Coverlet Meshing talks about Prism and how its predictive analytics uses technology similar Neo4j

 

Prism doesn’t scare me.
On 9/11, my office was on the 39th floor of One World Trade. I was one of the many nameless people you saw on the news running from the towers as they collapsed.

But the experience didn’t turn me into a hawk. In fact, I despise the talking heads who frame Prism as the price we pay for safety. And not just because they’re fear-mongering demagogues.

I hate them because I’m a technologist and they’re giving technology a bad name.

Let’s start with the basics.

What is Prism? If you’re the vendor that sold it to the National Security Agency, Prism is a proprietary black box that applies state-of-the-art predictive analytics to big data to infer relationships between known terrorists and their social networks. That’s marketing jargon, so let’s break it down.
Note that the only thing proprietary in that last paragraph is the vendor’s hokey sales pitch. Everything mentioned there can be built with open-source tools, specifically a scalable distributed graph such as Neo4j and some natural language processing (NLP) libraries from Stanford University. So if you’re in government IT or purchasing, don’t buy the vendor BS.
First, the graph …

In theory, every person in the world can be a node on a graph. And every communication between two people is just a relationship between those two unique nodes. So if you were able to compel Verizon and every carrier in the world to give you their complete call records, you could create the world’s largest game of Six Degrees of Kevin Bacon.
Supplement those phone records (as the thing that connects two people) with emails, instant messages, known aliases and financial transactions, and your ability to infer relationships dramatically improves.

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