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Mobile Data Mining

  • Yuan Yao
  • Xing Su
  • Hanghang Tong

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Yuan Yao, Xing Su, Hanghang Tong
    Pages 1-6
  3. Yuan Yao, Xing Su, Hanghang Tong
    Pages 7-16
  4. Yuan Yao, Xing Su, Hanghang Tong
    Pages 17-23
  5. Yuan Yao, Xing Su, Hanghang Tong
    Pages 25-30
  6. Yuan Yao, Xing Su, Hanghang Tong
    Pages 31-41
  7. Yuan Yao, Xing Su, Hanghang Tong
    Pages 43-50
  8. Yuan Yao, Xing Su, Hanghang Tong
    Pages 51-53
  9. Back Matter
    Pages 55-58

About this book

Introduction

This SpringerBrief presents a typical life-cycle of mobile data mining applications, including:

  • data capturing and processing which determines what data to collect, how to collect these data, and how to reduce the noise in the data based on smartphone sensors
  •  feature engineering which extracts and selects features to serve as the input of algorithms based on the collected and processed data
  •  model and algorithm design
In particular, this brief concentrates on the model and algorithm design aspect, and explains three challenging requirements of mobile data mining applications: energy-saving, personalization, and real-time

 Energy saving is a fundamental requirement of mobile applications, due to the limited battery capacity of smartphones. The authors  explore the existing practices in the methodology level (e.g. by designing hierarchical models) for saving energy. Another fundamental requirement of mobile applications is personalization.  Most of the existing methods tend to train generic models for all users, but the authors provide existing personalized treatments for mobile applications, as the behaviors may differ greatly from one user to another in many mobile applications. The third requirement is real-time. That is, the mobile application should return responses in a real-time manner, meanwhile balancing effectiveness and efficiency.

 This SpringerBrief targets data mining and machine learning researchers and practitioners working in these related fields. Advanced level students studying computer science and electrical engineering will also find this brief useful as a study guide. 

Keywords

Mobile data data mining data capturing energy-saving personalization online update travel mode detection activity recognition indoor localization smartphone sensors data denoising data segmentation feature extraction feature selection hierarchical model personalized model online model real-time

Authors and affiliations

  • Yuan Yao
    • 1
  • Xing Su
    • 2
  • Hanghang Tong
    • 3
  1. 1.State Key Laboratory for Novel SoftwareNanjing UniversityNanjingChina
  2. 2.Graduate CenterCity University of New YorkNew YorkUSA
  3. 3.Arizona State UniversityTempeUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-02101-6
  • Copyright Information The Author(s), under exclusive license to Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-02100-9
  • Online ISBN 978-3-030-02101-6
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site