Author: Ajay Ohri

http://about.me/ajayohri

Interview Kelci Miclaus, SAS Institute Using #rstats with JMP

Here is an interview with Kelci Miclaus, a researcher working with the JMP division of the SAS Institute, in which she demonstrates examples of how the R programming language is a great hit with JMP customers who like to be flexible.

 

Ajay- How has JMP been using integration with R? What has been the feedback from customers so far? Is there a single case study you can point out where the combination of JMP and R was better than any one of them alone?

Kelci- Feedback from customers has been very positive. Some customers are using JMP to foster collaboration between SAS and R modelers within their organizations. Many are using JMP’s interactive visualization to complement their use of R. Many SAS and JMP users are using JMP’s integration with R to experiment with more bleeding-edge methods not yet available in commercial software. It can be used simply to smooth the transition with regard to sending data between the two tools, or used to build complete custom applications that take advantage of both JMP and R.

One customer has been using JMP and R together for Bayesian analysis. He uses R to create MCMC chains and has found that JMP is a great tool for preparing the data for analysis, as well as displaying the results of the MCMC simulation. For example, the Control Chart platform and the Bubble Plot platform in JMP can be used to quickly verify convergence of the algorithm. The use of both tools together can increase productivity since the results of an analysis can be achieved faster than through scripting and static graphics alone.

I, along with a few other JMP developers, have written applications that use JMP scripting to call out to R packages and perform analyses like multidimensional scaling, bootstrapping, support vector machines, and modern variable selection methods. These really show the benefit of interactive visual analysis of coupled with modern statistical algorithms. We’ve packaged these scripts as JMP add-ins and made them freely available on our JMP User Community file exchange. Customers can download them and now employ these methods as they would a regular JMP platform. We hope that our customers familiar with scripting will also begin to contribute their own add-ins so a wider audience can take advantage of these new tools.

(see http://www.decisionstats.com/jmp-and-r-rstats/)

Ajay- Are there plans to extend JMP integration with other languages like Python?

Kelci- We do have plans to integrate with other languages and are considering integrating with more based on customer requests. Python has certainly come up and we are looking into possibilities there.

 Ajay- How is R a complimentary fit to JMP’s technical capabilities?

Kelci- R has an incredible breadth of capabilities. JMP has extensive interactive, dynamic visualization intrinsic to its largely visual analysis paradigm, in addition to a strong core of statistical platforms. Since our brains are designed to visually process pictures and animated graphs more efficiently than numbers and text, this environment is all about supporting faster discovery. Of course, JMP also has a scripting language (JSL) allowing you to incorporate SAS code, R code, build analytical applications for others to leverage SAS, R and other applications for users who don’t code or who don’t want to code.

JSL is a powerful scripting language on its own. It can be used for dialog creation, automation of JMP statistical platforms, and custom graphic scripting. In other ways, JSL is very similar to the R language. It can also be used for data and matrix manipulation and to create new analysis functions. With the scripting capabilities of JMP, you can create custom applications that provide both a user interface and an interactive visual back-end to R functionality. Alternatively, you could create a dashboard using statistical and/or graphical platforms in JMP to explore the data and with the click of a button, send a portion of the data to R for further analysis.

Another JMP feature that complements R is the add-in architecture, which is similar to how R packages work. If you’ve written a cool script or analysis workflow, you can package it into a JMP add-in file and send it to your colleagues so they can easily use it.

Ajay- What is the official view on R from your organization? Do you think it is a threat, or a complimentary product or another statistical platform that coexists with your offerings?

Kelci- Most definitely, we view R as complimentary. R contributors are providing a tremendous service to practitioners, allowing them to try a wide variety of methods in the pursuit of more insight and better results. The R community as a whole is providing a valued role to the greater analytical community by focusing attention on newer methods that hold the most promise in so many application areas. Data analysts should be encouraged to use the tools available to them in order to drive discovery and JMP can help with that by providing an analytic hub that supports both SAS and R integration.

