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.
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
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.
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.
Yup Yankee Doodle. Welcome to India. Ancient Land of Mystique. Modern Land of taking your job.
Let me give you how many jobs we created in India from you.
“Generation of employment
The rapid growth in IT-BPO industry has created large number of jobs for the expanding employablepopulation. The employment provided by the industry increased more than 8 times over FY2000-2009and reached 2.2 million in FY2009.”
6. Foreign trade grew rapidly and trade surplus was reduced to some extent. The total value of imports and exports for the first three quarters of this year was US$ 2,148.7 billion, up by 37.9 percent year-on-year. The value of exports was US$ 1,134.6 billion, up by 34.0 percent, and the value of imports was US$ 1,014.0 billion, up by 42.4 percent. The trade surplus was US$ 120.6 billion, a decline of US$ 14.9 billion over the same period last year.”
Thats a lot of money employing a lot of Chinese, maybe even more than the 1 million American jobs that went to India.
The computers, electronic equipment, and parts industries experienced the largest growth in trade deficits with China, leading with 627,700 (26%) of all jobs displaced between 2001 and 2008. As a result, the hardest hit Congressional districts were located in California and Texas, where remaining jobs in those industries are concentrated, and in North Carolina, which was hard hit by job displacement in a variety of manufacturing industries
NOTE- Thats also a very sensitive area in terms of security cyber et all
Anyways, thats all now. Thanks for the jobs Yankee boys
(written by an Indian who dislikes job losses anywhere)
ALISO VIEJO, Calif., Oct 19, 2010 (BUSINESS WIRE) — Predixion Software today introduced Predixion PMML Connexion(TM), an interface that provides Predixion Insight(TM), the company’s low-cost, self-service in the cloud predictive analytics solution, direct and seamless access to SAS, SPSS (IBM) and other predictive models for use by Predixion Insight customers. Predixion PMML Connexion enables companies to leverage their significant investments in legacy predictive analytics solutions at a fraction of the cost of conventional licensing and maintenance fees.
The announcement was made at the Predictive Analytics World conference in Washington, D.C. where Predixion also announced a strategic partnership with Zementis, Inc., a market leader in PMML-based solutions. Zementis is exhibiting in Booth #P2.
The Predictive Model Markup Language (PMML) standard allows for true interoperability, offering a mature standard for moving predictive models seamlessly between platforms. Predixion has fully integrated this PMML functionality into Predixion Insight, meaning Predixion Insight users can now effortlessly import PMML-based predictive models, enabling information workers to score the models in the cloud from anywhere and publish reports using Microsoft Excel(R) and SharePoint(R). In addition, models can also be written back into SAS, SPSS and other platforms for a truly collaborative, interoperable solution.
“Predixion’s investment in this PMML interface makes perfect business sense as the lion’s share of the models in existence today are created by the SAS and SPSS platforms, creating compelling opportunity to leverage existing investments in predictive and statistical models on a low-cost cloud predictive analytics platform that can be fed with enterprise, line of business and cloud-based data,” said Mike Ferguson, CEO of Intelligent Business Strategies, a leading analyst and consulting firm specializing in the areas of business intelligence and enterprise business integration. “In this economy, Predixion’s low-cost, self-service predictive analytics solutions might be welcome relief to IT organizations chartered with quickly adding additional applications while at the same time cutting costs and staffing.”
“We are pleased to be partnering with Zementis, truly a PMML market leader and innovator,” said Predixion CEO Simon Arkell. “To allow any SAS or SPSS customer to immediately score any of their predictive models in the cloud from within Predixion Insight, compare those models to those created by Predixion Insight, and share the results within Excel and Sharepoint is an exciting step forward for the industry. SAS and SPSS customers are fed up with the high prices they must pay for their business users just to access reports generated by highly skilled PhDs who are burdened by performing routine tasks and thus have become a massive bottleneck. That frustration is now a thing of the past because any information worker can now unlock the power of predictive analytics without relying on experts — for a fraction of the cost and from anywhere they can connect to the cloud,” Arkell said.
Dr. Michael Zeller, Zementis CEO, added, “Our mission is to significantly shorten the time-to-market for predictive models in any industry. We are excited to be contributing to Predixion’s self-service, cloud-based predictive analytics solution set.”
About Predixion Software
Predixion Software develops and markets collaborative predictive analytics solutions in the public and private cloud. Predixion enables self-service predictive analytics, allowing customers to use and analyze large amounts of data to make actionable decisions, all within the familiar environment of Excel and PowerPivot. Predixion customers are achieving immediate results across a multitude of industries including: retail, finance, healthcare, marketing, telecommunications and insurance/risk management.
