My article in IBM Mobile Insights: Three mathematical innovations for mobile data analytics

Mobile Signal Reflector by Wapster.
Shared in Creative Commons license

Mobile data analytics has become the business driven for various digital experience measurements and front office strategies. As mobile becomes the next enterprise ecosystem for global economy, minutest of the mobile data and its trends influence the executive decisions and customer satisfaction. Mobile device management is also overlapping with mobile data management in more number of ways. In this situation, a data science without business value discovery cannot enable the mobile enterprise. Mathematical sciences has the capability to bridge the vaguest of the problems with the most daunting challenges of the day.

Three mathematical innovations to transform mobile data and analytics:
In this article published in IBM Mobile Business Insights blog, I am exploring the role of cognitive, advanced learning and quantum physics in building big data analytics engines that can meet the contextual and the transient nature of data problems and algorithmic challenges. These are not technical revolutions alone, rather the realizations built on the strength of mathematical constructs and clarity.Read more and join me for an exciting discussion:

How an MIT Tutorial for School kids can revolutionize cloud computing?

In search of a new topic on cloud and the various perspectives of cloud, I was going through various communities, wikis and internet for sometime. Somewhere I found the history of cloud, somewhere on the implications of private clouds in defense sector. Interesting, but not enough!

I went ahead in quest. Somewhere I found the coinage ‘Cloud plus Data’. 

Then I started hunting this keyword in Google. A few results down, I saw the link: Wiki.Scratch!: 

Wiki.scratch? This title intrigued me. Out of sheer bewilderment, I went on to read the page. The wiki page speaks about a newly developed programming language for kids and a data type called cloud data there. The surprise ended there. But the idea of ‘Cloud Data type’ persisted with me. Recently I had read about the concept of temporary social media in the MIT Review Blog. It was described as one of the top ten disruptive technologies for the year 2013. MIT Technology Review.

Temporary Social Media is a concept where content self-destructs itself to enhance the privacy of online communication and make people feel freer and be instantaneous in the world of internet. It can even be a minimal control to the current data explosion. Connecting the dots, I just began to think why can’t we think of a system of programming where cloud based variables and data types be used to create a temporary social media. Thus cloud can become the birthplace for a new mode of social media. 

Read more at my developerWorks personal blog DataVerse! and please share your comments!

Big Data is not born in a Day !

Adieu to an Algorithmic Age::

Intelligence every where! Sensors every where! Data seems to be liberated from all the corners of universe. One may wonder, where was all these exabytes and petabytes of data hidden. Or is this internet universe that spawn monsters of data from nowhere? 

Absolutely not. Big Data is not born in a day ! This rich collection of data that we see accumulating in minutest measures of seconds was nothing but encapsulated in the abstractions of an algorithmic age. 

A Critique of Classical Computational Models:

In the scientific computing community, there is an emerging realization that we are moving ahead of an algorithmic age to an age of intelligence and adaptation. We emulate more and more the natural cognition. Data structures have grown beyond the linkages and contextual affinities of algorithms. We may look back for a while. What were we doing with information spaces all these while? Data structures in various information spaces where conditioned by a logic that could approximate a mathematical behavior.

In a way , Boolean logic is largely a mathematical behavior or an operational approximation for the convenience of calculation. Mathematical behavior can be modeled by approximations. Thus we created concept machines based on Boolean logic.Mathematical behavior or for that matter any behavioral logic is just the reflection of a larger set of conditions. If we look from a higher abstraction, we were trying to fix the multidimensional data structures into a partial projection of Boolean logic. Thus my argument is that Boolean logic was insufficient to capture the computational complexities of data streams.

Data Structures and Dialectical Logic

Then what is inherent in data structures: in reality, do they exist at all? I am not an exponent enough to lay down my arguments in mathematical formats. However, I may try in terms of dialectical logic here. Data as such is a representation of a physical entity or a cognitive process. It may have its own primary structure or it may be derived or dependent on a much more relatively invariant structure.  This integral between representational mathematical format ( symbol ) and the corresponding primary or secondary structure constitute a data structure. This visualization is in terms of dialectical logic. 

In the measures of physics, we may use the metrics of space time in many models. But when the data streams are dependent or realizations of energy structures the scenario becomes more interesting. The high energy physics and the future smart sensors may become the sources of such #energy structures. As the data explosions gains momentum, we may need inner eyes to capture the innate structures behind the 'Big Data' ecosystem.

Data Diary #5: Hyper Cubes of Information spaces

When, What and Where is Content ! 

3Ws of Data Science ...

When I was working on a meta data strategy for a data governance initiative, I came across the below interesting point :  

When to locate content , 

What is content and where to locate the content.

I believe these three thoughts works behind many of the search engine driven meta data strategies. They I came across a data dilemma  why do we see metadata becoming stale and stealth? And it made me to think about information spaces, their temporal properties and how to visualize them. Are they like the conventional space time conjectures and curvatures ? Then I realized that space-time is never an absolute metric of anything. 

Let's take a few information spaces. One imminent example that comes up in our mind is a library of wealth of information. Another one can be a stock market where numbers and stocks flock with finance capital. Yet another one can be a group of people assembled in a parliament or a conference.  And a very familiar example of a convenient information space is a data warehouse or a relational database. This is largely a information space sans soul of information. 

Connected Spaces

All these are information spaces and they define their metrics of content and metadata. Often information space is just associated with the needs of data visualization. This approach will provide only limited perspective of information spaces. Each information space is having a temporal or contextual aspect embedded or evolving around it. And it cannot be simplified in some relations of data structures. If data structures should meet this criteria, they should have a time variant structure associated with them.

Am I again going back to the traditional space-time? No, rather, just highlighting the necessity to accommodate  time in this situation. Every measurement system should know what it is going to measure. If this factor is not understood well, we will always witness an uncertainty or probability or chaos in measurement and results.

So if we design an information space to visualize content and metadata that locate them (When, What, Where), we need to know that all these co-ordinates themselves are manifestations of some other information spaces. Hence Information space cannot exist in silos. It exist through #Macro Connectors. The concept of macro connectors is not mine. This is proposed by MIT Media Lab. And it looks interesting. Information space thus becomes a connected space. 

So far so good. What do we achieve by extending these connections? How will information spaces work in cognitive computing / social computing environment.  Like light getting bend by gravity, I would love to say that information spaces get truncated and twisted, curled, diverged, converged by the real-time decisions of cognitive data nodes. Information overloading is just a behavior out of million possibilities in this information - cognition conjecture. 

Google: +Gokul Alex 

Twitter: @gokulgaze

PS: Content is social, Social is Me