Context-Aware Machine Learning and Mobile Data Analytics: Automated Rule-Based Services with Intelligent Decision-Making

Part I Preliminaries

1 Introduction to Context-Aware Machine Learning and Mobile Data

Analytics

1.1 Introduction

 

1.2 Context-Aware Machine Learning

1.3 Mobile Data Analytics

1.4 An Overview of this Book

1.5 Conclusion

References

2 Application Scenarios and Basic Structure for Context-Aware

Machine Learning Framework

2.1 Motivational Examples with Application Scenarios

2.2 Structure and Elements of Context-Aware Machine Learning

Framework

2.2.1 Contextual Data Acquisition

2.2.2 Context Discretization

2.2.3 Contextual Rule Discovery

 

2.2.4 Dynamic Updating and Management of Rules

2.3 Conclusion

References

3 A Literature Review on Context-Aware Machine Learning and

Mobile Data Analytics

3.1 Contextual Information

3.1.1 Definitions of Contexts

3.1.2 Understanding the Relevancy of Contexts

3.2 Context Discretization

3.2.1 Discretization of Time-Series Data

 

3.2.2 Static Segmentation

vii

viii Contents

3.2.3 Dynamic Segmentation

3.3 Rule Discovery

3.3.1 Association Rule Mining

3.3.2 Classification Rules

 

3.4 Incremental Learning and Updating

3.5 Identifying the Scope of Research

3.6 Conclusion

References

Part II Context-Aware Rule Learning and Management

4 Contextual Mobile Datasets, Pre-processing and Feature Selection

4.1 Smart Mobile Phone Data and Associated Contexts

4.1.1 Phone Call Log

4.1.2 Mobile SMS Log

4.1.3 Smartphone App Usage Log

 

4.1.4 Mobile Phone Notification Log

4.1.5 Web or Navigation Log

4.1.6 Game Log

4.1.7 Smartphone Life Log

4.1.8 Dataset Summary

4.2 Examples of Contextual Mobile Phone Data

4.2.1 Time-Series Mobile Phone Data

 

4.2.2 Mobile phone data with multi-dimensional contexts

4.2.3 Contextual Apps Usage Data

4.3 Data Preprocessing

4.3.1 Data Cleaning

4.3.2 Data Integration

4.3.3 Data Transformation

4.3.4 Data Reduction

 

4.4 Dimensionality Reduction

4.4.1 Feature Selection

4.4.2 Feature Extraction

4.4.3 Dimensionality Reduction Algorithms

4.5 Conclusion

References

5 Discretization of Time-Series Behavioral Data and Rule Generation

based on Temporal Context

5.1 Introduction

5.2 Requirements Analysis

 

5.3 Time-series Segmentation Approach

5.3.1 Approach Overview

5.3.2 Initial Time Slices Generation

5.3.3 Behavior-Oriented Segments Generation

Contents ix

5.3.4 Selection of Optimal Segmentation

5.3.5 Temporal Behavior Rule Generation using Time Segments

 

5.4 Effectiveness Comparison

5.5 Conclusion

References

6 Discovering User Behavioral Rules based on Multi-dimensional

Contexts

6.1 Introduction

6.2 Multi-dimensional Contexts in User Behavioral Rules

6.3 Requirements Analysis

6.4 Rule Mining Methodology

6.4.1 Identifying the Precedence of Context

 

6.4.2 Designing Association Generation Tree

6.4.3 Extracting Non-Redundant Behavioral Association Rules

6.5 Experimental Analysis

 

6.5.1 Effect on the Number of Produced Rules

6.5.2 Effect of Confidence Preference the Predicted Accuracy

6.5.3 Effectiveness Comparison

6.6 Conclusion

References

7 Recency-based Updating and Dynamic Management of Contextual

Rules

7.1 Introduction

7.2 Requirements Analysis

7.3 An Example of Recent Data

 

