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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-
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)
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!
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)
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
- 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
(Related- Is big data over hyped? by Jim Davis
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 .
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.
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.
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
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
and the actual flooding is done by tools like LOIC or HOIC
So what safeguards can be expected from the next wave of Teenage Mutant Ninjas..?
Part 1 in this series is avaiable at http://www.decisionstats.com/analytics-for-cyber-conflict/
The next articles in this series will cover-
- 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.
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
- 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
- Topic Models (LDA) http://www.decisionstats.com/topic-models/,
- Social Network Analysis http://en.wikipedia.org/wiki/Social_network_analysis,
- Graph Analysis http://micans.org/mcl/ and http://www.ncbi.nlm.nih.gov/pubmed/19407357
- 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
- the role of non state agents as well as state agents competing and cooperating,
- and what precautions can knowledge discovery in databases practitioners employ to avoid breaches of security, ethics, and regulation.
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.
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.