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Update!

I have been busy-

1) Finally my divorce came through. My advice – dont do it without a pre-nup ! Alimony means all the money.

2) Spending time on Quora after getting bored from LinkedIn, Twitter,Facebook,Google Plus,Tumblr, WordPress

See this answer to-

 What are common misconceptions about startups?

1) we will change the world
2) if we get 1% of a billion people market, we will be rich
3) if we have got funding, most of the job is done
4) lets pay ourselves high salaries since we got funded
5) our idea is awesome and cant be copied, improvised, stolen, replicated
6) startups are painless
7) it is a better life than a corporate career
8) long term vision is important than short term cash burn
9) we will never sell out or exit. never
10) its a great idea to make startups with friend

Say hello to me – http://www.quora.com/Ajay-Ohri/answers

3) Writing freelance articles on APIs for Programmable Web

Why write pro? See point 1)

Recent Articles-

http://blog.programmableweb.com/2012/07/30/predict-the-future-with-google-prediction-api/

http://blog.programmableweb.com/2012/08/01/your-store-in-the-cloud-google-cloud-storage-api/

http://blog.programmableweb.com/2012/07/27/the-romney-vs-obama-api/

4) Writing poetry on http://poemsforkush.com/. It now gets 23000 views a month. I wish I could say my poems were great, but the readers are kind (364 subscribers!) and also Google Image Search is very very kind.

5) Kicking tires with next book ” R for Cloud Computing” and be tuned for another writing announcement

6) Waiting for Paul Kent, VP, SAS Big Data to reply to my emails for interview after HE promised me!! You dont get to 105 interviews without being a bit stubborn!

7) Sighing on politics engulfing my American friends especially with regards to Chic-fil-A and Romney’s gaffes. Now thats what I call a first world problem! Protesting by eating or boycotting chicken sandwiches! In India we had the world’s biggest blackout two days in a row- and no one is attending the Hunger Fast against corruption protests!

8) Watching Olympics! Our glorious nation of 1.2 billion very smart people has managed to win 1 Bronze till today!! Michael Phelps has won more medals and more gold than the whole of  India has since the Olympics Games began!!

9) Consulting to pay the bills. includes writing R code, making presentations. Why consult when I have writing to do? See point 1)

10) Reading New York Times to get insights on Big Data and Analytics. Trust them- they know what they are doing!

Big Noise on Big Data

Increasingly Big Data is used in writing where Business Analytics was used, and data mining is thrown in as a word just to keep liberal art majors happy that they are reading a scientific article.

Some Big Words I have noticed in my Short life-

Big Data? High Performance Analytics? High Performance Computing ? Cloud Computing? Time Sharing? Data Mining? SEMMA? CRISP-DM? KDD? Business Intelligence? Business Analytics and Optimization? (pick a card and any card)

(or Just Moore’s Law catching up with the analytics)

Some examples-

Replace Big Data with Analytics in these articles and let me know if you can make out much of a difference

  • Big Data on Campus

http://www.nytimes.com/2012/07/22/education/edlife/colleges-awakening-to-the-opportunities-of-data-mining.html

  • From the man who famously said BI is dead, is now burying Business Analytics within the new buzzword , SAS CMO Jim Davis

How to transform big data from an obstacle into an asset

http://blogs.sas.com/content/corneroffice/2012/07/22/how-to-transform-big-data-from-an-obstacle-into-an-asset/

(Related- Is big data over hyped? by Jim Davis

http://www.sas.com/knowledge-exchange/business-analytics/featured/is-big-data-over-hyped/index.html )

I am sure by 2015, Jim Davis, NYT and the merry men of analytics will find some other buzzwords to rally the troops. In the meantime, let me throw out the flag and call it Big  .

Using Cloud Computing for Hacking

This is not about hacking the cloud. Instead this is about using the cloud to hack

 

Some articles last year wrote on how hackers used Amazon Ec2 for hacking/ddos attacks.

http://www.pcworld.com/businesscenter/article/216434/cloud_computing_used_to_hack_wireless_passwords.html

Roth claims that a typical wireless password can be guessed by EC2 and his software in about six minutes. He proved this by hacking networks in the area where he lives. The type of EC2 computers used in the attack costs 28 cents per minute, so $1.68 is all it could take to lay open a wireless network.

and

http://www.bloomberg.com/news/2011-05-15/sony-attack-shows-amazon-s-cloud-service-lures-hackers-at-pennies-an-hour.html

Cloud services are also attractive for hackers because the use of multiple servers can facilitate tasks such as cracking passwords, said Ray Valdes, an analyst at Gartner Inc. Amazon could improve measures to weed out bogus accounts, he said.