7.4 Identifying Optimal Period of Recent Log Data

7.4.1 Data Splitting...

第一部分 序言

1 引言：上下文感知機器學習和移動數據分析

1.1 引言

1.2 上下文感知機器學習

1.3 移動數據分析

1.4 本書概述

1.5 結論

參考文獻

2 上下文感知機器學習框架的應用場景和基本結構

2.1 應用場景的動機性例子

2.2 上下文感知機器學習框架的結構和元素

2.2.1 上下文數據獲取

2.2.2 上下文離散化

2.2.3 上下文規則發現

2.2.4 規則的動態更新和管理

2.3 結論

參考文獻

3 上下文感知機器學習和移動數據分析的文獻綜述

3.1 上下文信息

3.1.1 上下文定義

3.1.2 理解上下文的相關性

3.2 上下文離散化

3.2.1 時間序列數據的離散化

3.2.2 靜態分割

3.2.3 動態分割

3.3 規則發現

3.3.1 關聯規則挖掘

3.3.2 分類規則

3.4 增量學習和更新

3.5 確定研究範圍

3.6 結論

參考文獻

第二部分 上下文感知規則學習和管理

4 上下文移動數據集的預處理和特徵選擇

4.1 智能手機數據和相關上下文

4.1.1 手機通話記錄

4.1.2 手機短信記錄

4.1.3 智能手機應用使用記錄

4.1.4 手機通知記錄

4.1.5 網絡或導航記錄

4.1.6 遊戲記錄

4.1.7 智能手機生活記錄

4.1.8 數據集概述

4.2 上下文移動手機數據的例子

4.2.1 時間序列手機數據

4.2.2 具有多維上下文的手機數據

4.2.3 上下文應用使用數據

4.3 數據預處理

4.3.1 數據清理

4.3.2 數據集成

4.3.3 數據轉換

4.3.4 數據降維

4.4 維度降低

4.4.1 特徵選擇

4.4.2 特徵提取

4.4.3 維度降低算法

4.5 結論

參考文獻

5 基於時間上下文的時間序列行為數據離散化和規則生成

5.1 引言

5.2 需求分析

5.3 時間序列分割方法

5.3.1 方法概述

5.3.2 初始時間片段生成

5.3.3 基於行為的分段生成

5.3.4 選擇最佳分割

5.3.5 使用時間片段生成時間行為規則

5.4 效果比較

5.5 結論

參考文獻

6 基於多維上下文的用戶行為規則發現

6.1 引言

6.2 用戶行為規則中的多維上下文

6.3 需求分析

6.4 規則挖掘方法

6.4.1 確定上下文的先行性

6.4.2 設計關聯生成樹

6.4.3 提取非冗餘的行為關聯規則

6.5 實驗分析

6.5.1 對生成規則數量的影響

6.5.2 對預測準確性的影響

結論

參考文獻

Iqbal H. Sarker received his Ph.D. under the department of Computer Science and Software Engineering from Swinburne University of Technology, Melbourne, Australia in 2018. Currently, he is working as a faculty member of the Department of Computer Science and Engineering at Chittagong University of Engineering and Technology. He is one of the Research Founder of the International AIQT Foundation, Switzerland. His professional and research interests include - Data Science, Machine Learning, AI-Driven Computing, Cybersecurity Intelligence, Behavioral Analytics, Context-Aware Computing and IoT-Smart City Technologies. He has over 100 publications in leading venues including Journals (Journal of Network and Computer Applications - Elsevier, USA; Internet of Things - Elsevier; Expert Systems with Applications - Elsevier, UK; Journal of Big Data - Springer Nature, UK; Mobile Network and Applications - Springer, Netherlands; The Computer Journal, Oxford University Press, UK; IEEE Transactions on Artificial Intelligence, IEEE Access, USA and so on) and Conferences such as IEEE DSAA, IEEE Percom, ACM Ubicomp, ACM Mobiquitous, Springer LNCS PAKDD, Springer LNCS ADMA and so on. He is a member of IEEE and ACM.
Alan Colman: is an Adjunct Research Fellow in Software Engineering at Swinburne University of Technology, Melbourne. His main research focus is on adaptive service-oriented systems and architectures. He has also made research contributions to feature-oriented software engineering, context-aware computing, control-theoretic adaptation and performance prediction of software systems, and user-centric access control and data sharing with blockchain. He has over 150 publications in leading software journals and conference proceedings with over 2300 citations to his papers.