Ajay-  While you do use R, are there any plans to give back something to the R community in terms of your involvement and participation (say at useR events) or sponsoring contests.

 Kelci- We are certainly open to participating in useR groups. At Predictive Analytics World in NY last October, they didn’t have a local useR group, but they did have a Predictive Analytics Meet-up group comprised of many R users. We were happy to sponsor this. Some of us within the JMP division have joined local R user groups, myself included.  Given that some local R user groups have entertained topics like Excel and R, Python and R, databases and R, we would be happy to participate more fully here. I also hope to attend the useR! annual meeting later this year to gain more insight on how we can continue to provide tools to help both the JMP and R communities with their work.

We are also exploring options to sponsor contests and would invite participants to use their favorite tools, languages, etc. in pursuit of the best model. Statistics is about learning from data and this is how we make the world a better place.

About- Kelci Miclaus

Kelci is a research statistician developer for JMP Life Sciences at SAS Institute. She has a PhD in Statistics from North Carolina State University and has been using SAS products and R for several years. In addition to research interests in statistical genetics, clinical trials analysis, and multivariate analysis/visualization methods, Kelci works extensively with JMP, SAS, and R integration.

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Facebook IPO- Do you feel lucky?

2 Jan 2011 dealbook.nytimes.com

Facebook has raised $500 million from Goldman Sachs and a Russian investor in a transaction that values the company at $50 billion

29 Jan 2011 -www.bloomberg.com-$82.9-billion

14 Jun 2011-CNBC———————-$100 billion

27 Jun 2011 -news.cnet.com———-$70 billion

27 Sep 2011-Venturebeat.com——-$82.5 billion

100 billion valuation divided by 1000 million subscribers

=100 $ net present value of ad profit (note if 80 billion valuation with 800 million subscribers it is the same)

=250 $ net present value of ad revenues (assuming 40 % profitability)

=2500 $ net present value of online purchases by Facebook ad clicking customer

(assuming advertisers dedicate 10% of revenue to advertising by Facebook)

and the lucky Russian Investor who invested at 50 billion valuation only to see it double in six months, where else has he inVested

http://nymag.com/daily/intel/2011/01/facebooks_russian_investor_hel.html

Digital Sky Technologies co-founder Yuri Milner, who co-invested in the Goldman-Facebook deal, enviably poised in the middle. DST has been investing early and aggressively in some of the biggest names in the tech bubble boom like Facebook (DST first invested in May 2009), Zynga (the company that makes Farmville and Cityville for Facebook), and Groupon (the dudes that just turned down Google’s $6 billion).

NOTE -Both groupon and Zynga IPO  investors lost money as they are now below IPO price.

http://openchannel.msnbc.msn.com/_news/2011/01/05/5771129-russian-facebook-investors-have-sparked-us-concerns

More on Digital Sky Tech and Yuri Milner and the free internet in Putin’s Russia

Digital Sky got particular attention because of its broad control of the Russian Internet. DNI noted that the company is “a dominant force in the Runet,” owning the most popular Websites in the former Soviet Union, including Russia, Ukraine, Kazakhstan, Georgia, and Armenia as well as others in the Czech Republic and Poland. By some estimates it reported “over 70 percent of all page views in the Russian-language Internet are on its companies’ Websites.”

 

 

From Wall Street Journal-

May 1, 2011

http://www.zdnet.com/blog/facebook/wsj-facebook-growth-exceeds-expectations-100-billion-valuation-justifiable/1306

Last month, a private-market transaction of 100,000 shares of Facebook Class B Common Stock priced at $32.00 apiece gave the website a valuation of $80 billion. Two months ago, Facebook was valued at $65 billion, when investment firm General Atlantic reportedly bought 0.1 percent of Facebook by purchasing roughly 2.5 million Facebook shares from former Facebook employees. Three months ago, Kleiner Perkins Caufield & Byers (KPCB) invested $38 million in Facebook, which was only worth 0.00073 percent of the social network, but still resulted in a valuation of $52 billion.