Predixion Software is headquartered in Aliso Viejo, California with development offices in Redmond, Washington. The company has venture capital backing from established investors including DFJ Frontier, Miramar Venture Partners and Palomar Ventures. For more information please contact us at 949-330-6540, or visit us atwww.predixionsoftware.com.
Zementis, Inc. is a leading software company focused on the operational deployment and integration of predictive analytics and data mining solutions. Its ADAPA(R) decision engine successfully bridges the gap between science and engineering. ADAPA(R) was designed from the ground up to benefit from open standards and to significantly shorten the time-to-market for predictive models in any industry. For more information, please visit www.zementis.com.
Revolution Analytics has just released Revolution R Enterprise 4.0.1 for Red Hat Enterprise Linux, a significant step forward in enterprise data analytics. Revolution R Enterprise 4.0.1 is built on R 2.11.1, the latest release of the open-source environment for data analysis and graphics. Also available is the initial release of our deployment server solution, RevoDeployR 1.0, designed to help you deliver R analytics via the Web. And coming soon to Linux: RevoScaleR, a new package for fast and efficient multi-core processing of large data sets.
As a registered user of the Academic version of Revolution R Enterprise for Linux, you can take advantage of these improvements by downloading and installing Revolution R Enterprise 4.0.1 today. You can install Revolution R Enterprise 4.0.1 side-by-side with your existing Revolution R Enterprise installations; there is no need to uninstall previous versions.
The following information is all you will need to download and install the Academic Edition.
Revolution R Enterprise Academic edition and RevoDeployR are supported on Red Hat® Enterprise Linux® 5.4 or greater (64-bit processors).
Approximately 300MB free disk space is required for a full install of Revolution R Enterprise. We recommend at least 1GB of RAM to use Revolution R Enterprise.
For the full list of system requirements for RevoDeployR, refer to the RevoDeployR™ Installation Guide for Red Hat® Enterprise Linux®.
You will first need to download the Revolution R Enterprise installer.
Installation Instructions for Revolution R Enterprise Academic Edition
After downloading the installer, do the following to install the software:
Unpack the installer using the following command:
tar -xzf Revo-Ent-4.0.1-RHEL5-desktop.tar.gz
Change directory to the RevolutionR_4.0.1 directory created.
Run the installer by typing ./install.py and following the on-screen prompts.
Getting Started with the Revolution R Enterprise
After you have installed the software, launch Revolution R Enterprise by typing Revo64 at the shell prompt.
Documentation is available in the form of PDF documents installed as part of the Revolution R Enterprise distribution. Type Revo.home(“doc”) at the R prompt to locate the directory containing the manuals Getting Started with Revolution R (RevoMan.pdf) and the ParallelR User’s Guide(parRman.pdf).
Installation Instructions for RevoDeployR (and RServe)
After downloading the RevoDeployR distribution, use the following steps to install the software:
Note: These instructions are for an automatic install. For more details or for manual install instructions, refer to RevoDeployR_Installation_Instructions_for_RedHat.pdf.
Log into the operating system as root.
Change directory to the directory containing the downloaded distribution for RevoDeployR and RServe.
Unzip the contents of the RevoDeployR tar file. At prompt, type:
tar -xzf deployrRedHat.tar.gz
Change directories. At the prompt, type:
Launch the automated installation script and follow the on-screen prompts. At the prompt, type:
./installRedHat.sh Note:Red Hat installs MySQL without a password.
Getting Started with RevoDeployR
After installing RevoDeployR, you will be directed to the RevoDeployR landing page. The landing page has links to documentation, the RevoDeployR management console, the API Explorer development tool, and sample code.
The simple R-benchmark-25.R test script is a quick-running survey of general R performance. The Community-developed test consists of three sets of small benchmarks, referred to in the script as Matrix Calculation, Matrix Functions, and Program Control.
Revolution Analytics has created its own tests to simulate common real-world computations. Their descriptions are explained below.
Linear Algebra Computation
Base R 2.9.2
Revolution R (1-core)
Revolution R (4-core)
Speedup (4 core)
Singular Value Decomposition
Principal Components Analysis
Linear Discriminant Analysis
Speedup = Slower time / Faster Time – 1
This routine creates a random uniform 10,000 x 5,000 matrix A, and then times the computation of the matrix product transpose(A) * A.
m <- 10000
n <- 5000
A <- matrix (runif (m*n),m,n)
system.time (B <- crossprod(A))
The system will respond with a message in this format:
User system elapsed
37.22 0.40 9.68
The “elapsed” times indicate total wall-clock time to run the timed code.
