A glossary for big data in population and public health: discussion and commentary on terminology and research methods

被引:20
作者
Fuller, Daniel [1 ]
Buote, Richard [2 ]
Stanley, Kevin [3 ]
机构
[1] Mem Univ Newfoundland, Sch Human Kinet & Recreat, St John, NF A1C 5S7, Canada
[2] Mem Univ Newfoundland, Div Community Hlth & Humanities, Fac Med, St John, NF, Canada
[3] Univ Saskatchewan, Dept Comp Sci, Coll Arts & Sci, Saskatoon, SK, Canada
关键词
D O I
10.1136/jech-2017-209608
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
The volume and velocity of data are growing rapidly and big data analytics are being applied to these data in many fields. Population and public health researchers may be unfamiliar with the terminology and statistical methods used in big data. This creates a barrier to the application of big data analytics. The purpose of this glossary is to define terms used in big data and big data analytics and to contextualise these terms. We define the five Vs of big data and provide definitions and distinctions for data mining, machine learning and deep learning, among other terms. We provide key distinctions between big data and statistical analysis methods applied to big data. We contextualise the glossary by providing examples where big data analysis methods have been applied to population and public health research problems and provide brief guidance on how to learn big data analysis methods.
引用
收藏
页码:1113 / 1117
页数:5
相关论文
共 49 条
[1]   The future of video analytics for surveillance and its ethical implications [J].
Adams, Andrew A. ;
Ferryman, James M. .
SECURITY JOURNAL, 2015, 28 (03) :272-289
[2]   Big Data for Health [J].
Andreu-Perez, Javier ;
Poon, Carmen C. Y. ;
Merrifield, Robert D. ;
Wong, Stephen T. C. ;
Yang, Guang-Zhong .
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2015, 19 (04) :1193-1208
[3]  
[Anonymous], 1998, Mach Learn, DOI DOI 10.1023/A:1017181826899
[4]  
[Anonymous], EARTH IMAGING J
[5]  
Aschard H, BIORXIV
[6]  
Bethge M, 2016, DEEPART
[7]  
Blasch E, 2013, 2013 16 INT C INF FU
[8]  
boyd d., 2011, DECADE INTERNET TIME, DOI DOI 10.2139/SSRN.1926431
[9]  
Buitinck L, 2013, ECML PKDD WORKSH LAN, P108, DOI DOI 10.48550/ARXIV.1309.0238
[10]   Fall Detection Using Smartphone Audio Features [J].
Cheffena, Michael .
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2016, 20 (04) :1073-1080