Tag: easy

Predictive Models Ain’t Easy to Deploy

 

This is a guest blog post by Carole Ann Matignon of Sparkling Logic. You can see more on Sparkling Logic at http://my.sparklinglogic.com/

Decision Management is about combining predictive models and business rules to automate decisions for your business. Insurance underwriting, loan origination or workout, claims processing are all very good use cases for that discipline… But there is a hiccup… It ain’t as easy you would expect…

What’s easy?

If you have a neat model, then most tools would allow you to export it as a PMML model – PMML stands for Predictive Model Markup Language and is a standard XML representation for predictive model formulas. Many model development tools let you export it without much effort. Many BRMS – Business rules Management Systems – let you import it. Tada… The model is ready for deployment.

What’s hard?

The problem that we keep seeing over and over in the industry is the issue around variables.

Those neat predictive models are formulas based on variables that may or may not exist as is in your object model. When the variable is itself a formula based on the object model, like the min, max or sum of Dollar amount spent in Groceries in the past 3 months, and the object model comes with transaction details, such that you can compute it by iterating through those transactions, then the problem is not “that” big. PMML 4 introduced some support for those variables.

The issue that is not easy to fix, and yet quite frequent, is when the model development data model does not resemble the operational one. Your Data Warehouse very likely flattened the object model, and pre-computed some aggregations that make the mapping very hard to restore.

It is clearly not an impossible project as many organizations do that today. It comes with a significant overhead though that forces modelers to involve IT resources to extract the right data for the model to be operationalized. It is a heavy process that is well justified for heavy-duty models that were developed over a period of time, with a significant ROI.

This is a show-stopper though for other initiatives which do not have the same ROI, or would require too frequent model refresh to be viable. Here, I refer to “real” model refresh that involves a model reengineering, not just a re-weighting of the same variables.

For those initiatives where time is of the essence, the challenge will be to bring closer those two worlds, the modelers and the business rules experts, in order to streamline the development AND deployment of analytics beyond the model formula. The great opportunity I see is the potential for a better and coordinated tuning of the cut-off rules in the context of the model refinement. In other words: the opportunity to refine the strategy as a whole. Very ambitious? I don’t think so.

About Carole Ann Matignon

http://my.sparklinglogic.com/index.php/company/management-team

Carole-Ann Matignon Print E-mail

Carole-Ann MatignonCarole-Ann Matignon – Co-Founder, President & Chief Executive Officer

She is a renowned guru in the Decision Management space. She created the vision for Decision Management that is widely adopted now in the industry.  Her claim to fame is managing the strategy and direction of Blaze Advisor, the leading BRMS product, while she also managed all the Decision Management tools at FICO (business rules, predictive analytics and optimization). She has a vision for Decision Management both as a technology and a discipline that can revolutionize the way corporations do business, and will never get tired of painting that vision for her audience.  She speaks often at Industry conferences and has conducted university classes in France and Washington DC.

She started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication. At Cleversys (acquired by Kurt Salmon & Associates), she also conducted strategic consulting gigs around change management.

While playing with advanced software components, she found a passion for technology and joined ILOG (acquired by IBM). She developed a growing interest in Optimization as well as Business Rules. At ILOG, she coined the term BRMS while brainstorming with her Sales counterpart. She led the Presales organization for Telecom in the Americas up until 2000 when she joined Blaze Software (acquired by Brokat Technologies, HNC Software and finally FICO).

Her 360-degree experience allowed her to gain appreciation for all aspects of a software company, giving her a unique perspective on the business. Her technical background kept her very much in touch with technology as she advanced.

Statistical Theory for High Performance Analytics

A thing that strikes me when I was a student of statistics is that most theories of sampling, testing of hypothesis and modeling were built in an age where data was predominantly insufficient, computation was inherently manual and results of tests aimed at large enough differences.

I look now at the explosion of data, at the cloud computing enabled processing power on demand, and competitive dynamics of businesses to venture out my opinion-

1) We now have large , even excess data than we had before for statisticians a generation ago.

2) We now have extremely powerful computing devices, provided we can process our algorithms in parallel.

