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Mathematical Problems in Engineering - Volume 2015 2015, Article ID 548605, 8 pages -

Research Article

College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China

School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China

Received 6 June 2014; Revised 25 December 2014; Accepted 13 January 2015

Academic Editor: Thomas Hanne

Copyright © 2015 Xiaobo Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper addresses missing value imputation for the Internet of Things IoT. Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete. As a result of this, some work, which is related to the data of the IoT, can’t be carried out normally. And it leads to the reduction in the accuracy and reliability of the data analysis results. This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems. Then, we propose three new models to solve the corresponding problems, and they are model of missing value imputation based on context and linear mean MCL, model of missing value imputation based on binary search MBS, and model of missing value imputation based on Gaussian mixture model MGI. Experimental results showed that the three models can improve the accuracy, reliability, and stability of missing value imputation greatly and effectively.





Autor: Xiaobo Yan, Weiqing Xiong, Liang Hu, Feng Wang, and Kuo Zhao

Fuente: https://www.hindawi.com/



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