A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications

Jiyang Xie,Yanting Zhang,Yupeng Li,Jianhua Zhang,Jun Guo,Zeyu Song,Hong Yu,Jinnan Zhan,Yuanyuan Qiao,Zhanyu Ma

doi:10.1155/2018/8738613

Jiyang Xie, Yanting Zhang + Show 8 more

Open Access

https://doi.org/10.1155/2018/8738613

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Abstract

This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently applied data analysis methods are reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior analysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.

Highlights

A Survey on Machine Learning-Based Mobile Big Data AnalysisJiyang Xie ,1 Zeyu Song, Yupeng Li, Yanting Zhang, Hong Yu, Jinnan Zhan, Zhanyu Ma ,1 Yuanyuan Qiao, Jianhua Zhang ,2 and Jun Guo
With the success of wireless local access network (WLAN) technology
mobile big data (MBD) contains a large variety of information of offline data and online real-time data stream generated from smart mobile terminals, sensors, and services and hastens various applications based on the advancement of data analysis technologies, such as collaborative filtering-based recommendation [46, 47], user social behavior characteristics analysis [48,49,50,51], vehicle communications in the Internet of Vehicles (IoV) [52], online smart healthcare [53], and city residents’ activity analysis [6]

Summary

A Survey on Machine Learning-Based Mobile Big Data Analysis

Jiyang Xie ,1 Zeyu Song, Yupeng Li, Yanting Zhang, Hong Yu, Jinnan Zhan, Zhanyu Ma ,1 Yuanyuan Qiao, Jianhua Zhang ,2 and Jun Guo. This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. It reviews the state-of-the-art applications of data analysis in the area of MBD. The frequently applied data analysis methods are reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior analysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. We summarize the main challenges and future development directions of mobile big data analysis

Introduction

Development and Collection of the Mobile Big Data

Applications of Machine Learning Methods in the Mobile Big Data Analysis

Findings

Conclusions and Future Challenges