Table 1 Description of themes identified in big data definitions from literature

I

Volume, size, voluminous, cardinality

Large quantities of data in number of bytes; size of available data (e.g. all records instead of a sample); beyond conventional storage techniques; number of records at a particular instance

[3, 5, 6, 15, 32–34, 36, 37, 39]

Velocity, continuity

Flow rate at which data is created, stored, analysed, and visualised; increased through invention of new data streams such as social media; beyond conventional means of processing, needing new techniques such as streaming; growth of data over time

[3, 5, 6, 32–34, 37]

Variety, complexity

Many different types of data; not bound to a traditional data format; format changes over time; heterogeneous and unstructured data

[3, 5, 6, 15, 32–34, 36, 37, 39]

Veracity

Trustworthiness of data; reliability of data quality and gathering environment

[3, 32]

Value

Worth/relevancy of data (e.g. economic, individual/privacy, societal, humanity value)

[3, 6, 38]

Variability

Consistency of data over time; influences which systematically change data measures over time

[3, 34]

II

Information

Where signals are turned into data (e.g. book digitalisation, or gathering from personal device measurements)

[14]

Technology

Tools, systems, and software (e.g. scalable processing and transmission systems such as Hadoop)

[14, 15, 34–36, 38]

Methods

Procedures and their application (e.g. clustering, natural language processing, machine learning, neural networks, visualisation)

[14, 35, 38]

Impact

Ethical, business, societal

[14]

III

Beyond conventional

Data whose size call for methods beyond the tried-and-true; necessity of scalable systems for storage, processing, manipulation, analysis, visualisation

[35–37]

IV

Application

About the application domain treated in the papers

–