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Original author: 
Peter Kafka

Thanks to Quartz’s Zach Seward for jogging my memory about this oldie and goodie: Tumblr’s David Karp in a video interview taped in 2007, when he was 21, had 75,000 users and was talking about stuff like Digg, Flickr … and Twitter.

Karp’s interviewer is Howard Lindzon, who’s now known as the guy behind StockTwits. Assuming that the interview was taped close to the time it was published, it would have meant that the two men were talking as Karp was raising his first funding round of $750,000, led by Union Square Ventures and Spark Capital.

No need to say anything else:

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iPhoneDevSDK—the site apparently responsible for the hacks at Facebook, Apple, and Twitter—says it was not aware it was being used to attack visitors until it read press reports this week. In a news post (do not click if you're wary of security breaches) on Wednesday, site admins said they had no knowledge of the breach and were not contacted by any of the affected companies. Though, iPhoneDevSDK is now working with Facebook's security team in order to share information about what happened.

"We were alerted through the press, via an AllThingsD article, which cited Facebook. Prior to this article, we had no knowledge of this breach and hadn't been contacted by Facebook, any other company, or any law enforcement about the potential breach," wrote iPhoneDevSDK admin iseff.

"What we've learned is that it appears a single administrator account was compromised. The hackers used this account to modify our theme and inject JavaScript into our site. That JavaScript appears to have used a sophisticated, previously unknown exploit to hack into certain user's computers," he went on. "We're still trying to determine the exploit's exact timeline and details, but it appears as though it was ended (by the hacker) on January 30, 2013."

Read 6 remaining paragraphs | Comments

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Faced with the need to generate ever-greater insight and end-user value, some of the world’s most innovative companies — Google, Facebook, Twitter, Adobe and American Express among them — have turned to graph technologies to tackle the complexity at the heart of their data.

To understand how graphs address data complexity, we need first to understand the nature of the complexity itself. In practical terms, data gets more complex as it gets bigger, more semi-structured, and more densely connected.

We all know about big data. The volume of net new data being created each year is growing exponentially — a trend that is set to continue for the foreseeable future. But increased volume isn’t the only force we have to contend with today: On top of this staggering growth in the volume of data, we are also seeing an increase in both the amount of semi-structure and the degree of connectedness present in that data.

Semi-Structure

Semi-structured data is messy data: data that doesn’t fit into a uniform, one-size-fits-all, rigid relational schema. It is characterized by the presence of sparse tables and lots of null checking logic — all of it necessary to produce a solution that is fast enough and flexible enough to deal with the vagaries of real world data.

Increased semi-structure, then, is another force with which we have to contend, besides increased data volume. As data volumes grow, we trade insight for uniformity; the more data we gather about a group of entities, the more that data is likely to be semi-structured.

Connectedness

But insight and end-user value do not simply result from ramping up volume and variation in our data. Many of the more important questions we want to ask of our data require us to understand how things are connected. Insight depends on us understanding the relationships between entities — and often, the quality of those relationships.

Here are some examples, taken from different domains, of the kinds of important questions we ask of our data:

  • Which friends and colleagues do we have in common?
  • What’s the quickest route between two stations on the metro?
  • What do you recommend I buy based on my previous purchases?
  • Which products, services and subscriptions do I have permission to access and modify? Conversely, given this particular subscription, who can modify or cancel it?
  • What’s the most efficient means of delivering a parcel from A to B?
  • Who has been fraudulently claiming benefits?
  • Who owns all the debt? Who is most at risk of poisoning the financial markets?

To answer each of these questions, we need to understand how the entities in our domain are connected. In other words, these are graph problems.

Why are these graph problems? Because graphs are the best abstraction we have for modeling and querying connectedness. Moreover, the malleability of the graph structure makes it ideal for creating high-fidelity representations of a semi-structured domain. Traditionally relegated to the more obscure applications of computer science, graph data models are today proving to be a powerful way of modeling and interrogating a wide range of common use cases. Put simply, graphs are everywhere.

Graph Databases

Today, if you’ve got a graph data problem, you can tackle it using a graph database — an online transactional system that allows you to store, manage and query your data in the form of a graph. A graph database enables you to represent any kind of data in a highly accessible, elegant way using nodes and relationships, both of which may host properties:

  • Nodes are containers for properties, which are key-value pairs that capture an entity’s attributes. In a graph model of a domain, nodes tend to be used to represent the things in the domain. The connections between these things are expressed using relationships.
  • A relationship has a name and a direction, which together lend semantic clarity and context to the nodes connected by the relationship. Like nodes, relationships can also contain properties: Attaching one or more properties to a relationship allows us to weight that relationship, or describe its quality, or otherwise qualify its applicability for a particular query.

The key thing about such a model is that it makes relations first-class citizens of the data, rather than treating them as metadata. As real data points, they can be queried and understood in their variety, weight and quality: Important capabilities in a world of increasing connectedness.

Graph Databases in Practice

Today, the most innovative organizations are leveraging graph databases as a way to solve the challenges around their connected data. These include major names such as Google, Facebook, Twitter, Adobe and American Express. Graph databases are also being used by organizations in a range of fields including finance, education, web, ISV and telecom and data communications.