 

related-

http://techcrunch.com/2011/01/10/facebook-5/

 

Something is gotta give?

Go ahead and  Please. Buy Facebook Stock !

Do you feel lucky?

 

 

 

 

Top 5 XKCD on Data Visualization

By request, an analysis of Top 5  XKCDs on data visualization. Statisticians and Data Scientists to note-

1) DOT PLOT

 

2)  LINE PLOTS

3) FLOW CHARTS

4) PIE CHARTS and 5) BAR GRAPHS

I am not going into the big big graphs of course like the Star Wars Plot data visualization at

http://xkcd.com/657/ or the Money Chart at http://xkcd.com/980/ because I dont believe in data visualization to show off, but to keep it simple simply 🙂

Now I gotta find me a software that can write my blog for me 🙂

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.

Tantra Anjuna

While vacationing in Goa, I came across a marvelous place on Anjuna Beach called Tantra. It is basically a beach side restaurant cum hotel, but what makes it unique is the use of natural building materials like wooden poles tied together with ropes to create a magnificent and comfortable ambiance . The decor is artistic without being too loud, a big golden statue of the Buddha greets people as they come in , the music is Sanskrit and Hindi hymns , there are books left casually around for the reader. There are statues of Ganesha and a Shiva -Linga, evening lighting is shielded lighting, and dinner is over candle-lights and the manager Karan Singh has basically created a sort of mini-naturalistic paradise. It is basically India as it was meant to be, before we turned weirdly capitalistic.

 

An amazing thing at the Tantra is the pricing, they are very much a fit for the traveler who does not want to stick to expensive hotels, and does not want to compromise on hygiene. Tantra is clean, very healthy living, and you go to sleep with the sound and sight of the Ocean, and are woken gently by the Sun.

 

If you are in the mood to travel to India, you should travel to Goa, and in Goa, Tantra , Anjuna beach is one of the finest places you can have an authentic travel experience.

 

Location- Tantra is located almost at the heart of Anjuna Beach, it is just ahead of the famous Anjuna Famous Market. To get to Anjuna, you can take a taxi or Bus from Mapusa, or Margoa. To reach Margoa, you can take an air-conditioned Volvo or non-AC bus from Mumbai (that would be the best way to travel)

 

About Tantra- A great place in Anjuna, Goa  with a difference.

Set next to the flea market, surrounded by unique antique furniture and art. Experience the serene and chilled out space. Great place to watch the sunrise and sunset. Swing by.
Contact Info

TANTRA BEACH SHACK AND HUTS

Email:

singh87@yahoo.com

Website:
https://tantraanjuna.wordpress.com/
Office: +919970444637
Location:
Flea Market, Anjuna Beach, Goa, India

Note on Internet Privacy (Updated)and a note on DNSCrypt

I noticed the brouaha on Google’s privacy policy. I am afraid that social networks capture much more private information than search engines (even if they integrate my browser history, my social network, my emails, my search engine keywords) – I am still okay. All they are going to do is sell me better ads (maybe than just flood me with ads hoping to get a click). Of course Microsoft should take it one step forward and capture data from my desktop as well for better ads, that would really complete the curve. In any case , with the Patriot Act, most information is available to the Government anyway.

But it does make sense to have an easier to understand privacy policy, and one of my disappointments is the complete lack of visual appeal in such notices. Make things simple as possible, but no simpler, as Al-E said.

