Mobile Data Mining (eBook)

Yuan Yao, Xing Su, Hanghang Tong (Autoren)

eBook Download: PDF

2018 | 1st ed. 2018
IX, 58 Seiten
Springer International Publishing (Verlag)
978-3-030-02101-6 (ISBN)

Infos

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. 

Inhalt

Preface 6
Contents 7
Acronyms 9
1 Introduction 10
1.1 Background 10
1.2 Typical Applications 11
1.3 Steps, Characteristics, and Challenges 12
1.4 Roadmap 14
2 Data Capturing and Processing 16
2.1 Smartphone Sensors 16
2.2 Data Collection 17
2.3 Data Denoising 21
2.4 Summary 25
3 Feature Engineering 26
3.1 Data Segmentation 26
3.2 Feature Extraction 27
3.3 Feature Analysis and Sensor Selection 28
3.4 Summary 32
4 Hierarchical Model 33
4.1 Problem Description 33
4.2 A Hierarchical Framework 34
4.3 Experimental Evaluations 36
4.4 Summary 38
5 Personalized Model 39
5.1 Problem Description 39
5.2 The Personalized Approach: Overview 40
5.3 The Personalized Approach: Details 42
5.3.1 Similarity Computation 42
5.3.2 Distribution Estimation 43
5.3.3 Sample Selection 44
5.3.4 Sample Reweighting 44
5.3.5 Algorithm Analysis 45
5.4 Experimental Evaluations 46
5.4.1 Experiment Setup 47
5.4.2 Experiment Results 47
5.5 Summary 49
6 Online Model 50
6.1 Problem Description 50
6.2 Online Learning 51
6.3 Experimental Evaluations 52
6.3.1 Online Learning vs. Offline Learning 53
6.3.2 How Much Data Are Enough to Train an Initial Model? 56
6.3.3 Combining with the Hierarchical Model 56
6.4 Summary 57
7 Conclusions 58
7.1 Summary 58
7.2 Discussions 59
7.2.1 More Combinations of Sensors 59
7.2.2 More Usages of Smartphone Sensors 60
References 61

Produkt-Details

• Erscheint lt. Verlag: 31.10.2018
• Reihe/Serie: SpringerBriefs in Computer Science
• Reihe/Serie: SpringerBriefs in Computer Science
• Zusatzinfo: IX, 58 p. 22 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Netzwerke
• Themenwelt: Informatik ► Software Entwicklung ► Mobile- / App-Entwicklung
• Schlagworte: activity recognition • data capturing • data denoising • Data Mining • Data Segmentation • Energy-Saving • feature extraction • Feature Selection • hierarchical model • indoor localization • Mobile Data • online model • online update • personalization • personalized model • real-time • Smartphone Sensors • travel mode detection
• ISBN-10: 3-030-02101-7 / 3030021017
• ISBN-13: 978-3-030-02101-6 / 9783030021016