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Re: [ontolog-forum] FW: Looking to the Future of Data Science - NYTimes.

To: ontolog-forum@xxxxxxxxxxxxxxxx
From: John F Sowa <sowa@xxxxxxxxxxx>
Date: Sun, 31 Aug 2014 22:17:21 -0400
Message-id: <5403D731.3040605@xxxxxxxxxxx>
Dale and Kingsley,    (01)

I'd like to focus on two of the points that Dale made:    (02)

> Is it at all helpful to more precisely identify what is contributing
> to the bigness in big data?    (03)

For different applications, different features may have a stronger 
influence on the computational complexity.  In calculating the time,
look for whatever size N is dominating the computation.    (04)

For any amount N that is considered large, the algorithms that process
the data should take time that increases in proportion to N -- or at
least no worse than (N log N).    (05)

> Computing power will continue to increase making the number
> of instances  a trivial matter    (06)

No.  As computers get bigger and faster, they always find or create
more data for other computers to process.  So the typical data size N
will grow as fast as the size, speed, and number of computers.    (07)

And for any size N, N^2 will always be much, much, much bigger than N.    (08)

> According the ISO task force trying to make sense of big data
> a draft definition is:
> Big Data: a data set(s) with characteristics that for a particular
> problem domain at a given point in time cannot be efficiently processed
> using current/existing/established/traditional technologies and
> techniques in order to extract value    (09)

This definition is not bad, but it implies that the word 'big' is
a moving target.  A criterion based on computational complexity
scales with the technology.    (010)

John    (011)

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