 

Privacy activists forget that ads run on models built on AGGREGATED data, and most models are scored automatically. Unless you do something really weird and fake like, chances are the data pertaining to you gets automatically collected, algorithmic-ally aggregated, then modeled and scored, and a corresponding ad to your score, or segment is shown to you. Probably no human eyes see raw data (but big G can clarify that)

 

( I also noticed Google gets a lot of free advice from bloggers. hey, if you were really good at giving advice to Google- they WILL hire you !)

on to another tool based (than legalese based approach to privacy)

I noticed tools like DNSCrypt increase internet security, so that all my integrated data goes straight to people I am okay with having it (ad sellers not governments!)

Unfortunately it is Mac Only, and I will wait for Windows or X based tools for a better review. I noticed some lag in updating these tools , so I can only guess that the boys of Baltimore have been there, so it is best used for home users alone.

 

Maybe they can find a chrome extension for DNS dummies.

http://www.opendns.com/technology/dnscrypt/

Why DNSCrypt is so significant

In the same way the SSL turns HTTP web traffic into HTTPS encrypted Web traffic, DNSCrypt turns regular DNS traffic into encrypted DNS traffic that is secure from eavesdropping and man-in-the-middle attacks.  It doesn’t require any changes to domain names or how they work, it simply provides a method for securely encrypting communication between our customers and our DNS servers in our data centers.  We know that claims alone don’t work in the security world, however, so we’ve opened up the source to our DNSCrypt code base and it’s available onGitHub.

DNSCrypt has the potential to be the most impactful advancement in Internet security since SSL, significantly improving every single Internet user’s online security and privacy.

and

http://dnscurve.org/crypto.html

The DNSCurve project adds link-level public-key protection to DNS packets. This page discusses the cryptographic tools used in DNSCurve.

Elliptic-curve cryptography

DNSCurve uses elliptic-curve cryptography, not RSA.

RSA is somewhat older than elliptic-curve cryptography: RSA was introduced in 1977, while elliptic-curve cryptography was introduced in 1985. However, RSA has shown many more weaknesses than elliptic-curve cryptography. RSA’s effective security level was dramatically reduced by the linear sieve in the late 1970s, by the quadratic sieve and ECM in the 1980s, and by the number-field sieve in the 1990s. For comparison, a few attacks have been developed against some rare elliptic curves having special algebraic structures, and the amount of computer power available to attackers has predictably increased, but typical elliptic curves require just as much computer power to break today as they required twenty years ago.

IEEE P1363 standardized elliptic-curve cryptography in the late 1990s, including a stringent list of security criteria for elliptic curves. NIST used the IEEE P1363 criteria to select fifteen specific elliptic curves at five different security levels. In 2005, NSA issued a new “Suite B” standard, recommending the NIST elliptic curves (at two specific security levels) for all public-key cryptography and withdrawing previous recommendations of RSA.

Some specific types of elliptic-curve cryptography are patented, but DNSCurve does not use any of those types of elliptic-curve cryptography.

 

Analytics for Cyber Conflict

 

The emerging use of Analytics and Knowledge Discovery in Databases for Cyber Conflict and Trade Negotiations

 

The blog post is the first in series or articles on cyber conflict and the use of analytics for targeting in both offense and defense in conflict situations.

 

It covers knowledge discovery in four kinds of databases (so chosen because of perceived importance , sensitivity, criticality and functioning of the geopolitical economic system)-

  1. Databases on Unique Identity Identifiers- including next generation biometric databases connected to Government Initiatives and Banking, and current generation databases of identifiers like government issued documents made online
  2. Databases on financial details -This includes not only traditional financial service providers but also online databases with payment details collected by retail product selling corporates like Sony’s Playstation Network, Microsoft ‘s XBox and
  3. Databases on contact details – including those by offline businesses collecting marketing databases and contact details
  4. Databases on social behavior- primarily collected by online businesses like Facebook , and other social media platforms.