3) Even a slight uptick in modeling efficiency or mild uptick in business insight can provide huge monetary savings.

Call it High Performance Analytics or Big Data or Cloud Computing- are we sure statisticians are creating enough mathematical theory or are we just taking it easy in our statistics classrooms only to be subjected to something completely different when we hit the analytics workplace.

Do we  need more theorists as well? Is there ANY incentive for corporations with private R and D research teams to share their latest cutting edge theoretical work outside their corporate silo.

 

Related-

“a mathematician is a machine for turning coffee into theorems”

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 Appliance, Oracle 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

Machine Learning for Hackers – #rstats

I got the incredible and intriguing Machine Learning for Hackers for just $15.99 for an electronic copy from O Reilly Media. (Deal of the Day!)

It has just been launched this month!!

It is an incredible book- and I really  like the way O Reilly has made it so easy to download E Books

I am trying to read it while trying to a write a whole lot of other stuff— and it seems easy to read and understand even for non-hackers like me. Esp with Stanford delaying its online machine learning course- this is one handy e-book to have  to get you started in ML and data science!!

Click the image to see the real deal.

http://shop.oreilly.com/product/0636920018483.do

 

Analytics for Cyber Conflict -Part Deux

Part 1 in this series is avaiable at http://www.decisionstats.com/analytics-for-cyber-conflict/

The next articles in this series will cover-

  1. the kind of algorithms that are currently or being proposed for cyber conflict, as well as or detection

Cyber Conflict requires some basic elements of the following broad disciplines within Computer and Information Science (besides the obvious disciplines of heterogeneous database types for different kinds of data) –

1) Cryptography – particularly a cryptographic  hash function that maximizes cost and time of the enemy trying to break it.

From http://en.wikipedia.org/wiki/Cryptographic_hash_function

The ideal cryptographic hash function has four main or significant properties:

  • it is easy (but not necessarily quick) to compute the hash value for any given message
  • it is infeasible to generate a message that has a given hash
  • it is infeasible to modify a message without changing the hash
  • it is infeasible to find two different messages with the same hash

A commercial spin off is to use this to anonymized all customer data stored in any database, such that no database (or data table) that is breached contains personally identifiable information. For example anonymizing the IP Addresses and DNS records with a mashup  (embedded by default within all browsers) of Tor and MafiaaFire extensions can help create better information privacy on the internet.

This can also help in creating better encryption between Instant Messengers in Communication

2) Data Disaster Planning for Data Storage (but also simulations for breaches)- including using cloud computing, time sharing, or RAID for backing up data. Planning and creating an annual (?) exercise for a simulated cyber breach of confidential just like a cyber audit- similar to an annual accounting audit

3) Basic Data Reduction Algorithms for visualizing large amounts of information. This can include

  1. K Means Clustering, http://www.jstor.org/pss/2346830 , http://www.cs.ust.hk/~qyang/Teaching/537/Papers/huang98extensions.pdf , and http://stackoverflow.com/questions/6372397/k-means-with-really-large-matrix
  2. Topic Models (LDA) http://www.decisionstats.com/topic-models/,
  3. Social Network Analysis http://en.wikipedia.org/wiki/Social_network_analysis,
  4. Graph Analysis http://micans.org/mcl/ and http://www.ncbi.nlm.nih.gov/pubmed/19407357
  5. MapReduce and Parallelization algorithms for computational boosting http://www.slideshare.net/marin_dimitrov/large-scale-data-analysis-with-mapreduce-part-i

In the next article we will examine

  1. the role of non state agents as well as state agents competing and cooperating,
  2. and what precautions can knowledge discovery in databases practitioners employ to avoid breaches of security, ethics, and regulation.

Interview JJ Allaire Founder, RStudio

Here is an interview with JJ Allaire, founder of RStudio. RStudio is the IDE that has overtaken other IDE within the R Community in terms of ease of usage. On the eve of their latest product launch, JJ talks to DecisionStats on RStudio and more.

Ajay-  So what is new in the latest version of RStudio and how exactly is it useful for people?

