| NIST created a working group on this topic.
From : NIST Big Data Definitions and Taxonomies
Version 0.9
Definitions & Taxonomies Subgroup
NIST Big Data Working Group (NBD-WG)
November, 2013
Big data is used as a concept that refers to the inability of traditional data architectures to efficiently handle the new data sets. Characteristics that force a new architecture to achieve efficiencies are the dataset-at-rest characteristics volume, and variety of data from multiple domains or types; and from the data-in-motion characteristics of velocity, or rate of flow, and variability, as the change in velocity. Each of these characteristics result in different architectures or different data lifecycle process orderings to achieve needed efficiencies. A number of other terms (particularly anything that can be expressed using a term starting with the letter ‘V”) are also used, but a number of these refer to the analytics, not to new big data architectures.
Their docs are at :
Cheers, David
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On 23 Jan 2014, at 10:08, Ian Glendinning wrote: I tend to use a working definition more like this: Data is "big" when its size makes it large enough that patterns discoverable within it are statistically more significant than any patterns imposed by original schemas. Ian On Thu, Jan 23, 2014 at 12:32 AM, John McClure < jmcclure@xxxxxxxxxxxxxx> wrote: Hello,
Is the following definition for "Big Data" from Wikipedia what we're working
with? Or, in the context of Semantic Web and Applied Ontologies, are we
presuming "Big Semantic Data"?
Thanks for your comments.
Big data[1][2] is the term for a collection of data sets so large and
complex that it becomes difficult to process using on-hand database
management tools or traditional data processing applications. The challenges
include capture, curation, storage,[3] search, sharing, transfer,
analysis,[4] and visualization. The trend to larger data sets is due to the
additional information derivable from analysis of a single large set of
related data, as compared to separate smaller sets with the same total
amount of data, allowing correlations to be found to "spot business trends,
determine quality of research, prevent diseases, link legal citations,
combat crime, and determine real-time roadway traffic conditions."[5][6][7]
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