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Access PAW DC Session Videos Now
Predictive Analytics World is pleased to announce on-demand access to the videos of PAW Washington DC, October 2010, including over 30 sessions and keynotes that you may view at your convenience. Access this leading predictive analytics content online now:
Select individual conference sessions, or recognize savings by registering for access to one or two full days of sessions. These on-demand videos deliver PAW DC right to your desk, covering hot topics and advanced methods such as:
PAW DC videos feature over 25 speakers with case studies from leading enterprises such as: CIBC, CEB, Forrester, Macy’s, MetLife, Microsoft, Miles Kimball, Monster.com, Oracle, Paychex, SunTrust, Target, UPMC, Xerox, Yahoo!, YMCA, and more.
Keynote: Five Ways Predictive Analytics Cuts Enterprise Risk
Eric Siegel,Ph.D., Program Chair, Predictive Analytics World
All business is an exercise in risk management. All organizations would benefit from measuring, tracking and computing risk as a core process, much like insurance companies do.
Predictive analytics does the trick, one customer at a time. This technology is a data-driven means to compute the risk each customer will defect, not respond to an expensive mailer, consume a retention discount even if she were not going to leave in the first place, not be targeted for a telephone solicitation that would have landed a sale, commit fraud, or become a “loss customer” such as a bad debtor or an insurance policy-holder with high claims.
In this keynote session, Dr. Eric Siegel reveals:
– Five ways predictive analytics evolves your enterprise to reduce risk
– Hidden sources of risk across operational functions
– What every business should learn from insurance companies
– How advancements have reversed the very meaning of fraud
– Why “man + machine” teams are greater than the sum of their parts for enterprise decision support
Platinum Sponsor Presentation: Analytics – The Beauty of Diversity
Anne H. Milley,Senior Director of Analytic Strategy, Worldwide Product Marketing, SAS
Analytics contributes to, and draws from, multiple disciplines. The unifying theme of “making the world a better place” is bred from diversity. For instance, the same methods used in econometrics might be used in market research, psychometrics and other disciplines. In a similar way, diverse paradigms are needed to best solve problems, reveal opportunities and make better decisions. This is why we evolve capabilities to formulate and solve a wide range of problems through multiple integrated languages and interfaces. Extending that, we have provided integration with other languages so that users can draw on the disciplines and paradigms needed to best practice their craft.
Gold Sponsor Presentation: Predictive Analytics Accelerate Insight for Financial Services
Finbarr Deely,Director of Business Development,ParAccel
Financial services organizations face immense hurdles in maintaining profitability and building competitive advantage. Financial services organizations must perform “what-if” scenario analysis, identify risks, and detect fraud patterns. The advanced analytic complexity required often makes such analysis slow and painful, if not impossible. This presentation outlines the analytic challenges facing these organizations and provides a clear path to providing the accelerated insight needed to perform in today’s complex business environment to reduce risk, stop fraud and increase profits. * The value of predictive analytics in Accelerating Insight * Financial Services Analytic Case Studies * Brief Overview of ParAccel Analytic Database
TOPIC: SURVEY ANALYSIS Case Study: YMCA Turning Member Satisfaction Surveys into an Actionable Narrative
Dean Abbott,President, Abbott Analytics
Employees are a key constituency at the Y and previous analysis has shown that their attitudes have a direct bearing on Member Satisfaction. This session will describe a successful approach for the analysis of YMCA employee surveys. Decision trees are built and examined in depth to identify key questions in describing key employee satisfaction metrics, including several interesting groupings of employee attitudes. Our approach will be contrasted with other factor analysis and regression-based approaches to survey analysis that we used initially. The predictive models described are currently in use and resulted in both greater understanding of employee attitudes, and a revised “short-form” survey with fewer key questions identified by the decision trees as the most important predictors.
