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A Design of a Distributed Computing Problem Solving Environment for Dietary Data Analysis
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  • Journal title : Journal of KIISE
  • Volume 42, Issue 7,  2015, pp.834-839
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2015.42.7.834
 Title & Authors
A Design of a Distributed Computing Problem Solving Environment for Dietary Data Analysis
Choi, Jieun; Ahn, Younsun; Kim, Yoonhee;
 
 Abstract
Recently, wellness has become an issue related to improvements in personal health and quality of life. Data that are accumulated daily, such as meals and momentum records, in addition to body measurement information such as body weight, BMI and blood pressure have been used to analyze the personal health data of an individual. Therefore, it has become possible to prevent potential disease and to analyze dietary or exercise patterns. In terms of food and nutrition, analyses are performed to evaluate the health status of an individual using dietary data. However, it is very difficult to process the large amount of dietary data. An analysis of dietary data includes four steps, and each step contains a series of iterative tasks that are executed over a long time. This paper proposes a problem solving environment that automates dietary data analysis, and the proposed framework increases the speed with which an experiment can be conducted.
 Keywords
problem solving environment;wellness;distributed computing;dietary data analysis;parallel processing;automation;
 Language
Korean
 Cited by
 References
1.
Wullianallur Raghupathi and Viju Raghupathi, "Big data analytics in healthcare: promise and potential," Health Information Science and Systems, Vol. 2, No. 1, pp. 1-10, 2014. crossref(new window)

2.
Nitesh V, Chawla PhD, Darcy A, Davis PhD, "Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework," Journal of General Internal Medicine, Vol. 28, No. 3, pp. 660-665, 2013.

3.
R. Nambiar, R. Bhardwaj, A. Sethi, R. vargheese, "A look at challenges and opportunities of Big Data analytics in healthcare," Big Data, 2013 IEEE International Conference, pp. 17-22, Oct. 2013.

4.
CAP [Online]. Available: https://itea3.org/project/cap.html

5.
Lu, R.M., Tjhi, W.C., Kee Khoon Lee, Long Wang, Xiaorong Li, Di Ma, "A Framework for Cloudbased Large-Scale Data Analytics and Visualization: Case Study on Multiscale Climate Data," Cloud Computing Technology and Science (CloudCom), pp. 618-622, Nov. 29 - Dec. 1, 2011.

6.
S. Park, H. Kang, Y. Kim, C. Kim, Y. Hyun, "An Integrated Scientific Experiment Framework for Numerical Analysis in e-Science Environment," Computation Tools 2012:The Third International Conference on Computational Logics, Algebras, Programming, Tools, and Benchmarking, pp. 34-37, 2012.

7.
Hongyong Yu, Deshuai Wang, "Research and Implementation of Massive Health Care Data Management and Analysis Based on Hadoop," Computational and Information Sciences (ICCIS), pp. 514-517, 2012.

8.
SAS [Online]. Available: http://www.sas.com/

9.
Korean National Nutrition Survey [Online] Available: http://knhanes.cdc.go.kr/