JJ- The initial release of RStudio as well as the two follow-up releases we did last year were focused on the core elements of using R: editing and running code, getting help, and managing files, history, workspaces, plots, and packages. In the meantime users have also been asking for some bigger features that would improve the overall work-flow of doing analysis with R. In this release (v0.95) we focused on three of these features:

Projects. R developers tend to have several (and often dozens) of working contexts associated with different clients, analyses, data sets, etc. RStudio projects make it easy to keep these contexts well separated (with distinct R sessions, working directories, environments, command histories, and active source documents), switch quickly between project contexts, and even work with multiple projects at once (using multiple running versions of RStudio).

Version Control. The benefits of using version control for collaboration are well known, but we also believe that solo data analysis can achieve significant productivity gains by using version control (this discussion on Stack Overflow talks about why). In this release we introduced integrated support for the two most popular open-source version control systems: Git and Subversion. This includes changelist management, file diffing, and browsing of project history, all right from within RStudio.

Code Navigation. When you look at how programmers work a surprisingly large amount of time is spent simply navigating from one context to another. Modern programming environments for general purpose languages like C++ and Java solve this problem using various forms of code navigation, and in this release we’ve brought these capabilities to R. The two main features here are the ability to type the name of any file or function in your project and go immediately to it; and the ability to navigate to the definition of any function under your cursor (including the definition of functions within packages) using a keystroke (F2) or mouse gesture (Ctrl+Click).

Ajay- What’s the product road map for RStudio? When can we expect the IDE to turn into a full fledged GUI?

JJ- Linus Torvalds has said that “Linux is evolution, not intelligent design.” RStudio tries to operate on a similar principle—the world of statistical computing is too deep, diverse, and ever-changing for any one person or vendor to map out in advance what is most important. So, our internal process is to ship a new release every few months, listen to what people are doing with the product (and hope to do with it), and then start from scratch again making the improvements that are considered most important.

Right now some of the things which seem to be top of mind for users are improved support for authoring and reproducible research, various editor enhancements including code folding, and debugging tools.

What you’ll see is us do in a given release is to work on a combination of frequently requested features, smaller improvements to usability and work-flow, bug fixes, and finally architectural changes required to support current or future feature requirements.

While we do try to base what we work on as closely as possible on direct user-feedback, we also adhere to some core principles concerning the overall philosophy and direction of the product. So for example the answer to the question about the IDE turning into a full-fledged GUI is: never. We believe that textual representations of computations provide fundamental advantages in transparency, reproducibility, collaboration, and re-usability. We believe that writing code is simply the right way to do complex technical work, so we’ll always look for ways to make coding better, faster, and easier rather than try to eliminate coding altogether.

Ajay -Describe your journey in science from a high school student to your present work in R. I noticed you have been very successful in making software products that have been mostly proprietary products or sold to companies.

Why did you get into open source products with RStudio? What are your plans for monetizing RStudio further down the line?

JJ- In high school and college my principal areas of study were Political Science and Economics. I also had a very strong parallel interest in both computing and quantitative analysis. My first job out of college was as a financial analyst at a government agency. The tools I used in that job were SAS and Excel. I had a dim notion that there must be a better way to marry computation and data analysis than those tools, but of course no concept of what this would look like.

From there I went more in the direction of general purpose computing, starting a couple of companies where I worked principally on programming languages and authoring tools for the Web. These companies produced proprietary software, which at the time (between 1995 and 2005) was a workable model because it allowed us to build the revenue required to fund development and to promote and distribute the software to a wider audience.

By 2005 it was however becoming clear that proprietary software would ultimately be overtaken by open source software in nearly all domains. The cost of development had shrunken dramatically thanks to both the availability of high-quality open source languages and tools as well as the scale of global collaboration possible on open source projects. The cost of promoting and distributing software had also collapsed thanks to efficiency of both distribution and information diffusion on the Web.

When I heard about R and learned more about it, I become very excited and inspired by what the project had accomplished. A group of extremely talented and dedicated users had created the software they needed for their work and then shared the fruits of that work with everyone. R was a platform that everyone could rally around because it worked so well, was extensible in all the right ways, and most importantly was free (as in speech) so users could depend upon it as a long-term foundation for their work.

