Tag: Language

Mapreduce Book

Here is a new book on learning MapReduce and it has a free downloadable version as well.

Data-Intensive Text Processing with MapReduce

Jimmy Lin and Chris Dyer

ABSTRACT

Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader “think in MapReduce”, but also discusses limitations of the programming model as well.

You can download the book here

This book is part of the Morgan & Claypool Synthesis Lectures on Human Language Technologies. If you’re at a university, your institution may already subscribe to the series, in which case you can access the electronic version directly without cost (see this page for a list of institutional subscribers). Otherwise, to purchase:

Quite explicitly, this book focuses on MapReduce algorithm design, not Hadoop programming. Tom White’s Hadoop: The Definitive Guide is a great resource for learning Hadoop.

Want to be notified of updates? Interested in MapReduce algorithm design? Follow @lintool on Twitter here!

Big Data and R: New Product Release by Revolution Analytics

Press Release by the Guys in Revolution Analytics- this time claiming to enable terabyte level analytics with R. Interesting stuff but techie details are awaited.

Revolution Analytics Brings

Big Data Analysis to R

The world’s most powerful statistics language can now tackle terabyte-class data sets using

Revolution R Enterprise—at a fraction of the cost of legacy analytics products


JSM 2010 – VANCOUVER (August 3, 2010) — Revolution Analytics today introduced ‘Big Data’ analysis to its Revolution R Enterprise software, taking the popular R statistics language to unprecedented new levels of capacity and performance for analyzing very large data sets. For the first time, R users will be able to process, visualize and model terabyte-class data sets in a fraction of the time of legacy products—without employing expensive or specialized hardware.

The new version of Revolution R Enterprise introduces an add-on package called RevoScaleR that provides a new framework for fast and efficient multi-core processing of large data sets. It includes:

  • The XDF file format, a new binary ‘Big Data’ file format with an interface to the R language that provides high-speed access to arbitrary rows, blocks and columns of data.
  • A collection of widely-used statistical algorithms optimized for Big Data, including high-performance implementations of Summary Statistics, Linear Regression, Binomial Logistic Regressionand Crosstabs—with more to be added in the near future.
  • Data Reading & Transformation tools that allow users to interactively explore and prepare large data sets for analysis.
  • Extensibility, expert R users can develop and extend their own statistical algorithms to take advantage of Revolution R Enterprise’s new speed and scalability capabilities.

“The R language’s inherent power and extensibility has driven its explosive adoption as the modern system for predictive analytics,” said Norman H. Nie, president and CEO of Revolution Analytics. “We believe that this new Big Data scalability will help R transition from an amazing research and prototyping tool to a production-ready platform for enterprise applications such as quantitative finance and risk management, social media, bioinformatics and telecommunications data analysis.”

Sage Bionetworks is the nonprofit force behind the open-source collaborative effort, Sage Commons, a place where data and disease models can be shared by scientists to better understand disease biology. David Henderson, Director of Scientific Computing at Sage, commented: “At Sage Bionetworks, we need to analyze genomic databases hundreds of gigabytes in size with R. We’re looking forward to using the high-speed data-analysis features of RevoScaleR to dramatically reduce the times it takes us to process these data sets.”

Take Hadoop and Other Big Data Sources to the Next Level

Revolution R Enterprise fits well within the modern ‘Big Data’ architecture by leveraging popular sources such as Hadoop, NoSQL or key value databases, relational databases and data warehouses. These products can be used to store, regularize and do basic manipulation on very large datasets—while Revolution R Enterprise now provides advanced analytics at unparalleled speed and scale: producing speed on speed.

“Together, Hadoop and R can store and analyze massive, complex data,” said Saptarshi Guha, developer of the popular RHIPE R package that integrates the Hadoop framework with R in an automatically distributed computing environment. “Employing the new capabilities of Revolution R Enterprise, we will be able to go even further and compute Big Data regressions and more.”

Platforms and Availability

The new RevoScaleR package will be delivered as part of Revolution R Enterprise 4.0, which will be available for 32-and 64-bit Microsoft Windows in the next 30 days. Support for Red Hat Enterprise Linux (RHEL 5) is planned for later this year.

On its website (http://www.revolutionanalytics.com/bigdata), Revolution Analytics has published performance and scalability benchmarks for Revolution R Enterprise analyzing a 13.2 gigabyte data set of commercial airline information containing more than 123 million rows, and 29 columns.

Additionally, the company will showcase its new Big Data solution in a free webinar on August 25 at 9:00 a.m. Pacific.

Additional Resources

•      Big Data Benchmark whitepaper

•      The Revolution Analytics Roadmap whitepaper

•      Revolutions Blog

•      Download free academic copy of Revolution R Enterprise

•      Visit Inside-R.org for the most comprehensive set of information on R

•      Spread the word: Add a “Download R!” badge on your website

•      Follow @RevolutionR on Twitter

About Revolution Analytics

Revolution Analytics (http://www.revolutionanalytics.com) is the leading commercial provider of software and support for the popular open source R statistics language. Its Revolution R products help make predictive analytics accessible to every type of user and budget. The company is headquartered in Palo Alto, Calif. and backed by North Bridge Venture Partners and Intel Capital.

Media Contact

Chantal Yang
Page One PR, for Revolution Analytics
Tel: +1 415-875-7494

Email:  revolution@pageonepr.com

R Oracle Data Mining

Here is a new package called R ODM and it is an interface to do Data Mining via Oracle Tables through R. You can read more here http://www.oracle.com/technetwork/database/options/odm/odm-r-integration-089013.html and here http://cran.fhcrc.org/web/packages/RODM/RODM.pdf . Also there is a contest for creative use of R and ODM.

