• Corpus ID: 51908593

Mobile big data analysis with machine learning

@article{Xie2018MobileBD,
  title={Mobile big data analysis with machine learning},
  author={Jiyang Xie and Zeyu Song and Yupeng Li and Zhanyu Ma},
  journal={ArXiv},
  year={2018},
  volume={abs/1808.00803}
}
This paper investigates to identify the requirement and the development of machine learning-based mobile big data analysis through discussing the insights of challenges in the mobile big data (MBD). 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 adopted methods of data analysis are reviewed. Three typical applications of MBD analysis, namely wireless channel modeling, human… 

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References

SHOWING 1-10 OF 147 REFERENCES

Management and application of mobile big data

TLDR
An overview of mobile big data's content, scope, methods, challenges and samples is presented and a mobile data infrastructure (MDI) and aMobile data lifetime management (MDLM) model is introduced.

The Role of Data Analysis in the Development of Intelligent Energy Networks

TLDR
More comprehensive data analysis methods are needed to handle the increasing amount of data and to mine more valuable information in intelligent energy networks.

Data scheme-based wireless channel modeling method: motivation, principle and performance

TLDR
A channel-modeling method using PCA is proposed, whose principle is to utilize the features and structures extracted from the CIR data collected by measurements, and then model the wireless channel of the targeted measurement scenario.

The interdisciplinary research of big data and wireless channel: A cluster-nuclei based channel model

TLDR
A cluster-nuclei based model is proposed, which takes advantages of both the stochastical model and deterministic model for mobile communication, and can be expanded in versatile application to support future mobile research.

Mobile big data analytics using deep learning and apache spark

TLDR
An overview and brief tutorial on deep learning in mobile big data analytics and discusses a scalable learning framework over Apache Spark that speeds up the learning of deep models consisting of many hidden layers and millions of parameters.

Mobile Big Data Analytics: Research, Practice, and Opportunities

TLDR
This panel will explore how the academia and industry are tackling mobile big data analytic challenges and identify and debate the key challenges and opportunities, in terms of applications, queries, architectures, to which the mobile data management and mobile data mining communities should contribute to.

Big data enabled user behavior characteristics in mobile internet

TLDR
This analysis provides a comprehensive understanding of user behavior in 4G networks, which may be used by network operators to design appropriate mechanisms in resource provision and mobility management for resource consumers based on different categories of applications.

Learning to hash for big data: Current status and future trends

TLDR
By representing the data as binary code, learning to hash (LH) can dramatically reduce the storage and communication cost, thereby improving the efficiency and scalability of BDML systems, and can also alleviate the curse of dimensionality in BD ML systems.

Big data caching for networking: moving from cloud to edge

In order to cope with the relentless data tsunami in 5G wireless networks, current approaches such as acquiring new spectrum, deploying more BSs, and increasing nodes in mobile packet core networks

Big Data Driven Hidden Markov Model Based Individual Mobility Prediction at Points of Interest

TLDR
This paper investigates the effect of living habits on the models of spatio-temporal prediction and next-place prediction, and selects one from these two models for an individual to achieve effective mobility prediction at users’ points of interest.
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