TOPIC: INDUSTRY TRENDS 2010 Data Minter Survey Results: Highlights
Karl Rexer,Ph.D., Rexer Analytics
Do you want to know the views, actions, and opinions of the data mining community? Each year, Rexer Analytics conducts a global survey of data miners to find out. This year at PAW we unveil the results of our 4th Annual Data Miner Survey. This session will present the research highlights, such as:
Multiple Case Studies: U.S. DoD, U.S. DHS, SSA Text Mining: Lessons Learned
John F. Elder,Chief Scientist, Elder Research, Inc.
Text Mining is the “Wild West” of data mining and predictive analytics – the potential for gain is huge, the capability claims are often tall tales, and the “land rush” for leadership is very much a race.
In solving unstructured (text) analysis challenges, we found that principles from inductive modeling – learning relationships from labeled cases – has great power to enhance text mining. Dr. Elder highlights key technical breakthroughs discovered while working on projects for leading government agencies, including: Text Mining is the “Wild West” of data mining and predictive analytics – the potential for gain is huge, the capability claims are often tall tales, and the “land rush” for leadership is very much a race.
– Prioritizing searches for the Dept. of Homeland Security
– Quick decisions for Social Security Admin. disability
– Document discovery for the Dept. of Defense
– Disease discovery for the Dept. of Homeland Security
Keynote: How Target Gets the Most out of Its Guest Data to Improve Marketing ROI
Andrew Pole,Senior Manager, Media and Database Marketing, Target
In this session, you’ll learn how Target leverages its own internal guest data to optimize its direct marketing – with the ultimate goal of enhancing our guests’ shopping experience and driving in-store and online performance. You will hear about what guest data is available at Target, how and where we collect it, and how it is used to improve the performance and relevance of direct marketing vehicles. Furthermore, we will discuss Target’s development and usage of guest segmentation, response modeling, and optimization as means to suppress poor performers from mailings, determine relevant product categories and services for online targeted content, and optimally assign receipt marketing offers to our guests when offer quantities are limited.
Platinum Sponsor Presentation: Driving Analytics Into Decision Making
Jason Verlen,Director, SPSS Product Strategy & Management, IBM Software Group
Organizations looking to dramatically improve their business outcomes are turning to decision management, a convergence of technology and business processes that is used to streamline and predict the outcome of daily decision-making. IBM SPSS Decision Management technology provides the critical link between analytical insight and recommended actions. In this session you’ll learn how Decision Management software integrates analytics with business rules and business applications for front-line systems such as call center applications, insurance claim processing, and websites. See how you can improve every customer interaction, minimize operational risk, reduce fraud and optimize results.
TOPIC: DATA INFRASTRUCTURE AND INTEGRATION Case Study: Macy’s The world is not flat (even though modeling software has to think it is)
Paul Coleman,Director of Marketing Statistics, Macy’s Inc.
Software for statistical modeling generally use flat files, where each record represents a unique case with all its variables. In contrast most large databases are relational, where data are distributed among various normalized tables for efficient storage. Variable creation and model scoring engines are necessary to bridge data mining and storage needs. Development datasets taken from a sampled history require snapshot management. Scoring datasets are taken from the present timeframe and the entire available universe. Organizations, with significant data, must decide when to store or calculate necessary data and understand the consequences for their modeling program.
TOPIC: CUSTOMER VALUE Case Study: SunTrust When One Model Will Not Solve the Problem – Using Multiple Models to Create One Solution
Dudley Gwaltney,Group Vice President, Analytical Modeling, SunTrust Bank
In 2007, SunTrust Bank developed a series of models to identify clients likely to have large changes in deposit balances. The models include three basic binary and two linear regression models.
Based on the models, 15% of SunTrust clients were targeted as those most likely to have large balance changes. These clients accounted for 65% of the absolute balance change and 60% of the large balance change clients. The targeted clients are grouped into a portfolio and assigned to individual SunTrust Retail Branch. Since 2008, the portfolio generated a 2.6% increase in balances over control.
