Context-Aware Machine Learning and Mobile Data Analytics

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

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