John,
I like the theoretical direction of your definition, but the phrase "prohibitively expensive with existing hardware" is problematic.
What's prohibitively expensive depends on one's budget, business case, business model, etc. My personal budget would probably prohibit expenses for more than a terabyte or two of data, while Google's budget permits much larger data sets of course.
Also, Google could probably claim to be processing "big data", so from their perspective, big data is not prohibitively expensive.
I took the easy route, and consulted the modern source of all knowledge to a first approximation, Wikipedia, which gives the following definition:
"Big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications."
http://en.wikipedia.org/wiki/Big_data Phil
> Date: Sat, 30 Aug 2014 23:01:56 -0400
> From: sowa@xxxxxxxxxxx
> To: ontolog-forum@xxxxxxxxxxxxxxxx
> Subject: Re: [ontolog-forum] FW: Looking to the Future of Data Science - NYTimes.com - 2014.08.27
>
> David, Phil, and John B,
>
> I suggest a very simple definition for Big Data:
>
> Data whose size N (in bytes) is so large that any algorithm that
> takes time that is polynomial in N (for any exponent greater than 1)
> is prohibitively expensive with existing hardware.
>
> This definition scales with the technology. It was true in 1960, when
> people did research on sorting algorithms that took (N log N) time.
>
> The computers today are a million times bigger and faster than in 1960,
> but BIg Data today cannot be processed by any polynomial algorithm
> (with an exponent greater than 1).
>
> And that definition will still be true when computers are a million
> times bigger and faster than today's.
>
> John
>
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