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 ApplianceOracle 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

WPS Version 3 Released

Apparently-you can now use the language of SAS on a Mac using the British software WPS

http://teamwpc.co.uk/press/wps3_released

WPS software ready for Big Data, Cloud Computing and Apple Mac

ONDON, UK – 2 February 2012 – World Programming today released version 3 of their leading WPS data processing and analytics software.

Big data processing at affordable prices is driving adoption of WPS in the datacentre and across the enterprise for analytics, business intelligence and prediction, data management, ETL and reporting.

WPS version 3 boosts support for the language of SAS, extending core data processing capabilities as well as analytics and graphing. Further improvements have been made to performance and scalability together with a wide range of supported platforms.

The popularity of Linux platforms continues to grow as organisations look for platform flexibility and control over costs. WPS Link technology with WPS version 3 offers the option to use the popular WPS Workbench user interface (GUI) to connect to and run programs in server, grid, cluster and cloud environments, suiting modern datacentre-driven compute facilities.

Version 3 also brings the WPS Workbench user interface to Mac OS X, Solaris, AIX and Linux platforms including Linux for System z. The WPS Workbench benefits from significant enhancements including: improved handling and display of automatically generated output; importing and exporting data; multiple concurrent program execution; code generation templates and more.

WPS statistical analysis capability continues to expand. Organisations are increasingly looking to use their data to provide the insight, prediction and intelligence to make decisions that will affect their future. WPS has the power to handle the big data volumes of the modern enterprise and to produce results that can be depended on.

WPS version 3 is available as a free upgrade to all WPS license holders.

Related Links

www.teamwpc.co.uk/support/release/wps : Summary of all the major new features in WPS version 3 plus additional downloadable documents (release notes, change log, issues).

www.teamwpc.co.uk/products/wps : Explore in more detail all the features of WPS software.

More Information About WPS

WPS is a competitively priced, high performance, highly scalable data processing and analytics software product that allows users to execute programs written in the language of SAS. WPS is supported on a wide variety of hardware and operating system platforms and can connect to and work with many types of data with ease. The WPS user interface (Workbench) is frequently praised for its ease of use and flexibility, with the option to include numerous third-party extensions.

Press Enquiries: press@teamwpc.co.uk

and

http://teamwpc.co.uk/products/wps

Overview

World Programming System (WPS)

What is WPS?

The World Programming System (WPS) is a powerful and versatile platform for working with data. WPS software can run programs written in the language of SAS.

The supported syntax covers core, statistical and graphing functionality, and makes it possible to run many applications written in the language of SAS whilst the breadth of language support in WPS continues to grow.

The WPS Workbench IDE/GUI allows you to create, edit, manage and execute scripts and view the resulting output. Scripts can also be executed from the command line or in batch mode using WPS CLI.

Integrated Modular System

More About Modules…

Multi Platform Availability

WPS is available on a wide variety of hardware and operating system platforms, including Microsoft Windows, Apple Mac OS X, Linux (including for System z), AIX, Solaris and IBM Mainframe z/OS.

More About Platforms…

 

User Interface

WPS can be used in a number of different ways:

Handle Large Data Volumes

WPS can read and write to many of the most commonly used data file formats, databases and data warehouses. It is capable of handling huge data volumes, wherever the processing occurs, be that on a mainframe, in a cloud, cluster, grid, server or workstation.

 

and

http://teamwpc.co.uk/support/release/wps

Summary of Main New Features in WPS Version 3

For a more generalised overview of the current features of WPS, not just the ‘new’ features summarised below, please refer to the Product section.

Here are the main new features of the latest release.

  • Multi-Platform Workbench
    The WPS Workbench (IDE/GUI) is now offered on the following platforms:

    - AIX
    - Linux (x86 and System z)
    - Mac OS X
    - Solaris (x86 and Sparc)
    - Windows

  • Workbench Feature Enhancements
    The WPS Workbench has received many usability enhancements including:

    - Dataset import/export wizard.
    - 3rd-party eclipse plugin support.
    - Rename/delete datasets.
    - Assign/deassign libraries (libnames).
    - Find values in dataset viewer.
    - Enhanced dataset viewer display.
    - Automatic management of ODS HTML and Listing output.
    - Regular expression support in ‘find’ features.
    - Improved character set/codepage support.
    - Multiple concurrent WPS servers (see below).
    - WPS Link remote server capability (see below).

