Improved Statistical Analysis Method Based on Big Data Technology

Publications

Share / Export Citation / Email / Print / Text size:

International Journal of Advanced Network, Monitoring and Controls

Xi'an Technological University

Subject: Computer Science , Software Engineering

GET ALERTS

eISSN: 2470-8038

DESCRIPTION

0
Reader(s)
0
Visit(s)
0
Comment(s)
0
Share(s)

SEARCH WITHIN CONTENT

FIND ARTICLE

Volume / Issue / page

Related articles

VOLUME 2 , ISSUE 4 (December 2017) > List of articles

Improved Statistical Analysis Method Based on Big Data Technology

Hongsheng Xu * / Ganglong Fan / Ke Li

Keywords : Big data, Statistical analysis, Hadoop, Data acquisition, Data mining

Citation Information : International Journal of Advanced Network, Monitoring and Controls. Volume 2, Issue 4, Pages 26-30, DOI: https://doi.org/10.1109/iccnea.2017.46

License : (CC BY-NC-ND 4.0)

Published Online: 24-April-2018

ARTICLE

ABSTRACT

Big data technology refers to the rapid acquisition of valuable information from various types of large amounts of data. It can be divided into 8 technologies: data acquisition, data access, infrastructure, data processing, statistical analysis, data mining, model prediction and results presentation. The paper presents improved statistical analysis method based on big data technology. A statistical analysis model in big data environment is designed to extract useful information features from large amounts of data based on the Hadoop system by using its distributed storage and parallel processing mechanism.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

Patricia L. Mabry. Making Sense of the Data Explosion. American Journal of Preventive Medicine, 2011, 40(5),pp.12-30.

 

Viktor Mayer-Schonberger,Kenneth Cukier,Big Data: A Revolution That Will Transform How We Live, Work and Think, Hodder & Stoughton,2013.

 

Letouzey, S. Huberlant, P. Mares et al.. Assessment of Quality of Life of Patients Supported for Genital Prolapse Surgery: Feasibility of a Computerized Data Collection. The Journal of Minimally Invasive Gynecology, 2011, 18(6).

 

W. Aigner, A. Rind, S. Hoffmann. Comparative Evaluation of an Interactive Time-Series Visualization that Combines Quantitative Data with Qualitative Abstractions,Computer Graphics Forum, 2012, 31,pp.3-15.

 

B. Zhu, L. Xu, D. Faries et al.. PMH83 Comparison of Total Health Care Costs Between Remitters and Non-Remitters for Schizophrenia Patients from a Prospective Longitudinal, Observational Study in the Presence of Missing Data. Value in Health, 2012, 15(4),pp.100-120.

 

Hassibi,Khosrow & De, Big Data, Data Mining, and Machine Learn, John Wiley Sons, 2014.

 

Ahmed M. Abdel-Khalek, Mostafa A. Elseifi, Kevin Gaspard et al.. Model to Estimate Pavement Structural Number at Network Level with Rolling Wheel Deflectometer Data. Transportation Research Record: Journal of the Transportation Research Board, 2012, 2,pp.30- 41.

 

Lee, Keon Myung & Park, Seung Jong & Lee, Soft Computing in Big Data Processing, Springer, 2014.

 

Yanqing Lv, Jianmin Gao, Zhiyong Gao and Hongquan Jiang, “Multifractal information fusion based condition diagnosis for process complex”, Process Mechanical Engineering, (2012),pp.1-8.

 

Bauckhage C, Kersting K. Data mining and pattern recognition in agriculture, KI-Künstliche Intelligenz, 2013, 27(4): 313-324

 

EXTRA FILES

COMMENTS