Using the SunTrust example, this presentation will focus on:
TOPIC: RESPONSE & CROSS-SELL Case Study: Paychex Staying One Step Ahead of the Competition – Development of a Predictive 401(k) Marketing and Sales Campaign
Jason Fox,Information Systems and Portfolio Manager,Paychex
In-depth case study of Paychex, Inc. utilizing predictive modeling to turn the tides on competitive pressures within their own client base. Paychex, a leading provider of payroll and human resource solutions, will guide you through the development of a Predictive 401(k) Marketing and Sales model. Through the use of sophisticated data mining techniques and regression analysis the model derives the probability a client will add retirement services products with Paychex or with a competitor. Session will include roadblocks that could have ended development and ROI analysis. Speaker: Frank Fiorille, Director of Enterprise Risk Management, Paychex Speaker: Jason Fox, Risk Management Analyst, Paychex
TOPIC: SEGMENTATION Practitioner: Canadian Imperial Bank of Commerce Segmentation Do’s and Don’ts
Daymond Ling,Senior Director, Modelling & Analytics,Canadian Imperial Bank of Commerce
The concept of Segmentation is well accepted in business and has withstood the test of time. Even with the advent of new artificial intelligence and machine learning methods, this old war horse still has its place and is alive and well. Like all analytical methods, when used correctly it can lead to enhanced market positioning and competitive advantage, while improper application can have severe negative consequences.
This session will explore what are the elements of success, and what are the worse practices that lead to failure. The relationship between segmentation and predictive modeling will also be discussed to clarify when it is appropriate to use one versus the other, and how to use them together synergistically.
TOPIC: SOCIAL DATA
Thought Leadership Social Network Analysis: Killer Application for Cloud Analytics
James Kobielus,Senior Analyst, Forrester Research
Social networks such as Twitter and Facebook are a potential goldmine of insights on what is truly going through customers´minds. Every company wants to know whether, how, how often, and by whom they´re being mentioned across the billowing new cloud of social media. Just as important, every company wants to influence those discussions in their favor, target new business, and harvest maximum revenue potential. In this session, Forrester analyst James Kobielus identifies fruitful applications of social network analysis in customer service, sales, marketing, and brand management. He presents a roadmap for enterprises to leverage their inline analytics initiatives and leverage high-performance data warehousing (DW) clouds and appliances in order to analyze shifting patterns of customer sentiment, influence, and propensity. Leveraging Forrester’s ongoing research in advanced analytics and customer relationship management, Kobielus will discuss industry trends, commercial modeling tools, and emerging best practices in social network analysis, which represents a game-changing new discipline in predictive analytics.
Trish Mathe,
Director of Database Marketing, Life Line Screening
While Life Line is successfully executing a US CRM roadmap, they are also beginning this same evolution abroad. They are beginning in the UK where Merkle procured data and built a response model that is pulling responses over 30% higher than competitors. This presentation will give an overview of the US CRM roadmap, and then focus on the beginning of their strategy abroad, focusing on the data procurement they could not get anywhere else but through Merkle and the successful modeling and analytics for the UK. Speaker: Ozgur Dogan, VP, Quantitative Solutions Group, Merkle Inc Speaker: Trish Mathe, Director of Database Marketing, Life Line Screening
TOPIC: SURVEY ANALYSIS Case Study: Forrester Making Survey Insights Addressable and Scalable – The Case Study of Forrester’s Technographics Benchmark Survey
Marketers use surveys to create enterprise wide applicable strategic insights to: (1) develop segmentation schemes, (2) summarize consumer behaviors and attitudes for the whole US population, and (3) use multiple surveys to draw unified views about their target audience. However, these insights are not directly addressable and scalable to the whole consumer universe which is very important when applying the power of survey intelligence to the one to one consumer marketing problems marketers routinely face. Acxiom partnered with Forrester Research, creating addressable and scalable applications of Forrester’s Technographics Survey and applied it successfully to a number of industries and applications.
