What ‘light loving moths’ forgot to tell you about ‘experience design’

‘Light loving moths’ and the world of design?, why this weird comparison! Yes, you may be thinking for a while. As we know there is a common belief that moths, flies, beetles are attracted towards light and that leads them to their unfortunate miseries at the candle light. When we understand that it is by their sheer inability to understand the circular light emitting behavior of the artificial light, these little insects fall prey to the candle fire and other hazards caused by artificial sources of light. Those insects are actually in search of the moonlight that has a constant angle of radiation. 

This occurrence in nature often reminds me about the hypes of nurturing and manufacturing customer experience design. Primarily design has two layers; one physical and second ideological. When physical layer of design act as the necessary act to optimize the functional and material aspects of the subject matter or the object manifestation, ideological layer act as the force that can create a demand or customer appeal beyond its actual capacities. Hence when we make all sort of fuzz about design, design thinking, truth in design we should understand the layer in which we are applying our design efforts and logic.

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:

Do you have a digital consciousness in your enterprise?

Can we dream of an enterprise that is self aware, self organized, self prioritized and yet running on certain well defined economic and social principles? In today's world, such an enterprise must be digitally conscious all the time! 

With the emergence of semiconductor electronics, telecommunication networks and internet, technology has become the key game changer for market economy and social business. Technology has started influencing governmental and non-governmental programs in a big way. However, it was short of a comprehensive organizational transformation for long time. 

As reported in Ontology.com about data integration challenges, 99% of the businesses undertake integration projects every year. Over half of those integration projects are obsolete before completion. Again, 86% of the integration projects experience delays. Thus 25% of the IT budgets are spend on integrations. Thus the enterprise integration projects posit a huge opportunity for a system wide transformation and conscious effort.

The key steps to resolve data integration challenges were the pilot attempts at building an enterprise transformation capability within technology. WebServices, Service Orienter Architecture, Business Process Management tools, Machine to Machine communication and so on has enriched the middleware platforms with enterprise integration capabilities. Business intelligence and analytics engines were the next step in this evolution. They have added spice to the otherwise passive data stores and data marts. When I write this post today, even implementing mainframe analytics is not a daunting task for an enterprise. 

As the tools became strong enough for integration, we wanted our communication protocols and data formats to be light weight. XML has been a good document format to begin this journey. JavaScript Object Notation (JSON) has made the communication between web services and repositories further more light weight. REpresentational State Transfer (REST) architecture has been the other communication model that helped the distributed communication easier to model and abstract. Machine to machine communication protocols like XMPP, MQTT etc have helped the communication more seamless and automated. QR Codes and RFID technologies have also been highly beneficial for easier deployment of network data services and supply chain optimization. The list of innovations is not just countable. 

World information society and networked economy are no more isolated. Consumer electronics and social media networks has connected them in close knits. Thus world of business and social engagements have become digital. In this scenario, a globally integrated enterprise is possible only through a digital front office. 

Now integration is no longer tool driven. Customer experience and expectation determines the reach of technology. Down the lane, society has become more techno-centric and personalized. People and community groups began actively participating in the market dynamics. It is a kind of social business which offers 360 degree view of business processes and market dynamics. Social networks and social media centric business transactions reflected the customer sentiments in finer details. Hence customer experience and usability of products and services determined the longevity of brands in a big way. Thus information and analytics have become key components in the Enterprise architecture. Further more, when the world of communication is increasingly become digital, future enterprises need a 'digital edge' at affordable cost. 

Thus as the world marches to new terrains of digital experience and business opportunities, digital front office is a key to integrated enterprises and agile transformations to operations. The world around us demand an equal visibility of people and their data, to the say the least! We want easier access and transparency to people and their information. Thus in other words, digital eye is the key to agile enterprise. 

PS: The views are personal and does not represent any organization or enterprise.

Patent Regime and the abuses : Challenging times for creativity!

I am writing this post out of sheer fun and amazement at the coincidence of ideas and real life. Early morning today, before having my first cup of tea, I ventured to write the below blog post in Fizzog about the need for a systematic inclusion of societal thinking in the invention publishing process and Intellectual Property (IP) law. Though not on the exact lines, I could see an Economist article on the patent reform in America. This new law is mainly related to the patents pertaining to abstract inventories. 

The new Innovation law drafted by US policy makers aspire to achieve the below as per the mentioned article:

By shifting the costs of litigation on to the loser and forcing patent holder to disclose what the infringement is, the Innovation Act hopes to change this. There is probably no perfect equilibrium that protects inventors without stifling new inventions. At the moment the scales are weighted in one direction. The new act would add some balance.

