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!

Semantic Web and Social Web: Similarities and Differences !

Though semantic web and relational ontology has become a theoretical discussion among many pioneers of world wide web, it is yet to receive wider traction among the industrial use cases of internet. Yet the concept remains widely discussed in the academic circles and the research groups of enterprises focusing on online data distribution and search engines. However, semantic web as a paradigm has some differences from a social web where we can collaborate better and more meaningful.

Semantic web need to go way beyond the constructs and ambitions of autonomic computing if it should become a gateway to web socialization. Internet  should evolve to be more open web where we can create meanings and relationships within first degree of separation itself. I believe it is still very much skewed by the way search engines, resource identifiers, memory allocation architectures, data storage, binary approximation algorithms etc.

Social web is a world where society weaves a web of information around their social relationships and social use cases. It stems from the real web of information. Semantic web will be a subset of social web in this sense. If semantic web is dealing with information enterprise, social web is dealing with collaboration circles.Machines can become self aware and understand what is the cognizance of information that they process. But in social web, socialization will be the first phase and then semantic relationships will emerge. web sociology will depend on the the social coefficient that exist in various degrees of separation and the order of 'connectedness' in each collaboration circles.

The business dynamics of web sociology will depend on the market dynamics of software engineering economy as well. We cannot think of semantic or social web isolating it from the market ecosystem or software economic premises. In terms of social activities, what all are happening in web?

• Collaboration
• Conversation
• Construction
• Deconstruction
• Inspired connectivity
• Virtual sensitivity
• Cognitive mind mapping
• Symbolic learning
• Forgetting and temporary memory
• Recognition
• De-cognition
• Identification
• Crowd behavior
• Media affinity
• Informed passivity
• Social solitude
• Virtual sentiments 

This list goes on. Web sociology will need to come up with new frameworks where all these activities need to be aligned in the sociological perspective. Thus semantic web can generated meaningful association between machines and men. Semantic web can definitely write and read relationships with human beings. Socialization is something beyond communication. It is always an attempt to represent the social self and personal self to a wider audience. Only social web can aggregate and spread more meaningfulness, relationships and cognition in the collaboration circles. 

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.

Some Early thoughts on the Boundaries of Circuit Computing

Computational machines, their digital senses are hovering are all around us. We make decisions, we experience and negotiate, we travel and triangulate through their prowess. In other words, we as a collective of social knowledge embed intelligence and knowledge into their circuitry and digital logic. Their arises my question; how is this digital logic grounded in the larger set of mathematical logic. Is this digital logic a linear crystallization of natural computing algorithms? 

As we reach the boundaries of traditional algorithms of natural data set, the emerging frontiers of decision sciences are appearing as the phantom ghosts from nowhere. And at times we think they are coming from nowhere in the past and we call them data explosion and sensor revolution. Where has internet hidden all these Phantoms from the past.

Recently I read that mathematical logic has a lot of formalism derived from the brilliant approaches of the 20th century mathematician Hilbert, who tried to consolidate many of the mathematical problems under the ambit of a unified theory of mathematical theory. Thus we need to understand the formalism and whether their were some limitations on the approaches in the problem solving approaches of Hilbert methodology. 

Why all this question now? It is because of the very reason that the theoretical computational models need to take a new turn as we are seeing cognitive computing as the future approach to the decision making algorithms of future needs. When we shift the gears from circuit computing to cognitive computing, there must be a realization of the underlying mathematical logic and its complexity inherent. Our investigations must begin at the very root of the mathematical logic which derived its powers from the formalist approach. 

+Gokul Alex 

Unwind the Mind : Data Dynamics of Natural Computers

Data Diary (5): Natural Computers

Information space continues to enrich my imagination to new manifolds. The recent news that a new start up named Aysadi has come up with a Topological approach to machine learning and Big Data analytics, is really really an interesting conjecture. It is said that DARPA, NSF and Stanford were involved in this research for a long time. I hope this will be a good trend where we will have an open approach to data science. This should be beyond the dependency on specific tools. 

This inspired me to go through some nuances of topological learning and created lots and lots of questions in my 'unstructured' mathematical understanding. The stress on various 'in variance' conditions in topological analysis makes me believe that we are far from the best approach. A comprehensive approach to a data problem should not be defining a boundary to its explorations and insights. Yet my comment remains largely naive as I am not an authority or trained in topology. 

Continuing from our previous post on information - cognition   conjecture, I have landed on a cyclical condition. With the advent and advance of cognitive computing and neuroscience, we are creating anew computing machines driven by human cognition. So we can state that cognition can control computation and therefore information too. On the other side of the coin, can information control cognition. In simple terms the answer is yes, a plain yes. If so, can we create a cyclical information - cognition cyclical machine ? This should be a machine where cognition initiates information processing and then information processing generates new re-cognition. 

When I try to rationalize this order, I believe this is happening in all our day to day lively transactions. Going on the same lines, how many machines can claim to do this natural computing cycle to maximum approximation to the real world. And what is the most effective model to observe the data flow in this cognition - information - re-cognition cycle. Knowledge ( Neural Signals, Thought Processes ) in ( Cognition ) - (Language, Semantics, Syntax) in ( Information ) - ( Semiotics, Visuals, Shapes, Numbers, Senses, Emotions ) in Re-cognition seems to be data dynamics. Natural computing demands more rigorous modelling for data dynamics. In pursuit of more natural thoughts ... 

+Gokul Alex 

PS: Content is Social. Social is Me

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