TOPIC: HEALTHCARE Case Study: UPMC Health Plan A Predictive Model for Hospital Readmissions
Scott Zasadil,Senior Scientist, UPMC Health Plan
Hospital readmissions are a significant component of our nation’s healthcare costs. Predicting who is likely to be readmitted is a challenging problem. Using a set of 123,951 hospital discharges spanning nearly three years, we developed a model that predicts an individual’s 30-day readmission should they incur a hospital admission. The model uses an ensemble of boosted decision trees and prior medical claims and captures 64% of all 30-day readmits with a true positive rate of over 27%. Moreover, many of the ‘false’ positives are simply delayed true positives. 53% of the predicted 30-day readmissions are readmitted within 180 days.
My annual traffic to this blog was almost 99,000 . Add in additional views on networking sites plus the 400 plus RSS readers- so I can say traffic was 1,20,000 for 2010. Nice. Thanks for reading and hope it was worth your time. (this is a long post and will take almost 440 secs to read but the summary is just given)
My intent is either to inform you, give something useful or atleast something interesting.
see below-
Jan
Feb
Mar
Apr
May
Jun
2010
6,311
4,701
4,922
5,463
6,493
4,271
Jul
Aug
Sep
Oct
Nov
Dec
Total
5,041
5,403
17,913
16,430
11,723
10,096
98,767
Sandro Saita from http://www.dataminingblog.com/ just named me for an award on his blog (but my surname is ohRi , Sandro left me without an R- What would I be without R :)) ).
Aw! I am touched. Google for “Data Mining Blog” and Sandro is the best that it is in data mining writing.
”
DMR People Award 2010
There are a lot of active people in the field of data mining. You can discuss with them on forums. You can read their blogs. You can also meet them in events such as PAW or KDD. Among the people I follow on a regular basis, I have elected:
Ajay Ori
He has been very active in 2010, especially on his blog . Good work Ajay and continue sharing your experience with us!”
What did I write in 2010- stuff.
What did you read on this blog- well thats the top posts list.
well I guess I owe Tal G for almost 9000 views ( incidentally I withdrew posting my blog from R- Bloggers and Analyticbridge blogs – due to SEO keyword reasons and some spam I was getting see (below))
Still reading this post- gosh let me sell you some advertising. It is only $100 a month (yes its a recession)
Advertisers are treated on First in -Last out (FILO)
I have been told I am obsessed with SEO , but I dont care much for search engines apart from Google, and yes SEO is an interesting science (they should really re name it GEO or Google Engine Optimization)
Apparently Hadley Wickham and Donald Farmer are big keywords for me so I should be more respectful I guess.
Search Terms for 365 days ending 2010-12-31 (Summarized)
2009-12-31 to Today
Search
Views
libre office
925
facebook analytics
798
test drive a chrome notebook
467
test drive a chrome notebook.
215
r gui
203
data mining
163
wps sas lawsuit
158
wordle.net
133
wps sas
123
google maps jet ski
123
test drive chrome notebook
96
sas wps
89
sas wps lawsuit
85
chrome notebook test drive
83
decision stats
83
best statistics software
74
hadley wickham
72
google maps jetski
72
libreoffice
70
doug savage
65
hive tutorial
58
funny india
56
spss certification
52
donald farmer microsoft
51
best statistical software
49
What about outgoing links? Apparently I need to find a way to ask Google to pay me for the free advertising I gave their chrome notebook launch. But since their search engine and browser is free to me, guess we are even steven.
Clicks for 365 days ending 2010-12-31 (Summarized)
The only issue is Rattle can be quite difficult to install due to dependencies on GTK+
After fiddling for a couple of years- this is what I did
1) Created dual boot OS- Basically downloaded the netbook remix from http://ubuntu.com I created a dual boot OS so you can choose at the beginning whether to use Windows or Ubuntu Linux in that session. Alternatively you can download VM Player www.vmware.com/products/player/ if you want to do both
2) Download R packages using Ubuntu packages and Install GTK+ dependencies before that.
GTK + Requires
Libglade
Glib
Cairo
Pango
ATK
If you are a Linux newbie like me who doesnt get the sudo apt get, tar, cd, make , install rigmarole – scoot over to synaptic software packages or just the main ubuntu software centre and download these packages one by one.