R Interface to Oracle Data Mining

The R Interface to Oracle Data Mining ( R-ODM) allows R users to access the power of Oracle Data Mining’s in-database functions using the familiar R syntax. R-ODM provides a powerful environment for prototyping data analysis and data mining methodologies.

R-ODM is especially useful for:

  • Quick prototyping of vertical or domain-based applications where the Oracle Database supports the application
  • Scripting of “production” data mining methodologies
  • Customizing graphics of ODM data mining results (examples: classification, regression, anomaly detection)

The R-ODM interface allows R users to mine data using Oracle Data Mining from the R programming environment. It consists of a set of function wrappers written in source R language that pass data and parameters from the R environment to the Oracle RDBMS enterprise edition as standard user PL/SQL queries via an ODBC interface. The R-ODM interface code is a thin layer of logic and SQL that calls through an ODBC interface. R-ODM does not use or expose any Oracle product code as it is completely an external interface and not part of any Oracle product. R-ODM is similar to the example scripts (e.g., the PL/SQL demo code) that illustrates the use of Oracle Data Mining, for example, how to create Data Mining models, pass arguments, retrieve results etc.

R-ODM is packaged as a standard R source package and is distributed freely as part of the R environment’s Comprehensive R Archive Network ( CRAN). For information about the R environment, R packages and CRAN, see www.r-project.org.

and

Present and win an Apple iPod Touch!
The BI, Warehousing and Analytics (BIWA) SIG is giving an Apple iPOD Touch to the best new presenter. Be part of the TechCast series and get a chance to win!

Consider highlighting a creative use of R and ODM.

BIWA invites all Oracle professionals (experts, end users, managers, DBAs, developers, data analysts, ISVs, partners, etc.) to submit abstracts for 45 minute technical webcasts to our Oracle BIWA (IOUG SIG) Community in our Wednesday TechCast series. Note that the contest is limited to new presenters to encourage fresh participation by the BIWA community.

Also an interview with Oracle Data Mining head, Charlie Berger https://decisionstats.wordpress.com/2009/09/02/oracle/

SAS Sentiment Analysis wins Award

From Business Wire, the new Sentiment Analysis product by SAS Institute (created by acquisition Teragram ) wins an award. As per wikipedia

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

Sentiment analysis or opinion mining refers to a broad (definitionally challenged) area of natural language processing, computational linguistics and text mining. Generally speaking, it aims to determine the attitude of a speaker or a writer with respect to some topic. The attitude may be their judgment or evaluation (see appraisal theory), their affective state (that is to say, the emotional state of the author when writing) or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader).

It was developed by Teragram. Here is another Sentiment Analysis tool from Stanford Grad school at http://twittersentiment.appspot.com/search?query=sas

See-

Sentiment analysis for sas

–

Image Citation-

http://threeminds.organic.com/2009/09/five_reasons_sentiment_analysi.html

Read an article on sentiment analysis here at http://www.nytimes.com/2009/08/24/technology/internet/24emotion.html

And the complete press release at http://goo.gl/iVzf`

–SAS Sentiment Analysis delivers insights on customer, competitor and organizational opinions to a degree never before possible via manual review of electronic text. As a result, SAS, the leader in business analytics software and services, has earned the prestigious Communications Solutions Product of the Year Award fromTechnology Marketing Corporation (TMC).

“SAS has automated the time-consuming process of reading individual documents and manually extracting relevant information”

“SAS Sentiment Analysis has shown benefits for its customers and it provides ROI for the companies that use it,” said Rich Tehrani, CEO, TMC. “Congratulations to the entire team at SAS, a company distinguished by its dedication to software quality and superiority to address marketplace needs.”

Derive positive and negative opinions, evaluations and emotions

SAS Sentiment Analysis’ high-performance crawler locates and extracts sentiment from digital content sources, including mainstream websites, social media outlets, internal servers and incoming news feeds. SAS’ unique hybrid approach combines powerful statistical techniques with linguistics rules to improve accuracy to the detailed feature level. It summarizes the sentiment expressed in all available text collections – identifying trends and creating graphical reports that describe the expressed feelings of consumers, partners, employees and competitors in real time. Output from SAS Sentiment Analysis can be stored in document repositories, surfaced in corporate portals and used as input to additional SAS Text Analytics software or search engines to help decision makers evaluate trends, predict future outcomes, minimize risks and capitalize on opportunities.

“SAS has automated the time-consuming process of reading individual documents and manually extracting relevant information,” said Fiona McNeill, Global Analytics Product Marketing Manager at SAS. “Our integrated analytics framework helps organizations maximize the value of information to improve their effectiveness.”

SAS Sentiment Analysis is included in the SAS Text Analytics suite, which helps organizations discover insights from electronic text materials, associate them for delivery to the right person or place, and provide intelligence to select the best course of action. Whether answering complex search-and-retrieval questions, ensuring appropriate content is presented to internal or external constituencies, or predicting which activity or channel will produce the best effect on existing sentiments, SAS Text Analytics provides exceptional real-time processing speeds for large volumes of text.

SAS Text Analytics solutions are part of the SAS Business Analytics Framework, backed by the industry’s most comprehensive range of consulting, training and support services, ensuring customers maximum return from their IT investments.

Recognizing vision

The Communications Solutions Product of the Year Award recognizes vision, leadership and thoroughness. The most innovative products and services brought to the market from March 2008 through March 2009 were chosen as winners of this Product of the Year Award and are published on the INTERNET TELEPHONY and Customer Interaction Solutions websites.

Protected: Analyzing SAS Institute-WPS Lawsuit

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Protected: SAS Institute lawsuit against WPS Episode 2 The Clone Wars

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