By: MindXchng

MindXchng — Sun, 06 Feb 2011 16:10:46 +0000

I think that the differentiation between nominal, numeric data on one hand and continuous data such as Video/Audio data on other hand need a bifurcation at a much higher conceptual level. What I mean is that we can for example try to find the difference between humans and dolphins via one to one comparison, but we can also recognize that in biological nomenclature, these two species diverge at much higher classification level.
I think same goes for data. Quantized data such a numbers and continuous data such as audio, video need much different level of treatment, because audio and video data types don’t belong to the same data type as numbers, unless audio//video is decomposed at a sufficient level to match the numeric datatype.
Then the question is what category audio/video(referred as AV datatype) belong to? do we have data classification nomenclature which has a place holder for such continuous datatype? or are we going to decompose (via tags in parallel databases) so that we know what the AV data mean in numeric datatype world.
In decomposition approach, using fast processing power and using image processing, video/audio can be decomposed at a sufficiently detailed level to afford dissecting the AV data and extract meaningful numeric equivalent. With multiple dimensions added to AV data, the amount of memory needed to hold and subsequently to process this data can be huge. But with faster parallel processing processors and image processing, the AV data with its dimensions can be stored in a multi-dimensional matrix.
I think and just my opinion is that with mathematical processing applied to AV data, we are trying to attach the mathematical meaning to the data; that is we are trying to quantize the continuous data so that it is suitable to processing by routines that have historically acted only on numeric data. If that is the case then each decomposed component of the continuous data need to be processed separately and then a aggregate function such as Artificial intelligence (AI) need to be used to correlate each individual results to interpret the aggregate data and attach that meaning (AI output) to the continuous data.
This is just one approach to handle the AV data, I am sure there may be other heuristic approaches (as mentioned in the article, particle dynamics, visual metaphors..)too, but I think the different approaches need to be evaluated to find which one lends more suitable to finding the Av data classification that fulfills the goals of 1. Extensible, 2. uniform approach, 3. borrows from the best of the solutions we have in related disciplines, 4. has potential to lend itself to faster and thus smaller (in terms of time required for meta data handling) solution to the problem of interpreting the AV data.
Thanks,

Comments on: Visualizing media-rich data: Part 2

By: MindXchng