For R Dependencies, you need
PMML
XML
RGTK2
Again use r-cran as the prefix to these package names and simply install (almost the same way Windows does it easily -double click)
Save theses to your hard disk (e.g., to your Desktop) but don’t extract them. Then, on GNU/Linux run the install command shown below. This command is entered into a terminal window:
R CMD INSTALL rattle_2.6.0.tar.gz
After installation-
5) Type library(rattle) and rattle.info to get messages on what R packages to update for a proper functioning
</code>
> library(rattle)
Rattle: Graphical interface for data mining using R.
Version 2.6.0 Copyright (c) 2006-2010 Togaware Pty Ltd.
Type 'rattle()' to shake, rattle, and roll your data.
> rattle.info()
Rattle: version 2.6.0
R: version 2.11.1 (2010-05-31) (Revision 52157)
Sysname: Linux
Release: 2.6.35-23-generic
Version: #41-Ubuntu SMP Wed Nov 24 10:18:49 UTC 2010
Nodename: k1-M725R
Machine: i686
Login: k1ng
User: k1ng
Installed Dependencies
RGtk2: version 2.20.3
pmml: version 1.2.26
colorspace: version 1.0-1
cairoDevice: version 2.14
doBy: version 4.1.2
e1071: version 1.5-24
ellipse: version 0.3-5
foreign: version 0.8-41
gdata: version 2.8.1
gtools: version 2.6.2
gplots: version 2.8.0
gWidgetsRGtk2: version 0.0-69
Hmisc: version 3.8-3
kernlab: version 0.9-12
latticist: version 0.9-43
Matrix: version 0.999375-46
mice: version 2.4
network: version 1.5-1
nnet: version 7.3-1
party: version 0.9-99991
playwith: version 0.9-53
randomForest: version 4.5-36 upgrade available 4.6-2
rggobi: version 2.1.16
survival: version 2.36-2
XML: version 3.2-0
bitops: version 1.0-4.1
Upgrade the packages with:
> install.packages(c("randomForest"))
<code>
Now upgrade whatever package rattle.info tells to upgrade.
This is much simpler and less frustrating than some of the other ways to install Rattle.
If all goes well, you will see this familiar screen popup when you type
Additional features in R over other analytical packages-
1) Source Code is given to ensure complete custom solution and embedding for a particular application. Open source code has an advantage that is extensively peer- reviewed in Journals and Scientific Literature. This means bugs will found, shared and corrected transparently.
2) Wide literature of training material in the form of books is available for the R analytical platform.
3) Extensively the best data visualization tools in analytical software (apart from Tableau Software ‘s latest version). The extensive data visualization available in R is of the form a variety of customizable graphs, as well as animation. The principal reason third-party software initially started creating interfaces to R is because the graphical library of packages in R is more advanced as well as rapidly getting more features by the day.
4) Free in upfront license cost for academics and thus budget friendly for small and large analytical teams.
5) Flexible programming for your data environment. This includes having packages that ensure compatibility with Java, Python and C++.
6) Easy migration from other analytical platforms to R Platform. It is relatively easy for a non R platform user to migrate to R platform and there is no danger of vendor lock-in due to the GPL nature of source code and open community.
Statistics are numbers that tell (descriptive), advise ( prescriptive) or forecast (predictive). Analytics is a decision-making help tool. Analytics on which no decision is to be made or is being considered can be classified as purely statistical and non analytical. Thus ease of making a correct decision separates a good analytical platform from a not so good analytical platform. The distinction is likely to be disputed by people of either background- and business analysis requires more emphasis on how practical or actionable the results are and less emphasis on the statistical metrics in a particular data analysis task. I believe one clear reason between business analytics is different from statistical analysis is the cost of perfect information (data costs in real world) and the opportunity cost of delayed and distorted decision-making.