The table above reflects the elapsed time for this and the other benchmark tests. The test system was an INTEL® Xeon® 8-core CPU (model X55600) at 2.5 GHz with 18 GB system RAM running Windows Server 2008 operating system. For the Revolution R benchmarks, the computations were limited to 1 core and 4 cores by calling setMKLthreads(1) and setMKLthreads(4) respectively. Note that Revolution R performs very well even in single-threaded tests: this is a result of the optimized algorithms in the Intel MKL library linked to Revolution R. The slight greater than linear speedup may be due to the greater total cache available to all CPU cores, or simply better OS CPU scheduling–no attempt was made to pin execution threads to physical cores. Consult Revolution R’s documentation to learn how to run benchmarks that use less cores than your hardware offers.
The Cholesky matrix factorization may be used to compute the solution of linear systems of equations with a symmetric positive definite coefficient matrix, to compute correlated sets of pseudo-random numbers, and other tasks. We re-use the matrix B computed in the example above:
system.time (C <- chol(B))
Singular Value Decomposition with Applications
The Singular Value Decomposition (SVD) is a numerically-stable and very useful matrix decompisition. The SVD is often used to compute Principal Components and Linear Discriminant Analysis.
# Singular Value Deomposition
m <- 10000
n <- 2000
A <- matrix (runif (m*n),m,n)
system.time (S <- svd (A,nu=0,nv=0))
# Principal Components Analysis
m <- 10000
n <- 2000
A <- matrix (runif (m*n),m,n)
system.time (P <- prcomp(A))
# Linear Discriminant Analysis require (‘MASS’)
g <- 5
k <- round (m/2)
A <- data.frame (A, fac=sample (LETTERS[1:g],m,replace=TRUE))
train <- sample(1:m, k)
system.time (L <- lda(fac ~., data=A, prior=rep(1,g)/g, subset=train))
The September meeting is at the Oracle campus. (This is next door to the Oracle towers, so there is plenty of free parking.) The featured talk is from Alex Guazzelli (Vice President – Analytics, Zementis Inc.) who will talk about “Predictive analytics with R, PMML and ADAPA”.
* 6:15 – 7:00 Networking and Pizza (with thanks to Revolution Analytics)
* 7:00 – 8:00 Talk: Predictive analytics with R, PMML and ADAPA
* 8:00 – 8:30 General discussion
The rule in the past was that whenever a model was built in a particular development environment, it remained in that environment forever, unless it was manually recoded to work somewhere else. This rule has been shattered with the advent of PMML (Predictive Modeling Markup Language). By providing a uniform standard to represent predictive models, PMML allows for the exchange of predictive solutions between different applications and various vendors.
Once exported as PMML files, models are readily available for deployment into an execution engine for scoring or classification. ADAPA is one example of such an engine. It takes in models expressed in PMML and transforms them into web-services. Models can be executed either remotely by using web-services calls, or via a web console. Users can also use an Excel add-in to score data from inside Excel using models built in R.
R models have been exported into PMML and uploaded in ADAPA for many different purposes. Use cases where clients have used the flexibility of R to develop and the PMML standard combined with ADAPA to deploy range from financial applications (e.g., risk, compliance, fraud) to energy applications for the smart grid. The ability to easily transition solutions developed in R to the operational IT production environment helps eliminate the traditional limitations of R, e.g. performance for high volume or real-time transactional systems and memory constraints associated with large data sets.
Dr. Alex Guazzelli has co-authored the first book on PMML, the Predictive Model Markup Language which is the de facto standard used to represent predictive models. The book, entitled PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics, is available on Amazon.com. As the Vice President of Analytics at Zementis, Inc., Dr. Guazzelli is responsible for developing core technology and analytical solutions under ADAPA, a PMML-based predictive decisioning platform that combines predictive analytics and business rules. ADAPA is the first system of its kind to be offered as a service on the cloud.
Prior to joining Zementis, Dr. Guazzelli was involved in not only building but also deploying predictive solutions for large financial and telecommunication institutions around the globe. In academia, Dr. Guazzelli worked with data mining, neural networks, expert systems and brain theory. His work in brain theory and computational neuroscience has appeared in many peer reviewed publications. At Zementis, Dr. Guazzelli and his team have been involved in a myriad of modeling projects for financial, health-care, gaming, chemical, and manufacturing industries.
Dr. Guazzelli holds a Ph.D. in Computer Science from the University of Southern California and a M.S and B.S. in Computer Science from the Federal University of Rio Grande do Sul, Brazil.