Stephen E. Arnold: In the Cloud Big Data Meta Data Hack

Stephen E. Arnold

Stephen E. Arnold

Finally Some Cloudy News on Metadata

For Obama’s 2012 re-election campaign, his team broke down data silos and moved all the data to a cloud repository. The team built Narwhal, a shared data store interface for all of the campaigns’ application. Narwhal was dubbed “Obama’s White Whale,” because it is almost a mythical technology that federal agencies have been trying to develop for years. While Obama may be hanging out with Queequag and Ishmael, there is a more viable solution for the cloud says GCN’s article, “Big Metadata: 7 Ways To Leverage Your Data In the Cloud.”

Data silo migration may appear to be a daunting task, but it is not impossible to do. The article states:

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Owl: Browser or Device Fingerprinting

Who?  Who?

Who? Who?

“Device fingerprinting, also known as browser fingerprinting, is the practice of collecting properties of PCs, smartphones, and tablets to identify and track users. For the vast majority of browsers, the combination of these properties is unique, and thus functions as a “fingerprint” that can be used to track users without relying on cookies. Researchers have discovered that 145 of the Internet’s 10,000 top Web sites use device fingerprinting to track users without their knowledge or consent. A new study by KU Leuven-iMinds researchers has uncovered that 145 of the Internet’s 10,000 top Web sites track users without their knowledge or consent. The Web sites use hidden scripts to extract a device fingerprint from users’ browsers. Device fingerprinting circumvents legal restrictions imposed on the use of cookies and ignores the Do Not Track HTTP header. The findings suggest that secret tracking is more widespread than previously thought… To detect Web sites using device fingerprinting technologies, the researchers developed a tool called FPDetective. The tool crawls and analyses Web sites for suspicious scripts. This tool will be freely available at FPDetective Web site for other researchers to use and build upon.”

More:

Web sites secretly track users without relying on cookies

 

Neal Rauhauser: Mining Data Science Central

Neal Rauhauser

Neal Rauhauser

Mining Data Science Central

I was browsing LinkedIn a little while ago and I noticed the Data Science Central group. One of my contacts had shared something from it and the charter looked interesting, so I clicked ‘join’.

Data Science Central is the industry’s online resource for big data practitioners. From Analytics to Data Integration to Visualization, the Data Science Central approach is to provide a community experience that includes a robust editorial platform, social interaction, forum-based technical support, the latest in technology, tools and trends –and industry job opportunities.

This got me a notice that I’d have to sign up for DataScienceCentral‘s website. This isn’t that unusual, I got a similar pitch from Rapid7 a few days ago, and this led to fresh installs of Nessus and Metasploit, neither of which I’d touched in several years. Once I signed up for the site it wanted me to make a profile. I used to be really resistant to this sort of thing, but this is an undeniable trend in professional networking sites.

My profile URL included my account name, NealRauhauser, and it was very straightforward. I poked around for a few minutes and I found there are 21 members per page, 559 total pages, and the nearly 12,000 professional profile URLs are embedded in these pages. I opened a shell, wrote a little script, and if my math is right by around 21:30 eastern I will have them all, but at a fetch rate that won’t cause their server to melt down.

I’ll have to parse them and then decide what to do with the resulting URLs. I could feed them to OpenCalais or Alchemy via Maltego, but 12,000 at once would swamp those Named Entity Recognition services from the perspective of Maltego’s public transform servers, and probably overrun my computer’s memory in the process.

I did a trial run with the first seven featured members …

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Thomas Devenport: Those Good at Analytics Not Good at Visualization

Thomas Davenport

Thomas Davenport

Q&A: Tom Davenport urges more clarity in data analytics

By Joe McKendrick | March 19, 2013, 4:00 AM PDT
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Businesses may be seeking to compete on analytics, but it’s often difficult for business decision-makers to get their heads around data.

I recently had the opportunity to chat with Tom Davenport, visiting professor at Harvard University and co-author of the seminal work Competing on Analytics: The New Science of Winning, about the difficulties of converting to an analytics-driven culture. Davenport, who is also co-founder and research director of the International Institute for Analytics, and a senior advisor to Deloitte Analytics, is working on a new book, dicussing on how analytics need to be better communicated to business decision-makers. He shared some of the thinking behind his forthcoming work:

Q: BI and analytics vendors have been coming out with all sorts of graphic tools — dashboards, balanced scorecards and so on — for years. Do we need more than a nice splashy presentation on the tool to communicate analytics?

TD: We’ve all grown up on pie charts and bar charts or whatever, but there are probably at least tens, if not hundreds of alternative approaches to visual analytics. Narratives are a pretty good way to convey information in the past, so maybe we should be converting our data and analysis into stories. People are starting to do that more. Most analysts were unfortunately not trained in how you communicate effectively about analytics, so we’ve got a long way to go in terms of doing a better job of that.

Q: More and more data is flowing through enterprises. Is it a challenge to get C-level executives interested in turning this data into analytics?

TD: Not for all applications. Because increasingly people are feeding data into computers and the results go into another computer, and the decisions are getting more automated. Any time you have a human involved, it’s important to try to help them extricate the meaning of the data and analysis. And there a variety of ways to do that. Historically, we haven’t been too terribly good at it, the quantitative people among us.

Read full interview.