  • Multiple Concurrent WPS Servers
    In previous releases of the WPS Workbench it was only possible to have one local server on which you could run your scripts. WPS version 3 allows you to set up multiple servers in the WPS Workbench and to pick which server to run any given script on. The WPS Workbench manages all the output, logs and datasets generated by each server for you.
    This enhancement, combined with the New WPS Link technology (see below) allows you to run your programs wherever you would like and control it all from the WPS Workbench.
  • Remote Server Connection
    New WPS Link technology allows the WPS Workbench to link to remote WPS Servers on other Mac, Linux or UNIX servers and to run scripts on those machines. It also allows you to view any resulting output locally in your WPS Workbench on your local machine. This enables you to make use of centralised storage and processing resources including grids and clusters of WPS processing servers and removes the requirement to process or store any data on the workstation.
  • Multi-Threading Summarisation
    Workstations and Servers with multiple CPU cores or hyper-threading can benefit from the new multi-threaded summarising engine in WPS version 3.
    This significantly improves the performance of many procedures within WPS that perform summarisation of data including PROC SUMMARY, PROC MEANS and other statistical procedures such as PROC TTEST.
  • Microsoft Windows® Installer
    WPS for Windows now allows in-place upgrade without requiring the removal of previous version of WPS beforehand.
  • Core Language Support
    WPS version 3 continues the expansion of it’s language support with even more new language items.
  • Statistical Analysis
    The support in WPS Statistics has been expanded to include:

    - PROC DISTANCE
    - PROC FACTOR
    - PROC GLM
    - PROC GLMMOD
    - PROC PRINCOMP
    - PROC STDIZE
    - PROC TTEST

    PROC LOGISTIC has been improved to allow the following model selection methods FORWARDS, BACKWARDS, STEPWISE and FAST.
    Numerous other statements and options have been added to the DATA STEP and other PROCS.

  • Financial Functions
    Support has been added for the following financial functions:

    - PMT
    - IPMT
    - PPMT
    - CUMIPMT
    - CUMPRINC
    - EFFRATE
    - NOMRATE

  • DATA Step Enhancements
    Support has been added for MODIFY and UPDATE statements within the DATA Step as well as support for NOMISS and UNIQUE constraints. Numerous other enhancements have also been added to the DATA Step like the addition of the COMPGED, CALL COMPCOST and UUIDGEN DATA Step functions.
  • Data Set Index Enhancements
    WPS support for data set indexes has been extended and optimised to offer faster index build and modification actions as well as faster index retrieval. Index creation speed has been dramatically improved. For example, on a 50 million row dataset WPS version 3 may create an index 10 times faster than WPS 2. The index files WPS version 3 produces are also significantly smaller than those produced by previous WPS versions, typically up to 50% smaller.
  • Improved WPD Library Engine
    WPS version 3 has a new, improved version of the World Programming Data Set (WPD) library engine. WPD files generated by version 3.x cannot be read by previous version 2.X releases of WPS.

    Version 2 files can be read and written to by WPS 3 using the new WPDV2 engine.
    The WPD engine in WPS 3 can read version 2 datasets without program modification, however by default the WPD engine will now write WPS 3 datasets. Please see Upgrading comments below*.

  • Sybase®
    A new WPS Engine for Sybase on Windows, Linux, Solaris and AIX platforms.
  • XML Data Support
    A new Libray engine for XML in the WPS Core module provides generic XML data import/export support and use of Oracle, CDISC and XMLMAP transformations.
  • PROC IMPORT/EXPORT support for Microsoft Access and Excel
    WPS version 3 now provides full support in PROCs IMPORT/EXPORT for Microsoft Access and Excel.

Jobs in Analytics

Some job openings from WPS/Analytic Searches.com

Please review WPS searches below and contact me if you would like to have a confidential conversation.

Full position descriptions at http://www.analyticsearches.com/wps-search-current-positions/ .

Submit resume/ questions to mary@analyticsearches.com.

All referrals are appreciated and earn you $1000.00 each when hired.
—————————————————————————————-
Associate Analytics & Research/Connecticut
Bank Business Analyst/IL/To $95K
Bank Manager/Model Dev & Risk Mgt/IL/To $140K
Banking Risk Analyst/IL/To $100K
Credit Card Risk Analyst/ Illinois
Director, Analytics/Chicago/To $130K
Director of Analytics/NJ/To $140K
Quantitative Manager Advanced Analytics/DC/To $150K
Quantitative Modeler/DC/To $130K
Research Statistician/IL/To $110K
Senior Manager Data & Analytics/NY, IL/To $110K
Statistician/San Diego
VP Actuary Pricing/Connecticut
VP Digital Strategist/Chicago/To $150K
VP Enterprise Catastrophe Strategy and Reporting/CT
VP Strategy & Analytics/Chicago/To $150K

Submit resume or questions to mary@analyticsearches.com.

http://www.analyticsearches.com

Jill Dyche on 2012

In part 3 of the series for predictions for 2012, here is Jill Dyche, Baseline Consulting/DataFlux.

