

SQL-MapReduce is one of the core capabilities within Aster Data’s flagship product. Aster DatanCluster™ 4.6, the industry’s first massively parallel processing (MPP) analytic platform has an integrated analytics engine that stores and processes both relational and non-relational data at scale. With Aster Data’s unique analytics framework that supports both SQL and
SQL-MapReduce™, customers benefit from rich, new analytics on large data volumes with complex data types. Aster Data analytic functions are embedded within the analytic platform and processed locally with data, which allows for faster data exploration. The SQL-MapReduce framework provides scalable fault-tolerance for new analytics, providing users with superior reliability, regardless of number of users, query size, or data types.


About Aster Data
Aster Data is a market leader in big data analytics, enabling the powerful combination of cost-effective storage and ultra-fast analysis of new sources and types of data. The Aster Data nCluster analytic platform is a massively parallel software solution that embeds MapReduce analytic processing with data stores for deeper insights on new data sources and types to deliver new analytic capabilities with breakthrough performance and scalability. Aster Data’s solution utilizes Aster Data’s patent-pending SQL-MapReduce to parallelize processing of data and applications and deliver rich analytic insights at scale. Companies including Barnes & Noble, Intuit, LinkedIn, Akamai, and MySpace use Aster Data to deliver applications such as digital marketing optimization, social network and relationship analysis, and fraud detection and prevention.


About Teradata
Teradata is the world’s leader in data warehousing and integrated marketing management through itsdatabase softwaredata warehouse appliances, and enterprise analytics. For more information, visitteradata.com.

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Teradata is a trademark or registered trademark of Teradata Corporation in the United States and other countries.