Specific to the following domains R has the following costs and benefits
Business Analytics
R is free per license and for download
It is one of the few analytical platforms that work on Mac OS
It’s results are credibly established in both journals like Journal of Statistical Software and in the work at LinkedIn, Google and Facebook’s analytical teams.
It has open source code for customization as per GPL
It also has a flexible option for commercial vendors like Revolution Analytics (who support 64 bit windows) as well as bigger datasets
It has interfaces from almost all other analytical software including SAS,SPSS, JMP, Oracle Data Mining, Rapid Miner. Existing license holders can thus invoke and use R from within these software
Huge library of packages for regression, time series, finance and modeling
High quality data visualization packages
Data Mining
R as a computing platform is better suited to the needs of data mining as it has a vast array of packages covering standard regression, decision trees, association rules, cluster analysis, machine learning, neural networks as well as exotic specialized algorithms like those based on chaos models.
Flexibility in tweaking a standard algorithm by seeing the source code
The RATTLE GUI remains the standard GUI for Data Miners using R. It was created and developed in Australia.
Business Dashboards and Reporting
Business Dashboards and Reporting are an essential piece of Business Intelligence and Decision making systems in organizations. R offers data visualization through GGPLOT, and GUI like Deducer and Red-R can help even non R users create a metrics dashboard
For online Dashboards- R has packages like RWeb, RServe and R Apache- which in combination with data visualization packages offer powerful dashboard capabilities.
R can be combined with MS Excel using the R Excel package – to enable R capabilities to be imported within Excel. Thus a MS Excel user with no knowledge of R can use the GUI within the R Excel plug-in to use powerful graphical and statistical capabilities.
Additional factors to consider in your R installation-
There are some more choices awaiting you now-
1) Licensing Choices-Academic Version or Free Version or Enterprise Version of R
2) Operating System Choices-Which Operating System to choose from? Unix, Windows or Mac OS.
3) Operating system sub choice- 32- bit or 64 bit.
4) Hardware choices-Cost -benefit trade-offs for additional hardware for R. Choices between local ,cluster and cloud computing.
5) Interface choices-Command Line versus GUI? Which GUI to choose as the default start-up option?
6) Software component choice- Which packages to install? There are almost 3000 packages, some of them are complimentary, some are dependent on each other, and almost all are free.
7) Additional Software choices- Which additional software do you need to achieve maximum accuracy, robustness and speed of computing- and how to use existing legacy software and hardware for best complementary results with R.
1) Licensing Choices-
You can choose between two kinds of R installations – one is free and open source from http://r-project.org The other R installation is commercial and is offered by many vendors including Revolution Analytics. However there are other commercial vendors too.
Windows remains the most widely used operating system on this planet. If you are experienced in Windows based computing and are active on analytical projects- it would not make sense for you to move to other operating systems. This is also based on the fact that compatibility problems are minimum for Microsoft Windows and the help is extensively documented. However there may be some R packages that would not function well under Windows- if that happens a multiple operating system is your next option.
Enterprise R from Revolution Analytics- Enterprise R from Revolution Analytics has a complete R Development environment for Windows including the use of code snippets to make programming faster. Revolution is also expected to make a GUI available by 2011. Revolution Analytics claims several enhancements for it’s version of R including the use of optimized libraries for faster performance.
MacOS
Reasons for choosing MacOS remains its considerable appeal in aesthetically designed software- but MacOS is not a standard Operating system for enterprise systems as well as statistical computing. However open source R claims to be quite optimized and it can be used for existing Mac users. However there seem to be no commercially available versions of R available as of now for this operating system.
Linux
Ubuntu
Red Hat Enterprise Linux
Other versions of Linux
Linux is considered a preferred operating system by R users due to it having the same open source credentials-much better fit for all R packages and it’s customizability for big data analytics.
Ubuntu Linux is recommended for people making the transition to Linux for the first time. Ubuntu Linux had an marketing agreement with revolution Analytics for an earlier version of Ubuntu- and many R packages can installed in a straightforward way as Ubuntu/Debian packages are available. Red Hat Enterprise Linux is officially supported by Revolution Analytics for it’s enterprise module. Other versions of Linux popular are Open SUSE.