Part 2 was Timo Elliot, SAP at http://www.decisionstats.com/timo-elliott-on-2012/ and Part 1 was Jim Kobielus, Forrester at http://www.decisionstats.com/jim-kobielus-on-2012/

Ajay: What are the top trends you saw happening in 2011?

 

Well, I hate to say I saw them coming, but I did. A lot of managers committed some pretty predictable mistakes in 2011. Here are a few we witnessed in 2011 live and up close:

 

1.       In the spirit of “size matters,” data warehouse teams continued to trumpet the volumes of stored data on their enterprise data warehouses. But a peek under the covers of these warehouses reveals that the data isn’t integrated. Essentially this means a variety of heterogeneous virtual data marts co-located on a single server. Neat. Big. Maybe even worthy of a magazine article about how many petabytes you’ve got. But it’s not efficient, and hardly the example of data standardization and re-use that everyone expects from analytical platforms these days.

 

2.       Development teams still didn’t factor data integration and provisioning into their project plans in 2011. So we saw multiple projects spawn duplicate efforts around data profiling, cleansing, and standardization, not to mention conflicting policies and business rules for the same information. Bummer, since IT managers should know better by now. The problem is that no one owns the problem. Which brings me to the next mistake…

 

3.       No one’s accountable for data governance. Yeah, there’s a council. And they meet. And they talk. Sometimes there’s lunch. And then nothing happens because no one’s really rewarded—or penalized for that matter—on data quality improvements or new policies. And so the reports spewing from the data mart are still fraught and no one trusts the resulting decisions.

 

But all is not lost since we’re seeing some encouraging signs already in 2012. And yes, I’d classify some of them as bona-fide trends.

 

Ajay: What are some of those trends?

 

Job descriptions for data stewards, data architects, Chief Data Officers, and other information-enabling roles are becoming crisper, and the KPIs for these roles are becoming more specific. Data management organizations are being divorced from specific lines of business and from IT, becoming specialty organizations—okay, COEs if you must—in their own rights. The value proposition for master data management now includes not just the reconciliation of heterogeneous data elements but the support of key business strategies. And C-level executives are holding the data people accountable for improving speed to market and driving down costs—not just delivering cleaner data. In short, data is becoming a business enabler. Which, I have to just say editorially, is better late than never!

 

Ajay: Anything surprise you, Jill?

 

I have to say that Obama mentioning data management in his State of the Union speech was an unexpected but pretty powerful endorsement of the importance of information in both the private and public sector.

 

I’m also sort of surprised that data governance isn’t being driven more frequently by the need for internal and external privacy policies. Our clients are constantly asking us about how to tightly-couple privacy policies into their applications and data sources. The need to protect PCI data and other highly-sensitive data elements has made executives twitchy. But they’re still not linking that need to data governance.

 

I should also mention that I’ve been impressed with the people who call me who’ve had their “aha!” moment and realize that data transcends analytic systems. It’s operational, it’s pervasive, and it’s dynamic. I figured this epiphany would happen in a few years once data quality tools became a commodity (they’re far from it). But it’s happening now. And that’s good for all types of businesses.

 

About-

Jill Dyché has written three books and numerous articles on the business value of information technology. She advises clients and executive teams on leveraging technology and information to enable strategic business initiatives. Last year her company Baseline Consulting was acquired by DataFlux Corporation, where she is currently Vice President of Thought Leadership. Find her blog posts on www.dataroundtable.com.

Timo Elliott on 2012

Continuing the DecisionStats series on  trends for 2012, Timo Elliott , Technology Evangelist  at SAP Business Objects, looks at the predictions he made in the beginning of  2011 and follows up with the things that surprised him in 2011, and what he foresees in 2012.

You can read last year’s predictions by Mr Elliott at http://www.decisionstats.com/brief-interview-timo-elliott/

Timo- Here are my comments on the “top three analytics trends” predictions I made last year:

(1) Analytics, reinvented. New DW techniques make it possible to do sub-second, interactive analytics directly against row-level operational data. Now BI processes and interfaces need to be rethought and redesigned to make best use of this — notably by blurring the distinctions between the “design” and “consumption” phases of BI.

