Wlodarczak, Peter and Ally, Mustafa and Soar, Jeffrey (2016) Data process and analysis technologies of big data. In: Networking for big data. Big Data Series. Taylor & Francis (CRC Press), Boca Raton, FL, United States, pp. 103-119.
Abstract
Mayer-Schonberger and Cukier define Big Data as 'the ability of society to harness informationin novel ways to produce useful insights or goods and services of significant value [1].' Throughout the current literature, Big Data is usually defined by the three Vs: volume, velocity, and variety. Volume refers to the large amount of data. 'Large' ranges from gigabytes to petabytes. Typically, the volume increases, but the data itself does not change. Velocity refers to the speed at which the data volume grows. For instance, on Facebook more than 500â•–TB of data is created daily [2]. Variety describes the data types in Big Data. Big Data usually consists of structured, semistructured, and unstructured data. Big Data
requires all 3â•–Vs to apply (Figure 6.1).
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| Item Type: | Book Chapter (Commonwealth Reporting Category B) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Permanent restricted access to Published version, in accordance with the copyright policy of the publisher. |
| Faculty / Department / School: | Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise |
| Date Deposited: | 16 May 2017 02:27 |
| Last Modified: | 22 Feb 2018 02:43 |
| Uncontrolled Keywords: | data process, data analysis, big data technologies |
| Fields of Research : | 08 Information and Computing Sciences > 0806 Information Systems > 080699 Information Systems not elsewhere classified |
| Socio-Economic Objective: | E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences |
| URI: | http://eprints.usq.edu.au/id/eprint/31906 |
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