Multiple operating systems-
Virtualization vs Dual Boot-
You can also choose between having a VMware VM Player for a virtual partition on your computers that is dedicated to R based computing or having operating system choice at the startup or booting of your computer. A software program called wubi helps with the dual installation of Linux and Windows.
64 bit vs 32 bit – Given a choice between 32 bit versus 64 bit versions of the same operating system like Linux Ubuntu, the 64 bit version would speed up processing by an approximate factor of 2. However you need to check whether your current hardware can support 64 bit operating systems and if so- you may want to ask your Information Technology manager to upgrade atleast some operating systems in your analytics work environment to 64 bit operating systems.
Hardware choices- At the time of writing this book, the dominant computing paradigm is workstation computing followed by server-client computing. However with the introduction of cloud computing, netbooks, tablet PCs, hardware choices are much more flexible in 2011 than just a couple of years back.
Hardware costs are a significant cost to an analytics environment and are also remarkably depreciated over a short period of time. You may thus examine your legacy hardware, and your future analytical computing needs- and accordingly decide between the various hardware options available for R.
Unlike other analytical software which can charge by number of processors, or server pricing being higher than workstation pricing and grid computing pricing extremely high if available- R is well suited for all kinds of hardware environment with flexible costs. Given the fact that R is memory intensive (it limits the size of data analyzed to the RAM size of the machine unless special formats and /or chunking is used)- it depends on size of datasets used and number of concurrent users analyzing the dataset. Thus the defining issue is not R but size of the data being analyzed.
Local Computing- This is meant to denote when the software is installed locally. For big data the data to be analyzed would be stored in the form of databases.
Server version- Revolution Analytics has differential pricing for server -client versions but for the open source version it is free and the same for Server or Workstation versions.
Workstation
Cloud Computing- Cloud computing is defined as the delivery of data, processing, systems via remote computers. It is similar to server-client computing but the remote server (also called cloud) has flexible computing in terms of number of processors, memory, and data storage. Cloud computing in the form of public cloud enables people to do analytical tasks on massive datasets without investing in permanent hardware or software as most public clouds are priced on pay per usage. The biggest cloud computing provider is Amazon and many other vendors provide services on top of it. Google is also coming for data storage in the form of clouds (Google Storage), as well as using machine learning in the form of API (Google Prediction API)
Amazon
Google
Cluster-Grid Computing/Parallel processing- In order to build a cluster, you would need the RMpi and the SNOW packages, among other packages that help with parallel processing.
How much resources
RAM-Hard Disk-Processors- for workstation computing
Instances or API calls for cloud computing
Interface Choices
Command Line
GUI
Web Interfaces
Software Component Choices
R dependencies
Packages to install
Recommended Packages
Additional software choices
Additional legacy software
Optimizing your R based computing
Code Editors
Code Analyzers
Libraries to speed up R
citation- R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
Best of All- I really liked this online book created by Professor S. Sayad
Its succinct and beautiful and describes all of the Data Mining you want to read in one Map (actually 4 images painstakingly assembled with perfection)
The best thing is- in the original map- even the sub items are click-able for specifics like Pie Chart and Stacked Column chart are not in one simple drop down like Charts- but rather by nature of the kind of variables that lead to these charts. For doing that- you would need to go to the site itself- ( see http://chem-eng.utoronto.ca/~datamining/dmc/categorical_variables.htm
Again- there is no mention of the data visualization software used to create the images but I think I can take a hint from the Software Page which says software used are-
Predictive Analytics World March 2011 in San Francisco is packed with the top predictive analytics experts, practitioners, authors and business thought leaders, including keynote speakers:
Predictive Analytics World focuses on concrete examples of deployed predictive analytics. Hear from the horse’s mouth precisely how Fortune 500 analytics competitors and other top practitioners deploy predictive modeling, and what kind of business impact it delivers. Click here to view the agenda at-a-glance.