Jun Han: received his Ph.D. in Computer Science from the University of Queensland. Since 2003, he has been Professor of Software Engineering at Swinburne University of Technology. He has authored two books and published over 260 peer-reviewed articles in leading international journals and conferences. His current research interests include adaptive and context-aware systems, services and cloud systems engineering, software and service behavior mining, data-driven software engineering, software architectures, software security and performance
Paul Watters: Professor Paul A. Watters is Academic Dean at Academies Australasia Polytechnic, an ASX-listed higher education provider operating 18 colleges in Australia and Singapore. Professor Watters is also Honorary Professor of Security Studies and Criminology at Macquarie University, and Adjunct Professor of Cyber Security at La Trobe University. He has worked closely with many large companies and law enforcement agencies in Australia on applied cyber R&D projects, and he has written many books and academic papers on cybersecurity, cybercrime and related topics. His research has been cited 4,964 times, and his h-index is 33. He obtained his PhD at Macquarie University in 2000, and read for his MPhil at the University of Cambridge in 1997 after completing a BA(First Class Honours) at the University of Tasmania, and a BA at the University of Newcastle. Professor Watters is a Fellow of the British Computer Society, a Senior Member of the IEEE, a Chartered IT Professional, and a Member of the Australian Psychological Society.

Iqbal H. Sarker於2018年在澳洲墨爾本的Swinburne科技大學的計算機科學和軟件工程系獲得博士學位。目前，他在孟加拉的Chittagong工程與技術大學的計算機科學和工程系擔任教職。他是國際AIQT基金會的研究創始人之一，該基金會位於瑞士。他的專業和研究興趣包括數據科學、機器學習、人工智能驅動的計算、網絡安全情報、行為分析、上下文感知計算和物聯網智慧城市技術。他在領先期刊（如美國愛思唯爾的《網絡與計算機應用》、愛思唯爾的《物聯網》、愛思唯爾的《專家系統與應用》、Springer Nature的《大數據》、Springer的《移動網絡和應用》、牛津大學出版社的《計算機期刊》、IEEE的《人工智能交易》、IEEE的《訪問》等）和會議（如IEEE DSAA、IEEE Percom、ACM Ubicomp、ACM Mobiquitous、Springer LNCS PAKDD、Springer LNCS ADMA等）上發表了100多篇論文。他是IEEE和ACM的成員。

Alan Colman是墨爾本Swinburne科技大學軟件工程的兼職研究員。他的主要研究重點是適應性服務導向系統和架構。他還在特徵導向軟件工程、上下文感知計算、控制理論適應和軟件系統性能預測、以及以用戶為中心的區塊鏈訪問控制和數據共享方面做出了研究貢獻。他在領先的軟件期刊和會議論文中發表了150多篇論文，他的論文被引用了2300多次。

Jun Han於2003年獲得昆士蘭大學的計算機科學博士學位。自那時以來，他一直在墨爾本的Swinburne科技大學擔任軟件工程教授。他撰寫了兩本書，並在領先的國際期刊和會議上發表了260多篇同行評審的文章。他目前的研究興趣包括適應性和上下文感知系統、服務和雲系統工程、軟件和服務行為挖掘、數據驅動的軟件工程、軟件架構、軟件安全和性能。

Paul Watters是澳洲和新加坡上市的高等教育機構Academies Australasia Polytechnic的學術院長。Watters教授還是麥覺理大學安全研究和犯罪學的名譽教授，以及拉筹伯大学的网络安全兼职教授。他與澳洲的許多大公司和執法機構密切合作，從事應用的網絡安全研發項目，並在網絡安全、網絡犯罪和相關主題上撰寫了許多書籍和學術論文。他的研究已被引用了4964次，他的h指數為33。他在2000年獲得麥覺理大學的博士學位，在1997年完成了劍橋大學的MPhil學位，並在新南威爾士大學獲得了一級榮譽學士學位和紐卡斯爾大學的學士學位。Watters教授是英國計算機學會的會士，IEEE的高級會員，特許IT專業人士，以及澳大利亞心理學會的會員。