It examines the role of

  1. voluntary privacy safeguards and government regulations ,

  2. weak cryptographic security of databases,

  3. weakness in balancing marketing ( maximized data ) with privacy (minimized data)

  4. and lastly the role of ownership patterns in database owning corporates

A small distinction between cyber crime and cyber conflict is that while cyber crime focusses on stealing data, intellectual property and information  to primarily maximize economic gains

cyber conflict focuses on stealing information and also disrupt effective working of database backed systems in order to gain notional competitive advantages in economics as well as geo-politics. Cyber terrorism is basically cyber conflict by non-state agents or by designated terrorist states as defined by the regulations of the “target” entity. A cyber attack is an offensive action related to cyber-infrastructure (like the Stuxnet worm that disabled uranium enrichment centrifuges of Iran). Cyber attacks and cyber terrorism are out of scope of this paper, we will concentrate on cyber conflicts involving databases.

Some examples are given here-

Types of Knowledge Discovery in –

1) Databases on Unique Identifiers- including biometric databases.

Unique Identifiers or primary keys for identifying people are critical for any intensive knowledge discovery program. The unique identifier generated must be extremely secure , and not liable to reverse engineering of the cryptographic hash function.

For biometric databases, an interesting possibility could be determining the ethnic identity from biometric information, and also mapping relatives. Current biometric information that is collected is- fingerprint data, eyes iris data, facial data. A further feature could be adding in voice data as a part of biometric databases.

This is subject to obvious privacy safeguards.

For example, Google recently unveiled facial recognition to unlock Android 4.0 mobiles, only to find out that the security feature could easily be bypassed by using a photo of the owner.

 

 

Example of Biometric Databases

In Afghanistan more than 2 million Afghans have contributed iris, fingerprint, facial data to a biometric database. In India, 121 million people have already been enrolled in the largest biometric database in the world. More than half a million customers of the Tokyo Mitsubishi Bank are are already using biometric verification at ATMs.

Examples of Breached Online Databases

In 2011, Playstation Network by Sony (PSN) lost data of 77 million customers including personal information and credit card information. Additionally data of 24 million customers were lost by Sony’s Sony Online Entertainment. The websites of open source platforms like SourceForge, WineHQ and Kernel.org were also broken into 2011. Even retailers like McDonald and Walgreen reported database breaches.

 

The role of cyber conflict arises in the following cases-

  1. Databases are online for accessing and authentication by proper users. Databases can be breached remotely by non-owners ( or “perpetrators”) non with much lesser chance of intruder identification, detection and penalization by regulators, or law enforcers (or “protectors”) than offline modes of intellectual property theft.

  2. Databases are valuable to external agents (or “sponsors”) subsidizing ( with finance, technology, information, motivation) the perpetrators for intellectual property theft. Databases contain information that can be used to disrupt the functioning of a particular economy, corporation (or “ primary targets”) or for further chain or domino effects in accessing other data (or “secondary targets”)

  3. Loss of data is more expensive than enhanced cost of security to database owners

  4. Loss of data is more disruptive to people whose data is contained within the database (or “customers”)

So the role play for different people for these kind of databases consists of-

1) Customers- who are in the database

2) Owners -who own the database. They together form the primary and secondary targets.

3) Protectors- who help customers and owners secure the databases.

and

1) Sponsors- who benefit from the theft or disruption of the database

2) Perpetrators- who execute the actual theft and disruption in the database

The use of topic models and LDA is known for making data reduction on text, and the use of data visualization including tied to GPS based location data is well known for investigative purposes, but the increasing complexity of both data generation and the sophistication of machine learning driven data processing makes this an interesting area to watch.

 

 

The next article in this series will cover-

the kind of algorithms that are currently or being proposed for cyber conflict, the role of non state agents , and what precautions can knowledge discovery in databases practitioners employ to avoid breaches of security, ethics, and regulation.

Citations-

  1. Michael A. Vatis , CYBER ATTACKS DURING THE WAR ON TERRORISM: A PREDICTIVE ANALYSIS Dartmouth College (Institute for Security Technology Studies).
  2. From Data Mining to Knowledge Discovery in Databases Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyt