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Collaborating Authors

Big Data, IoT, Wearables: A Connected World with Intelligence

@machinelearnbot

At the CES 2015, I was fascinated by all sorts of possible applications of IoT – socks with sensors, mattresses with sensors, smart watches, smart everything – it seems like a scene in sci-fi movies has just come true. People are eager to learn more about what's happening around them and now they can. While I was at there I attended a talk given by David Pogue – he is awesome. He pointed out that the prevalence of smartphone is the key to the realization of the phenomenon called "Quantified Self." Smart phones play a vital role as a hub where all our personal data converge and present, seamlessly.


The Dark Side of Big Data

@machinelearnbot

Ashley Madison, IRS, Target, Sony…What do they have in common? Here we only name a few but of the most tremendous crisis of data breach in recent years - yes, it is happening and it is happening everywhere. The cost of data breach comes to a new high at 154 per record of stolen or leaked data, adding up to millions of data for each incident, including the law suits, losing customers and intangible assets of the companies. Big data, indeed, is turning the most valuable asset nowadays and that of course becomes a target of hackers and insider criminals. Among all the companies that suffer from data loss, financial services and healthcare are the most at stake with no doubt, for their data is the easiest to monetize.


Making MySQL 5,888 times faster

@machinelearnbot

When Jeff our architect first ran the benchmark I could not believe it! I was sitting in front of the terminal screen trying to take in what I had just seen. "Jeff is this correct?!" I asked. I had patiently waited 588 seconds (close to 10 minutes) for MySQL to execute a query and then watched as BigObject cranked out the same query in 0.1 seconds! That's a 5,888x boost in performance!


Who are alike? Use BigObject feature vector to find similarities

@machinelearnbot

Cluster Analysis is a common technique to group a set of objects in the way that the objects in the same group share certain attributes. It's commonly used in marketing and sales planning to define market segmentations. Here at BigObject we adopt a simple approach to exploring the similarities between objects. We simply calculate the "Feature Vector" based on given attributes and use the score to determine which objects are "alike." This is a simple example to show how to use BigObject to extract product features and then find similar products in your retail data.


Giving away 1,000 copies of Big Data In Action.

@machinelearnbot

Titus Blair is a family man, full-time world traveler, futurist, entrepreneur and Director of User Engagement at BigObject. As an industry leader in big data, he has the inside track on how companies are using big data in the real world and he has compiled a collection of those use cases in "Big Data In Action". Titus hopes that you will learn a ton from these real examples and do something truly amazing in the world of big data!