This is just a scraping on the surface of the issues. The patent filing and publishing system has been maligned not only by legal issues, but by the undue greed and haste to overtake the competitor or to sabotage a fair competition.  It may be a better idea to involve the competing parties to the discussion table before awarding the patent. It may be also a good idea to think of shared patents when there is a substantial overlap in the areas of application and the invention inventories.

My article on IBM Mobile Business Insights Blog: Mobile Sense and the customer experience

Mobile applications are known for its first impression bias. As the Instagram founder says customer decides to download an app all within 30 seconds. Customers are very much task oriented in dealing with mobile apps and with their mobile usages. However 91% of the mobile users keep mobile with them 100% of the time. Hence mobile applications are at time a lucrative business opportunity and an ever challenging imbroglio. Please read more about this high profile business and technical challenge in the IBM Mobile Business Insights blog article : 

Join me in conversation in the blog and twitter @gokulgaze. Please share your likes in Facebook if you find this article informative!

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. 

How IBM Watson helped me select the right mobile apps: A science fiction

How IBM Watson helped me select the right mobile apps

This is a fictional narrative on cognitive mobiles. With Supercomputers like +IBM Watson, cognitive computing has become an immediate reality. Can these cognitive computers solve our real life problems and confused mind? This article presents a dream where a man's misery with mobile app is resolved with the help of a cognitive computer! Please read more and share your thoughts at the IBM Mobile Business Insights blog! ...

What will you say about Mobile Enterprise in 10 seconds !

In the recently concluded #IBM Redbooks Social Media Residency for Mobile Enterprise thought leadership, we had great fun taking video shoot on the idea exchange on #MobileFirst. When I was invite to speak a few words, thoughts from all corner gazed at me. I strongly believe mobile is the best social computing platform which can be the intelligent interface between simple humans and complex machines. Thus humans, mobiles and complex machines like #IBM Watson together can build a smarter planet. More importantly, we should know why are we building it. It is for a connected living in a network society.

Disclaimer: The Opinions are my own.

Digital Front Office: The Enterprise Social Bus for Digital Economy !

Watch your thoughts; they become words.

Watch your words; they become actions.

Watch your actions; they become habits

Watch your habits; they become character.

Watch your character; it becomes your destiny.’

(From the famous quote of Lao-Tze)

If we have a time machine that can watch every customer 24× 7 like the above words, it will be one of the most resourceful devices for the social business and digital consumer! How does Customer Experience Lab realize Digital Front Office innovations? What is digital front office for you: A social business hub or customer experience laboratory? … Either ways, you are right! Digital front office is a multi dimensional platform for your business needs. 

We strive to analyze, predict, and transform customer experience through a deeper understanding of customer’s thoughts, words and action. ‘Digital front office’ is the convergence of all the techno social efforts in this perspective. The synergy of social, mobile, cloud and big data innovations is realizing this perennial dream of world markets and business leaders a real social business story. The ubiquitous smart phones, spontaneity of analytics, pervasive social media and the cost effective cloud solutions have made today’s consumer truly digital. Thus the new age digital consumer expects a digital front office in the transactions and experiences. 

Realizing a digital front office requires the right mix of business and technical innovations to the life of customer. At the IBM Customer Experience lab, researches and business innovators work in tandem to envision the world class customer experiences. Located at the T.J. Watson Research Center, with additional virtual collaborators around the world, the IBM Customer Experience Lab is a partnership between IBM Research and IBM Global Business Services. 

When digital front office is a significant business value accelerator (BVA) and primary innovation hub for better digital consumer experience, at the same time it is a virtual window to the world of social business. Social Business thrives on the mobilization of social capital and new social identities. Thus a digital front office can watch the real time trends in social fragments and social identities through various modes of analytics. Innovations like ‘transformative analytics’ are progressing in this trajectory. From media and entertainment industry to micro finance institutions, we can see vivid and variant models of social business. 

A digital front office strategy with an evolutionary and adaptive approach to customer experience analytics(CEA) can realize social business platforms with global market reach and solid growth potentials. This is where IBM Customer Experience Lab meets next generation market dynamics.

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.

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 

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.

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

Data Diary #4

Information Machines and Metadata Strategy: Some early thoughts

How much of engineering is required for designing / evolving +information machines so that they can always differentiate between data and +metadata and further go beyond to create knowledge and insights out of it. While thinking of this question. I cam across the role played by Search Engines in this. No doubt, search engines are information machines. They learn the #semantic graphs of information relationships in a sea of indexes. Let me bring some metrics here. How will we measure the effectiveness of search engines as information machines. Do we need some axis to plot / visualize their effectiveness.

