3D Data Visualization

Data visualization in 2D is what we have done most of our lives. Till recently, I viewed 3D as a medium for understanding and manipulating complex structures (say molecules, genes, architectural maps etc) both for academic and commercial use. With mashups, came the concept that you could intermix n-dimensional data (like OLAP) in a Web 2.0 environment. IBM’s ManyEyes uses this data to create 2D and 3D chart visualizations.

However, 3D visualization common use application still eludes me. I remember, as far as 10-12 years back, someone talked to me about “walking” into an Oracle database as an administrator and using hands to reorganize tablespaces, compress them and many other administrative actions. Years later, I watched Michael Douglas in Disclosure moving around a 3D file system and Tom Cruise and team waving life-videos and geo-spatial data on screens looking like a sheet of glass (I think it was in Minority Report).

Now Green Phosphor has come out with 3D technology based on the Content Injection and Control Protocol (CICP), sort of a “http for virtual worlds“, that merges excel data or database query outputs with 3D representations in a virtual world.

But I still struggle with possible applications. My friend Sid, at Indusgeeks, and his wonderful team, are looking at immersive and interactive 3D learning spaces for learning and collaboration.

What makes sense for me is not “representational” 3D (i.e. 3D visualization that depicts n-dimensional data visually), but “meaningful, context driven” 3D. For example, real time data about movement of whales in the oceans could be merged with a virtual ocean world where students could come and explore, replete with ocean and whale sounds, measurement techniques and tools etc. Or for that matter, a data center created on the fly for practice on measurements of power and cooling, from data that represents servers, power units, HVACs etc. These systems mirror real-life in ways that can go beyond the real life experience (e.g. walk inside a server or inside an artery). But they are also limited by the amount of kinesthetic immersion they can supply.

What is also interesting now is the availability of mobile phones with graphics accelerator cards built in. Imagine having a virtual world experience on your mobile phone. Look at Imageon. In fact, technologies are emerging today that allow you to use your mobile phone to click an image and have a backend system process indexed image (and possibly video) databases to return to you information related to that image. Imagine never having to be lost again or to click a product picture in a electronics store and get all the information and reviews related to that item.

In 3D terms, imagine being in a virtual world that reconstructs the actual background environment that each participant is coming from – one driving his car while in the conference, the other in her office, the third on the field at the scene of action, each being able to access and share information.

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Slightly Morbid

I came across this article which I found morbidly fascinating and extremely relevant. Somewhat similar to spectral networks discussed by Lisa Lane in CCK08, the article deals with an online presence left orphaned when the real life “basis” ends. One obvious aspect, made more complex by social networking, is that your user names and passwords are now important assets, perhaps even as important in some cases as bank account or social security information (maybe we shall see these merge as time goes by). Perhaps at some time, there may be a trade built in, passing from generation to generation through inheritance or from company to company through direct sales. The other obvious aspect is the amount of information that will be left orphaned in the public domain, perhaps never to be archived or perhaps to simply be deleted past some period of inactivity.

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Connectivist Metrics

I thought I would take a stab at defining what connectivist metrics could include. Having read in Stephen’s post, Connectivist Dynamics in Communities, that connectivist networks produce connective knowledge and that four elements  (autonomy, diversity, open-ness and interactivity & connectedness) distinguish a knowledge-generating network from a mere set of connected elements, I thought it would make sense to start here.

The metrics that are typically being suggested are metrics such as page views, new memberships,  number and type of new media contributions, number of discussion threads, ratings (satisfaction and post ratings), number of new topics, number of connections, social network tracking and number of posts/discussions etc.

I would venture some possible metrics for the four categories that Stephen outlined. Disclaimer: These are random speculations for now.

autonomy

• Number of individuals who joined the network through invitation vs. requested membership/enrolled by their own agency?
• Number of members who understand how to use tools that are engaged by the network to perform basic functions necessary to participate in the network?
• Number of members who initiated a conversation that involved other people?
• Number of people who participated in a learning activity initiated by others?

diversity

• Number of times a member agreed or disagreed with an opinion expressed in the community?
• Number of members belonging to distinct backgrounds (coud be multiple views here)
• Unique resources bookmarked by the member?
• Unique connections vs shared connections
• How many unique conversations exist at any point in time?
• Number of homogeneous or differentiated conversations, by context and by participation?

open-ness

• What is the net flow of connections to and from the network? Positive/negative vs high/medium/low
• Number of accepted/rejected requests to join the network

interactivity & connectedness

• How many members are engaged in each conversation on average?
• Per member, per background, per conversation statistics of participation
• Trend analyses offered by SNA

Further, the RoI from the network would perhaps emerge if these metrics can answer the following questions:

• Did the network generate new knowledge?
• Did members who needed to learn in order to perform actually learn?
• Were any members disadvantaged in any way and could not benefit from the interactions?
• Did any innovative ideas arise out of the interactions?
• Did nodes in the network become more connected?
• Did members show an increased ability to manage new information and adapt?

More thoughts to follow.

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On communities, processes and technology

A recent set of conversations with customers and colleagues around communities of practice, networked learning, tools and platforms has provoked a lot of thought.

One perspective, that was heavily process oriented & steeped in real life experiences, argued that unless processes and workflows (and related metrics) were established, implementing these tools in the enterprise would be exhausting and with little return for the amount of effort it would take to manage and the money it would cost.

Then I came across (thanks Swati and Shikha!) this Defence Acquisition University (DAU)  Community of Practice Implementation Guide, which provides a 14-step, 3 phaseprocess for setting up practices that could contain CoPs, Shared Interest Areas (SIA) and collaborative workspaces. This document is very elaborate and covers processes, roles, permissions, workflow, engagement rules and metrics for setting up CoPs and community knowledge bases.

With true process orientation, this document provides a fairly detailed best practice for the DAU in its community development initiatives. What struck me, at second glance, was the fact that it leverages the same principles that we would use to create and manage an enterprise unit. 

The second set of comments was around how useful or participated in really are blogs and wikis. Talk CoPs or networked learning, and all that people think of is Web 2.0 technology and tools, the hype not really difficult to understand, given that major technology vendors are pushing for implementation of these tools in their recent launches.

The perception that the process and/or the technology are responsible for making networked learning happen is problematic. This is especially true given the power laws we have experienced in terms of community participation and effectiveness or the constant refrain that elearning is not, perhaps, living upto its potential.

Stephen explains in his post, Connectivist Dynamics in Communities, that connectivist networks produce connective knowledge. Four elements  distinguish a knowledge-generating network from a mere set of connected elements. These are autonomy, diversity, open-ness and interactivity & connectedness. There are compelling arguments that Stephen makes, as in the past, that we need to respect these elements if we want to increase the probability of generating new knowledge (and make sense of the current base of knowledge). These elements can also be the basis of metrics and tracking.

George laments the inadequacy of tools for sense-making. He also declares “…But any view of society that does not start with the individual is disconcerting.”

All these views, taken together, suggest that there is something more to networked learning than just processes and technology. It is a connectivist approach, a model that focuses on how we learn, that provides us a different lens through which to regard fundamental questions such as how do we learn to perform in a fast changing environment or how do we get incited to participate in a network to create new knowledge.

More concerted thoughts to follow in good time…

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