I spent most of 2011 talking about this theme at various conferences: how existing BI technology israpidly becoming obsolete and how the changes are akin to the move from film to digital photography. Technology that has been around for many years (in-memory, column stores, datawarehouse appliances, etc.) came together to create exciting new opportunities and even generally-skeptical industry analysts put out press releases such as “Gartner Says Data Warehousing Reaching Its Most Significant Inflection Point Since Its Inception.” Some of the smaller BI vendors had been pushing in-memory analytics for years, but the general market started paying more attention when megavendors like SAP started painting a long-term vision of in-memory becoming a core platform for applications, not just analytics. Database leader Oracle was forced to upgrade their in-memory messaging from “It’s a complete fantasy” to “we have that too”.

(2) Corporate and personal BI come together. The ability to mix corporate and personal data for quick, pragmatic analysis is a common business need. The typical solution to the problem — extracting and combining the data into a local data store (either Excel or a departmental data mart) — pleases users, but introduces duplication and extra costs and makes a mockery of information governance. 2011 will see the rise of systems that let individuals and departments load their data into personal spaces in the corporate environment, allowing pragmatic analytic flexibility without compromising security and governance.

The number of departmental “data discovery” initiatives continued to rise through 2011, but new tools do make it easier for business people to upload and manipulate their own information while using the corporate standards. 2012 will see more development of “enterprise data discovery” interfaces for casual users.

(3) The next generation of business applications. Where are the business applications designed to support what people really do all day, such as implementing this year’s strategy, launching new products, or acquiring another company? 2011 will see the first prototypes of people-focused, flexible, information-centric, and collaborative applications, bringing together the best of business intelligence, “enterprise 2.0”, and existing operational applications.

2011 saw the rise of sophisticated, user-centric mobile applications that combine data from corporate systems with GPS mapping and the ability to “take action”, such as mobile medical analytics for doctors or mobile beauty advisor applications, and collaborative BI started becoming a standard part of enterprise platforms.

And one that should happen, but probably won’t: (4) Intelligence = Information + PEOPLE. Successful analytics isn’t about technology — it’s about people, process, and culture. The biggest trend in 2011 should be organizations spending the majority of their efforts on user adoption rather than technical implementation.

Unsurprisingly, there was still high demand for presentations on why BI projects fail and how to implement BI competency centers.  The new architectures probably resulted in even more emphasis on technology than ever, while business peoples’ expectations skyrocketed, fueled by advances in the consumer world. The result was probably even more dissatisfaction in the past, but the benefits of the new architectures should start becoming clearer during 2012.

What surprised me the most:

The rapid rise of Hadoop / NoSQL. The potentials of the technology have always been impressive, but I was surprised just how quickly these technology has been used to address real-life business problems (beyond the “big web” vendors where it originated), and how quickly it is becoming part of mainstream enterprise analytic architectures (e.g. Sybase IQ 15.4 includes native MapReduce APIs, Hadoop integration and federation, etc.)

Prediction for 2012:

As I sat down to gather my thoughts about BI in 2012, I quickly came up with the same long laundry list of BI topics as everybody else: in-memory, mobile, predictive, social, collaborative decision-making, data discovery, real-time, etc. etc.  All of these things are clearly important, and where going to continue to see great improvements this year. But I think that the real “next big thing” in BI is what I’m seeing when I talk to customers: they’re using these new opportunities not only to “improve analytics” but also fundamentally rethink some of their key business processes.

Instead of analytics being something that is used to monitor and eventually improve a business process, analytics is becoming a more fundamental part of the business process itself. One example is a large telco company that has transformed the way they attract customers. Instead of laboriously creating a range of rate plans, promoting them, and analyzing the results, they now use analytics to automatically create hundreds of more complex, personalized rate plans. They then throw them out into the market, monitor in real time, and quickly cull any that aren’t successful. It’s a way of doing business that would have been inconceivable in the past, and a lot more common in the future.

 

About

 

Timo Elliott

Timo Elliott is a 20-year veteran of SAP BusinessObjects, and has spent the last quarter-century working with customers around the world on information strategy.

He works closely with SAP research and innovation centers around the world to evangelize new technology prototypes.

His popular Business Analytics blog tracks innovation in analytics and social media, including topics such as augmented corporate reality, collaborative decision-making, and social network analysis.

His PowerPoint Twitter Tools lets presenters see and react to tweets in real time, embedded directly within their slides.

A popular and engaging speaker, Elliott presents regularly to IT and business audiences at international conferences, on subjects such as why BI projects fail and what to do about it, and the intersection of BI and enterprise 2.0.

Prior to Business Objects, Elliott was a computer consultant in Hong Kong and led analytics projects for Shell in New Zealand. He holds a first-class honors degree in Economics with Statistics from Bristol University, England

Timo can be contacted via Twitter at https://twitter.com/timoelliott

 Part 1 of this series was from James Kobielus, Forrestor at http://www.decisionstats.com/jim-kobielus-on-2012/