My Reading Highlights 10/7 to 10/14

It has been an interesting week for reading about tech.  Below are some of the highlights that I came across.  One thing to note is over at ReadWriteWeb, founder Richard MacManus is leaving the company.  This site has been a long time favorite of mine and his presence will be certainly missed.  I certainly hope the new owners, SAY Media, will keep up the quality of the reporting on tech news and analysis.

ReadWriteWeb Death Watch:  In-House Datacenters
This was an interesting read in that I find myself managing three datacenters in my business.  All are a resource sync but most of my clients are not ready to move to a public cloud environment.  That said we are certainly heading that way.

Google's Neural Networks Advanced Artificial Intelligence

Not long ago we heard that Google had developed a large scale neural network to identify Cats in YouTube videos.  They are now turning that to enhance their voice search options on mobile devices (Android).  

Why the trick to analyzing Twitter data is more data

Derrick Harris provides an interesting look at the debate on whether Twitter data provides some real value for decision analytics.  In short yes but it needs to be combined with other data to make it more accurate and reliable.  Good read.  Especially the points on Twitter being ineffective at predicting elections.

It pays to know you:  Interest graph master Gravity get $10.6M

This is an interesting concept.  The ability to profile people and what they read and then tailor content delivery based on graphing a person interest.  This notion also feeds into the basic concepts that are popping up all over the place for digital personal assistants or tailored search results that are happening now with Android and Siri.  We are certainly looking at the start of a big tech push in this area.

Tech That Protects The President, Part 1: Data Mining

I'm looking forward to reading this 3 part series.  I few years back I had the opportunity to work on a new data system for the White House.  It was pretty interesting to see how politics and information collided   As an example everything that goes into the WH is logged and eventually put into records.  Imagine what that means when you are dealing with big data environments and the situation room.  So back on subject, it will be interesting to see how this series evolves.  The first part, Data Mining, really focuses on a tech called uReveal where it is used to sift through news articles, blogs, forums, etc to discover concepts that are troubling and alerts the user.  One interesting note here is the lack of discussion on privacy and how the Secret Service is handling that given all the inquires from Congress to DHS who are doing the same action.

Few enterprises are ready for the app economy's data explosion

This was an interesting read from GigaOm that discusses some of the challenges that are present on performing better analytics when considering the ever growing consumer use of APIs and apps in the digital economy.  Anant Jhigran lays out three new sources that are needed to get the complete view of our customers and business.

Oracle Team USA Capsize

This is my video of the week from San Francisco.  It combines two things interesting things that I follow:  Oracle and Sailing.  Here Oracle capsizes.  I'm sure I'll make a PowerPoint out of that picture someday.

Putting 1 Trillion in Context

In reading a recent article about Amazon's S3 Cloud service I recalled a conversation with Mickey McManus up at Maya.  We were discussing Big Data last year and the ideas we had about working with ever increases volumes of data.

The Read Write Web article's title I just read was Amazon S3 Showing Signs of Slowing as It Approaches 1 Trillion Objects.  This got me thinking about the conversation with Mickey.  With the US deficit always being discussed in the Trillions these days it is easy not to understand the magnitude of something measured in the trillions.  Here is just something to consider with discussing Trillions of anything:

If you take something that we all deal with, the unit measure of Time, and put that into context I think you will get the picture.  So starting with the basics, 60 seconds = 1 minute; we all know that and it isn't that much time.  So let's scale that up.  What is the amount of time in:

• 1 Million Seconds = 11.57 days
• 1 Billion Seconds = 31.7 years
• 1 Trillion Seconds = 41709 years

41K years, that is a long time ago and allows us have a better understanding what a trillion really means when we start thinking about what the Amazon S3 service is approaching.

The challenge here is the rate at which we are approaching the Trillion object mark.  It is happening very rapidly and almost occurring over the last 2 decades of data, information, and services that are exploding through the Internet.  The question we should start asking ourselves now is how do we handle way beyond the Trillions of anything on the Internet. When a trillion objects are common place.

Today we are all using technologies such as Hadoop, or variants of Hadoop and building massive data centers that deal with complex challenges in cooling and power management and efficiencies.  These are examples of our basic building blocks today.  Evolution in this area will only take us so far.    We as an information society must starting thinking about transformational ideas and completely different technologies if we hope to harness the not so different horizon of 2030.