 

and this article by Anti-Sec pointed out how one can obtain a debit card anonymously

https://www.facebook.com/notes/lulzsec/want-to-be-a-ghost-on-the-internet/230293097062823

VPN Account without paper trail

  • Purchase prepaid visa card with cash
  • Purchase Bitcoins with Money Order
  • Donate Bitcoins to different account

 

Masking your IP address to log on is done by TOR

https://www.torproject.org/download/download.html.en

and the actual flooding is done by tools like LOIC or HOIC

http://sourceforge.net/projects/loic/

and

http://www.4shared.com/rar/UmCu0ds1/hoic.html

 

So what safeguards can be expected from the next wave of Teenage Mutant Ninjas..?

 

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.

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

Jill Dyche on 2012

In part 3 of the series for predictions for 2012, here is Jill Dyche, Baseline Consulting/DataFlux.

Part 2 was Timo Elliot, SAP at http://www.decisionstats.com/timo-elliott-on-2012/ and Part 1 was Jim Kobielus, Forrester at http://www.decisionstats.com/jim-kobielus-on-2012/

Ajay: What are the top trends you saw happening in 2011?

 

Well, I hate to say I saw them coming, but I did. A lot of managers committed some pretty predictable mistakes in 2011. Here are a few we witnessed in 2011 live and up close:

 

1.       In the spirit of “size matters,” data warehouse teams continued to trumpet the volumes of stored data on their enterprise data warehouses. But a peek under the covers of these warehouses reveals that the data isn’t integrated. Essentially this means a variety of heterogeneous virtual data marts co-located on a single server. Neat. Big. Maybe even worthy of a magazine article about how many petabytes you’ve got. But it’s not efficient, and hardly the example of data standardization and re-use that everyone expects from analytical platforms these days.

 

2.       Development teams still didn’t factor data integration and provisioning into their project plans in 2011. So we saw multiple projects spawn duplicate efforts around data profiling, cleansing, and standardization, not to mention conflicting policies and business rules for the same information. Bummer, since IT managers should know better by now. The problem is that no one owns the problem. Which brings me to the next mistake…

 

3.       No one’s accountable for data governance. Yeah, there’s a council. And they meet. And they talk. Sometimes there’s lunch. And then nothing happens because no one’s really rewarded—or penalized for that matter—on data quality improvements or new policies. And so the reports spewing from the data mart are still fraught and no one trusts the resulting decisions.

 

But all is not lost since we’re seeing some encouraging signs already in 2012. And yes, I’d classify some of them as bona-fide trends.

 

Ajay: What are some of those trends?

 

Job descriptions for data stewards, data architects, Chief Data Officers, and other information-enabling roles are becoming crisper, and the KPIs for these roles are becoming more specific. Data management organizations are being divorced from specific lines of business and from IT, becoming specialty organizations—okay, COEs if you must—in their own rights. The value proposition for master data management now includes not just the reconciliation of heterogeneous data elements but the support of key business strategies. And C-level executives are holding the data people accountable for improving speed to market and driving down costs—not just delivering cleaner data. In short, data is becoming a business enabler. Which, I have to just say editorially, is better late than never!

 

Ajay: Anything surprise you, Jill?

 

I have to say that Obama mentioning data management in his State of the Union speech was an unexpected but pretty powerful endorsement of the importance of information in both the private and public sector.

 

I’m also sort of surprised that data governance isn’t being driven more frequently by the need for internal and external privacy policies. Our clients are constantly asking us about how to tightly-couple privacy policies into their applications and data sources. The need to protect PCI data and other highly-sensitive data elements has made executives twitchy. But they’re still not linking that need to data governance.

 

I should also mention that I’ve been impressed with the people who call me who’ve had their “aha!” moment and realize that data transcends analytic systems. It’s operational, it’s pervasive, and it’s dynamic. I figured this epiphany would happen in a few years once data quality tools became a commodity (they’re far from it). But it’s happening now. And that’s good for all types of businesses.

 

About-

Jill Dyché has written three books and numerous articles on the business value of information technology. She advises clients and executive teams on leveraging technology and information to enable strategic business initiatives. Last year her company Baseline Consulting was acquired by DataFlux Corporation, where she is currently Vice President of Thought Leadership. Find her blog posts on www.dataroundtable.com.

Comic material on Google Plus

 

Here is some more memorable stuff I saw on Google Plus these last couple of weeks-

  1. This is the truth       
  2. Politically Correct  (more…)
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