One of my favorite questions will be how much of data / information / relationships / indexes / semantic webs can be converted to working knowledge by Search engines? Rather can they do this task at all without the intervention of human interactions, at all ! 

Going by the same lines, my categorization / differential positioning of +metadata with respect to data will not be merely based on the relationship between the meaning and associative positioning of data nodes. It will be rather based on how one node of data connects the other node of data to a third node of data. So data and +metadata should always have more than one connecting dot between them. Thus when we use a search engine to find all the +metadata about a particular data node, it should find the meta data based on this semantic graph. 

+Gokul Alex 

@gokulgaze, PS: Views are my own and do not subscribe to any organization or institution

Data Diary #3:

Network Equipment Providers's (NEP) and Next Generation Content

We have already witnessed the wonders of streaming media and video streams. Now comes the real time video streams. At the recently concluded Mobile World Congress, Peter Linder,VP, Fixed Broadband and Convergence, Ericsson speaks about how much Ericsson is enthusiastic about the possibility of live and multimedia content in mega sporting events such as FIFA World Cup, Olympics and so on.

Ericsson Speaks on Next Generation Content

This speech is a pointer to the direction in which Network Equipment Providers must be moving. This is nothing but out of the realization that Voice communication networks can no longer be the only revenue generator or business value accelerator. Voice -Data convergence, Content explosion and the entrance of traditional NEP's in this ecosystem opens up interesting possibilities of competition in the content aggregation and content analytics space.

Already we have a lot of players in the content aggregation and content analytics space. How can NEP's leverage their experience and infrastructure to create new opportunities and user experience ? And that is something worth study.

+Gokul Alex 

Data Diary #2

Data Visuals : Dreams unlimited!!

Visuals are there everywhere, from primitive Ajanata, Ellora caves to the huge Bulletin board of the metropolis. As some cultural theorists say, we live in the world of images. And it is un-disputable that images speak fancier than words, if not louder. And as days progress, data is becoming more and more ubiquitous and visible in all the quadrants of life. It is not that all these data has been born into our networks all of a sudden. Instead, we are becoming more and more conscious about the possibility of sharing and transforming the data. 

Hence these two universes are colliding and co-creating a new paradigm called data visualization. By the time you analyse the thoughts, you can visit the below page to know the mushrooming tools in the data visualization space. This shows the demand in this space. Periodic Table of Data Visualization Tools

Data visualization is a field with exciting features and powers in Web Intelligence. Web Intelligence is slowly transforming the Business Intelligence space all together. Data Visualization as a strategy stands very much coherent to IBM's thought of simplifying life. Gone are the days when we need the expertise of complex queries to mine the data from huge data stores. Now the imperative is to leave the complexities to a selected few data scientists and to empower the rest of the world to focus on the beauty of the data. By the time I finish this introduction on data visualization, at least some of us may be started exploring or experiencing in our daily work or social media life. 

In Public internet we come to see its capabilities through tools such as Klout, Cloze, Vizify, Orange etc.Thus Social Business is no longer confined to the interfaces of text analytics and streaming hash tags. It has become more intuitive and info-graphic in nature. Image analytics may be a tough ask for the present computing circuits. But any day, we can see it happen. Visualizaton need not be confined to data alone as meta data can also be visualized. At then end of the day, only your imagination is your boundary!

Twitter: @gokulgaze

Note: All the views are purely personal ...

Data Diary #1

Monsters in Memory !

Can you imagine idle java processes eating away 70% of your Operating system RAM share. In our Content management system built on a propriety product suite, we were taken by surprise to realize that it is a baggage from the product architecture. However, I must not point fingers at the vendor as they have mentioned in their product manuals to disable the non-essential services to be disabled in the early phase it self. But we have left with no such choice now. May be this kind of elasticity & flexibility is desirable any product in the days forward.

Our content management system follows an event based consumer - subscriber model for sharing the events across different content related services such as Workflow, We found that our event subsystem and the associated small relational database related Java Processes are alive all the time. That must be the rationale behind keeping these processes alive.

Along with it, we could find a Servlet and Search agent built on Java API's alive all the time. All the villains are identified. But not able to keep them behind the bars as they are essential for the system's standard behavior and as-is status. This makes me think and highlight that Content management systems are still memory and event intensive model. I must say that this +ECM has derived lot of base model from the standards of Version Control systems and relational data models. What made it unique was the OS level coupling it has achieved by adding a lot of modifications on the OS registries and device drivers at the firmware level .. Anyway I am just curious what will these bunch of monsters will do tomorrow as the story continues. Data Diary and Data Dilemma continues ...

+Gokul Alex

PS: All views are my own