So I started RStudio with the aim of making useful contributions to the R community. We started with building an IDE because it seemed like a first-rate development environment for R that was both powerful and easy to use was an unmet need. Being aware that many other companies had built successful businesses around open-source software, we were also convinced that we could make RStudio available under a free and open-source license (the AGPLv3) while still creating a viable business. At this point RStudio is exclusively focused on creating the best IDE for R that we can. As the core product gets where it needs to be over the next couple of years we’ll then also begin to sell other products and services related to R and RStudio.

About-

http://rstudio.org/docs/about

Jjallaire

JJ Allaire

JJ Allaire is a software engineer and entrepreneur who has created a wide variety of products including ColdFusion,Windows Live Writer, Lose It!, and RStudio.

From http://en.wikipedia.org/wiki/Joseph_J._Allaire
In 1995 Joseph J. (JJ) Allaire co-founded Allaire Corporation with his brother Jeremy Allaire, creating the web development tool ColdFusion.[1] In March 2001, Allaire was sold to Macromedia where ColdFusion was integrated into the Macromedia MX product line. Macromedia was subsequently acquired by Adobe Systems, which continues to develop and market ColdFusion.
After the sale of his company, Allaire became frustrated at the difficulty of keeping track of research he was doing using Google. To address this problem, he co-founded Onfolio in 2004 with Adam Berrey, former Allaire co-founder and VP of Marketing at Macromedia.
On March 8, 2006, Onfolio was acquired by Microsoft where many of the features of the original product are being incorporated into the Windows Live Toolbar. On August 13, 2006, Microsoft released the public beta of a new desktop blogging client called Windows Live Writer that was created by Allaire’s team at Microsoft.
Starting in 2009, Allaire has been developing a web-based interface to the widely used R technical computing environment. A beta version of RStudio was publicly released on February 28, 2011.
JJ Allaire received his B.A. from Macalester College (St. Paul, MN) in 1991.
RStudio-

RStudio is an integrated development environment (IDE) for R which works with the standard version of R available from CRAN. Like R, RStudio is available under a free software license. RStudio is designed to be as straightforward and intuitive as possible to provide a friendly environment for new and experienced R users alike. RStudio is also a company, and they plan to sell services (support, training, consulting, hosting) related to the open-source software they distribute.

Amazon gives away 750 hours /month of Windows based computing

and an additional 750 hours /month of Linux based computing. The windows instance is really quite easy for users to start getting the hang of cloud computing. and it is quite useful for people to tinker around, given Google’s retail cloud offerings are taking so long to hit the market

But it is only for new users.

http://aws.typepad.com/aws/2012/01/aws-free-usage-tier-now-includes-microsoft-windows-on-ec2.html

WS Free Usage Tier now Includes Microsoft Windows on EC2

The AWS Free Usage Tier now allows you to run Microsoft Windows Server 2008 R2 on an EC2 t1.micro instance for up to 750 hours per month. This benefit is open to new AWS customers and to those who are already participating in the Free Usage Tier, and is available in all AWS Regions with the exception of GovCloud. This is an easy way for Windows users to start learning about and enjoying the benefits of cloud computing with AWS.

The micro instances provide a small amount of consistent processing power and the ability to burst to a higher level of usage from time to time. You can use this instance to learn about Amazon EC2, support a development and test environment, build an AWS application, or host a web site (or all of the above). We’ve fine-tuned the micro instances to make them even better at running Microsoft Windows Server.

You can launch your instance from the AWS Management Console:

We have lots of helpful resources to get you started:

Along with 750 instance hours of Windows Server 2008 R2 per month, the Free Usage Tier also provides another 750 instance hours to run Linux (also on a t1.micro), Elastic Load Balancer time and bandwidth, Elastic Block Storage, Amazon S3 Storage, and SimpleDB storage, a bunch of Simple Queue Service and Simple Notification Service requests, and some CloudWatch metrics and alarms (see the AWS Free Usage Tier page for details). We’ve also boosted the amount of EBS storage space offered in the Free Usage Tier to 30GB, and we’ve doubled the I/O requests in the Free Usage Tier, to 2 million.