PAW SF 2011 will feature speakers with case studies from leading enterprises. such as:
PAW’s March agenda covers hot topics and advanced methods such as uplift (net lift) modeling, ensemble models, social data, search marketing, crowdsourcing, blackbox trading, fraud detection, risk management, survey analysis and otherinnovative applications that benefit organizations in new and creative ways.
Join PAW and access the best keynotes, sessions, workshops, exposition, expert panel, live demos, networking coffee breaks, reception, birds-of-a-feather lunches, brand-name enterprise leaders, and industry heavyweights in the business.
Complex Event Processing (CEP- not to be confused by Circular Probability Error) is defined processing many events happening across all the layers of an organization, identifying the most meaningful events within the event cloud, analyzing their impact, and taking subsequent action in real time.
Oracle CEP is a Java application server for the development and deployment of high-performance event driven applications. It can detect patterns in the flow of events and message payloads, often based on filtering, correlation, and aggregation across event sources, and includes industry leading temporal and ordering capabilities. It supports ultra-high throughput (1 million/sec++) and microsecond latency.
Tibco is also trying to get into this market (it claims to have a 40 % market share in the public CEP market 😉 though probably they have not measured the DoE and DoD as worthy of market share yet
What it is: Methods 1 through 3 look at historical data and traditional architectures with information stored in the warehouse. In this environment, it often takes months of data cleansing and preparation to get the data ready to analyze. Now, what if you want to make a decision or determine the effect of an action in real time, as a sale is made, for instance, or at a specific step in the manufacturing process. With streaming data architectures, you can look at data in the present and make immediate decisions. The larger flood of data coming from smart phones, online transactions and smart-grid houses will continue to increase the amount of data that you might want to analyze but not keep. Real-time streaming, complex event processing (CEP) and analytics will all come together here to let you decide on the fly which data is worth keeping and which data to analyze in real time and then discard.
When you use it: Radio-frequency identification (RFID) offers a good user case for this type of architecture. RFID tags provide a lot of information, but unless the state of the item changes, you don’t need to keep warehousing the data about that object every day. You only keep data when it moves through the door and out of the warehouse.
The same concept applies to a customer who does the same thing over and over. You don’t need to keep storing data for analysis on a regular pattern, but if they change that pattern, you might want to start paying attention.
Figure 4: Traditional architecture vs. streaming architecture
In academia here is something called SASE Language
The query below retrieves the total trading volume of Google stocks in the 4 hour period after some bad news occurred.
PATTERN SEQ(News a, Stock+ b[ ])WHERE [symbol] AND a.type = 'bad' AND b[i].symbol = 'GOOG' WITHIN 4 hoursHAVING b[b.LEN].volume < 80%*b[1].volumeRETURN sum(b[ ].volume)
The next query reports a one-hour period in which the price of a stock increased from 10 to 20 and its trading volume stayed relatively stable.
PATTERN SEQ(Stock+ a[])WHERE [symbol] AND a[1].price = 10 AND a[i].price > a[i-1].price AND a[a.LEN].price = 20 WITHIN 1 hourHAVING avg(a[].volume) ≥ a[1].volumeRETURN a[1].symbol, a[].price
The third query detects a more complex trend: in an hour, the volume of a stock started high, but after a period of price increasing or staying relatively stable, the volume plummeted.
PATTERN SEQ(Stock+ a[], Stock b)WHERE [symbol] AND a[1].volume > 1000 AND a[i].price > avg(a[…i-1].price)) AND b.volume < 80% * a[a.LEN].volume WITHIN 1 hourRETURN a[1].symbol, a[].(price,volume), b.(price,volume)
(note from Ajay-
I was not really happy about the depth of resources on CEP available online- there seem to be missing bits and pieces in both open source, academic and corporate information- one reason for this is the obvious military dual use of this technology- like feeds from Satellite, Audio Scans, etc)
r-bloggers.com
Reddit
Twitter
Google
en.wordpress.com
xianblog.wordpress.com
DECISION STATS 