Big data - What is it and what use is it?

Journal of Ambulatory Care Management

Volumn 37, Issue 3, 2014, Pages 196-198

  Atkinson, Graham ^a  

^a JKTG Foundation   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONFIDENTIALITY; DATA MINING; HUMAN; INFORMATION PROCESSING; PROCEDURES; STANDARDS; STATISTICAL ANALYSIS; STATISTICS;

CONFIDENTIALITY; DATA COLLECTION; DATA INTERPRETATION, STATISTICAL; DATA MINING; HUMANS; STATISTICS AS TOPIC;

EID: 84902156988     PISSN: 01489917     EISSN: 15503267     Source Type: Journal    
DOI: 10.1097/JAC.0000000000000039     Document Type: Article

References (8)

1. Anon. (2012a, August 9). Dr Fishers casebook: Small is beautiful. Significance, 9(4), 29
2. Anon. (2012b). "Small data drove a great flowering of statistical methodology, leading to the development of t methods by Student and other ways of analyzing data that come in not vast quantities," from Dr. Fishers Casebook: Small is beautiful. Significance, 9(4), 29
3. Lazer, D., Kennedy, R., King, G., &Vespignani, A. (2014). Parable of Google flu: Traps in Big Data analysis. Science, 343(6176), 1203-1205
4. Mayer-Schonberger, V., &Cukier, K. (2014). Big Data, Mariner Books. Boston, MA: Houghton Mifflin Harcourt
5. McCarthy, I. (in press). The minimally clinically important difference and its use in medical research. Retrieved from http://www . significancemagazine.org/details/webexclusive/2041047/The-Minimum-Clinically- Important-Differenceand-its-Use-in-Medical-Research.html#
6. National Research Council. (2013). Frontiers in massive data analysis. Washington, DC: National Academies Press
7. Rose, S. (2012, August 9). Big Data and the future. Significance, 9(4), 47-48
8. Wang, S. D. (2013). Opportunities and challenges of clinical research in the big data era: From RCT to BCT. The Journal of Thoracic Disease, 5(6):721-723