Paper by Jonathan Stuart Ward and Adam Barker: “The term big data has become ubiquitous. Owing to shared origin between academia, industry and the media there is no single unified definition, and various stakeholders provide diverse and often contradictory definitions. The lack of a consistent definition introduces ambiguity and hampers discourse relating to big data. This short paper attempts to collate the various definitions which have gained some degree of traction and to furnish a clear and concise definition of an otherwise ambiguous term…
Despite the range and differences existing within each of the aforementioned definitions there are some points of similarity. Notably all definitions make at least one of the following assertions:
Size: the volume of the datasets is a critical factor.
Complexity: the structure, behaviour and permutations of the datasets is a critical factor.
Technologies: the tools and techniques which are used to process a sizable or complex dataset is a critical factor.
The definitions surveyed here all encompass at least one of these factors, most encompass two. An extrapolation of these factors would therefore postulate the following: Big data is a term describing the storage and analysis of large and or complex data sets using a series of techniques including, but not limited to: NoSQL, MapReduce and machine learning.”