The following examples offer use case scenarios of graph databases in practice.

  • Adobe Systems currently leverages a graph database to provide social capabilities to its Creative Cloud — a new array of services to media enthusiasts and professionals. A graph offers clear advantages in capturing Adobe’s rich data model fully, while still allowing for high performance queries that range from simple reads to advanced analytics. It also enables Adobe to store large amounts of connected data across three continents, all while maintaining high query performance.
  • Europe’s No. 1 professional network, Viadeo, has integrated a graph database to store all of its users and relationships. Viadeo currently has 40 million professionals in its network and requires a solution that is easy to use and capable of handling major expansion. Upon integrating a graph model, Viadeo has accelerated its system performance by more than 200 percent.
  • Telenor Group is one of the top ten wireless Telco companies in the world, and uses a graph database to manage its customer organizational structures. The ability to model and query complex data such as customer and account structures with high performance has proven to be critical to Telenor’s ongoing success.

An access control graph. Telenor uses a similar data model to manage products and subscriptions.

An access control graph. Telenor uses a similar data model to manage products and subscriptions.

  • Deutsche Telekom leverages a graph database for its highly scalable social soccer fan website attracting tens of thousands of visitors during each soccer match, where it provides painless data modeling, seamless data model extendibility, and high performance and reliability.
  • Squidoo is the popular social publishing platform where users share their passions. They recently created a product called Postcards, which are single-page, beautifully designed recommendations of books, movies, music albums, quotes and other products and media types. A graph database ensures that users have an awesome experience as it provides a primary data store for the Postcards taxonomy and the recommendation engine for what people should be doing next.

Such examples prove the pervasiveness of connections within data and the power of a graph model to optimally map relationships. A graph database allows you to further query and analyze such connections to provide greater insight and end-user value. In short, graphs are poised to deliver true competitive advantage by offering deeper perspective into data as well as a new framework to power today’s revolutionary applications.

A New Way of Thinking

Graphs are a new way of thinking for explicitly modeling the factors that make today’s big data so complex: Semi-structure and connectedness. As more and more organizations recognize the value of modeling data with a graph, they are turning to the use of graph databases to extend this powerful modeling capability to the storage and querying of complex, densely connected structures. The result is the opening up of new opportunities for generating critical insight and end-user value, which can make all the difference in keeping up with today’s competitive business environment.

Emil is the founder of the Neo4j open source graph database project, which is the most widely deployed graph database in the world. As a life-long compulsive programmer who started his first free software project in 1994, Emil has with horror witnessed his recent degradation into a VC-backed powerpoint engineer. As the CEO of Neo4j’s commercial sponsor Neo Technology, Emil is now mainly focused on spreading the word about the powers of graphs and preaching the demise of tabular solutions everywhere. Emil presents regularly at conferences such as JAOO, JavaOne, QCon and OSCON.

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In "Credibility ranking of tweets during high impact events," a paper published in the ACM's Proceedings of the 1st Workshop on Privacy and Security in Online Social Media , two Indraprastha Institute of Information Technology researchers describe the outcome of a machine-learning experiment that was asked to discover factors correlated with reliability in tweets during disasters and emergencies:

The number of unique characters present in tweet was positively correlated to credibility, this may be due to the fact
that tweets with hashtags, @mentions and URLs contain
more unique characters. Such tweets are also more informative and linked, and hence credible. Presence of swear words
in tweets indicates that it contains the opinion / reaction of
the user and would have less chances of providing informa-
tion about the event. Tweets that contain information or
are reporting facts about the event, are impersonal in nature, as a result we get a negative correlation of presence of
pronouns in credible tweets. Low number of happy emoticons [:-), :)] and high number of sad emoticons [:-(, :(] act
as strong predictors of credibility. Some of the other important features (p-value < 0.01) were inclusion of a URL in
the tweet, number of followers of the user who tweeted and
presence of negative emotion words. Inclusion of URL in a
tweet showed a strong positive correlation with credibility,
as most URLs refer to pictures, videos, resources related to
the event or news articles about the event.

Of course, this is all non-adversarial: no one is trying to trick a filter into mis-assessing a false account as a true one. It's easy to imagine an adversarial tweet-generator that suggests rewrites to deliberately misleading tweets to make them more credible to a filter designed on these lines. This is actually the substance of one of the cleverest science fiction subplots I've read: in Peter Watt's Behemoth, in which a self-modifying computer virus randomly hits on the strategy of impersonating communications from patient zero in a world-killing pandemic, because all the filters allow these through. It's a premise that's never stopped haunting me: the co-evolution of a human virus and a computer virus.

Credibility Ranking of Tweets during High Impact Events [PDF]

(via /.)

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datamining

Twitter users are about to become major marketing fodder, as two research companies get set to release information to clients who will pay for the privilege of mining the data. Boulder, Colorado-based Gnip Inc and DataSift Inc, based in the U.K. and San Francisco, are licensed by Twitter to analyze archived tweets and basic information about users, like geographic location. DataSift announced this week that it will release Twitter data in packages that will encompass the last two years of activity for its customers to mine, while Gnip can go back only 30 days. Twitter opted not to comment on the sale and deferred questions to DataSift. In 2010, Twitter agreed to share all of its tweets with the U.S. Library of Congress. Details of how that information will be shared publicly are still in development, but there are some stated restrictions, including a six-month delay and a prohibition against using the information for commercial purposes.

http://www.rawstory.com/rs/2012/03/01/twitter-is-selling-your-data

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Click here to read This Pixel-Perfect Chiptune Rendition of Lana Del Rey's &quot;Video Games&quot; Surpasses the Original in Every Way

Soulful songstress Lana Del Rey made a splash last year with her single "Video Games", a song that repeated the term 'video games' several times, causing many gamers to fall instantly in love despite the games in question being used as an example of how routine her once passionate relationship had become. The video was lovely, but it lacked a certain something: Actual video games. More »

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After posting my Screensaver Culture presentation yesterday it was blogged on Creative Applications by Greg Smith and I’ve gotten quite a few responses on Twitter. Some of the comments are on point and some are just funny.

Below is a more or less complete list. In summary, the arguments are roughly as follows:

  • “Screensavers are outdated / unnecessary.” Well, yes. But that has never meant much in terms of deciding whether a cultural phenomenom succeeds or is banished to the Wasteland of Forgotten Memes. Tamagotchis or animated GIFs, anyone? 90% of all iPhone / Android apps are unnecessary for everyday living, yet the smartphone app culture is a runaway train.
  • “Developing screensavers is currently way too hard.” I share this sentiment and suspect it to the main culprit along with its corollary: “Installing screensavers is too hard / scary / likely to mess with the rest of my computer.”
  • “It’s impossible to improve on flying toasters.” This terrifying thought is exactly why I would suggest screensavers need revisiting.

In conclusion: Between being tricky to develop and just as tricky to install and successfully use, screensavers stand no chance of recovering ground as a cultural phenomenom. Despite their close link to the app culture that is currently dominating our lives, screensavers (aka “ambient software”) will get no love.

This might not seem like such a terrible loss, but I still posit that ambient data gadgets with possible integration to web / mobile apps would’ve been a great usage scenario. There are some ways this could still happen:

  • Microsoft and Apple realize the lost potential and relaunch their screensaver frameworks complete with app stores for screensavers. (Unlikely.)
  • Google develops a screensaver mode for Chrome as part of their Chrome apps initiative and allows sales of screensavers through the Chrome app store. (Entirely possible if a little optimistic. My favorite option by far, though. Google, are you listening?)
  • In both these scenarios, new screensavers would be based on HTML5 with WebGL, allowing them to be cross-platform and based on open standards. Because you all understand that proprietary is stupid, right?

A sad footnote: I had to uninstall the brilliant Briblo screensaver after realizing it was interfering with the taskbar on Windows 7. So I’m back to the ever popular blank screen, like so much of the world population.

The Tweets

@mariuswatz Screensavers tie us to nuclear power!

— Dragan Espenschied (@despens) January 16, 2012

@mariuswatz I thought screensavers were obsolete. I guess that makes them a good platform for art.

— Jesse L Rosenberg (@nervous_jesse) January 16, 2012

@mariuswatz computers are no longer unused long enough to trigger the screen saver

— noisia (@noisia) January 16, 2012

@mariuswatz who has screensavers these days? LCD screens just go to black on powersave.

— Danny Birchall (@dannybirchall) January 16, 2012

@mariuswatz I’m not sure it’s possible to improve on flying toasters…

— Rob Myers (@robmyers) January 16, 2012

@mariuswatz would love to make screensavers. But they are really hard to create on the Mac using Cinder or openFrameworks.

— Jan Vantomme (@vormplus) January 16, 2012

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“Gentlemen, we can rebuild it. We have the technology. We can make Twitter better than it was before. Better, stronger, faster.”

That’s the speech I imagine Niall Kennedy giving himself recently when he decided to rewrite Twitter’s front end using web best practices. The result is a read only Twitter that’s a little less pretty, but a whole lot more streamlined.

To start, Kennedy converted Twitter’s table layout to XHTML/CSS-based design. He also split the page load so that all those little avatar graphics are loaded asynchronously, which makes pages appear faster.

One of the larger undertakings was localizing (or is it localising?) the site. Kennedy had to choose common wording throughout the site and ensure nothing that would ever need translating was hard-coded.

Kennedy claims a 41% decrease in bandwidth and a much faster DOM footprint. It’s a geeky way of saying that TwitterFE is an improved Twitter.

Of course, the front end has never been Twitter’s biggest problem. It’s the back-end, with thousands of messages a second, that prompts the Fail Whale. In all, TwitterFE reminds of redesigning Craigslist. It seems like a great idea, but it ends up being a solution searching for a problem.

But as a case study, TwitterFE is extremely useful. Perhaps Kennedy will release his source code, which runs on Google App Engine, and we can all learn from his experience.

See also:

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