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:

Transparency and Measurability :: The twin towers of Digital Economy

People and Technology: How they enrich a social network?

#Facebook, as the name goes, became this ubiquitous a social media only with a human face and a treasure chest of information. These two constitute the twin towers to build a powerful social media. These two ends should enrich each other in a social media platform. More people means more information, more information means more attractive to people. 

Without people, read & write web ( web 2.0 paradigm ) will just be a pipe dream. Social Media as a source of data opens up anew opportunities for collaboration, new insights, further more modes of communication and so on.

Yet making relevant decisions from social media remains a daunting task for many information management systems. We evolve complex algorithms and decision systems for this. We traverse the path of natural language and cognitive networks for this aim. Why?

The data formats and the metadata about people varies significantly across social media. This diversity often inspires the evolution of various ecosystems around each social networks. When #Pinterest gains momentum, the network society around it various substantially with #Twitter communes or #Google Plus hangouts.

Social Media Observatory: A step towards Digital Front Office

Beyond this multiplicity of modes of communication, a unified strategy for web intelligence from social media has a lot of challenges even today. The data exchange across social media need to be more transparent and measurable for this. Social Media Observatory concept is a landmark concept in this direction. An observatory is a platform which can access and plot the patterns and constellation of ideas and aspirations in social media.

This transformation is an imperative in making social media a viable channel for Smarter commerce, further leading to a comprehensive digital front office. This envisions the need for a front office digitization  (#FOD) strategy incorporating social media evolution as well. That will take us near the goal for a fully digitized globally integrated enterprise. A Digital enterprise cannot exist without a digitally segmented market and a digitized channel for network society. And that means the synergy of social media, big data, cloud and mobile platforms as a prelude and a stage setter. 

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.

Future Engineering : An Electronics Reader

Future Engines for Innovative Electronics Landscape

Big Data and the future of Epidemiology

People are always the source of any data systems. And hence mobile phones cannot be ignored while thinking about moving from information into insights. MIT in the Technology Review online magazine speaks about different use cases of deriving insights from the mobile phone usage. Researchers in MIT are dreaming that the data mining strategies in this direction can even be the future of epidemiology itself. On the research on genetics of malaria parasite based on Africa, they have studied the cell phone call data. It can be helpful to understand phenomena like ethnic divisions in Nairobi slums and the spread of cholera in Rwanda.

HetNet – Network Mix for tomorrow

Portable devices and programming languages are the backbone of the connected devices. Now comes portable telecommunication networks, thanks to HetNet. Mobile experts define the HetNet as a network with complex interoperation between macro cell, small cell, and in some cases WiFi network elements used together to provide a mosaic of coverage, with handoff capability between network elements. A Wide Area Network can use macro cells, Pico cells, and/or femtocells in order to offer wireless coverage in an environment with a wide variety of wireless coverage zones, ranging from an open outdoor environment to office buildings, homes, and underground areas. In short HetNets can offer better cell associations and load balancing. On a product end, HetNets can enhance the capabilities of FaceTime, VideoSkype and related streaming applications.

Codassium- Coding in Online Streams

Web RTC Technology has stirred up an excitement in the online collaboration space. It enables audio/video streaming and data sharing between browser clients (peers). Codassium is a live tool that works on WebRTC. As a set of standards, WebRTC provides any browser with the ability to share application data and perform teleconferencing peer to peer, without the need to install plug-ins or third-party software. WebRTC components are accessed with JavaScript APIs. Currently in development are the Network Stream API, which represent an audio or video data stream, and the PeerConnection API, which allows two or more users to communicate browser-to-browser. 

Mozilla Servo – Browsing in Parallel Computers

Mozilla dreams of future versions of Firefox to be able to “take advantage of tomorrow’s faster, multi-core, heterogeneous computing architectures,” writes Mozilla CTO Brendan Eich on the company’s blog. To make that happen Mozilla is developing a new browser engine dubbed Servo. Servo is not an extension of Gecko, Firefox’s current rendering engine, but an entirely new beast written specifically to take advantage of modern, massively parallel processing hardware. Mozilla Servo is written in Mozilla’s homegrown Rust programming language, a C++ style language that attempts to provide more security by avoiding memory corruption and buffer overflows, a common attack vector in today’s browsers. 

World’s Very first Website – Comes Back

Twenty years ago on April 30th, CERN Research Lab in Europe, published a statement that made the World Wide Web freely available to everyone. To celebrate that moment in history, CERN is bringing the very first website back to life at its original URL. We can see the very first webpage that Tim Berners-Lee and the WWW team ever put online, point your browser to http://info.cern.ch/hypertext/WWW/TheProject.html.

PS: Content is Social. Social is Me.