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Introduction to Context-Aware Machine Learning and Mobile Data Analytics

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Context-Aware Machine Learning and Mobile Data Analytics

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Abstract

The concept of context-aware computing has grown in popularity in recent years, especially with the current evolution of smart mobile devices. Recent advancements in smartphones and their sensing capabilities have made the devices enable to collect the rich contextual information, such as external and internal context, as well as phone usage records of users in various day-to-day situations. Individuals’ cell phone usage patterns can vary significantly in the real world, behaving differently in various contexts—for example, temporal, spatial, social, or relevant others. Extracting insights or useful knowledge, e.g., rules, from the contextual data can be used to build data-driven intelligent context-aware models or systems for smart and automated decision-making, where machine learning technologies are the key. The prominent application fields of context-aware machine learning modeling are many, but not limited to personalized assistance services, recommendation systems, human-centric computing, adaptive and intelligent systems, IoT services, smart cities as well as mobile privacy and security systems. Thus a study on context-aware machine learning modeling utilizing users’ mobile phone data is important, which can make a vital turn in the way of interaction among people and mobile devices in our real-world life. In this book, we have bestowed a comprehensive survey on this topic through a context-aware machine learning framework that explores multi-dimensional contexts in machine learning modeling, context discretization with time-series modeling, contextual rule discovery and predictive analytics, and recent-pattern or rule-based behavior modeling, to provide intelligent services. Furthermore, we have also discussed how the extracted contextual rules can play a vital role to build a context-aware expert system for mobile devices. We have also explored the importance of deep neural network learning in the area. Finally, we have summarized this book highlighting several real-world context-aware applications that intelligently assist individual smartphone users in their everyday activities, as well as prospective research works and challenges in the field of context-aware machine learning and mobile data analytics.

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Authors and Affiliations

  1. Swinburne University of Technology, Melbourne, VIC, Australia

    Iqbal H. Sarker, Alan Colman & Jun Han

  2. Chittagong University of Engineering & Technology, Chittagong, Bangladesh

    Iqbal H. Sarker

  3. Macquarie University, Sydney, NSW, Australia

    Paul Watters

  4. Cyberstronomy Pty Ltd, Melbourne, Australia

    Paul Watters

Authors
  1. Iqbal H. Sarker
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  2. Alan Colman
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  3. Jun Han
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  4. Paul Watters
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Sarker, I.H., Colman, A., Han, J., Watters, P. (2021). Introduction to Context-Aware Machine Learning and Mobile Data Analytics. In: Context-Aware Machine Learning and Mobile Data Analytics. Springer, Cham. https://doi.org/10.1007/978-3-030-88530-4_1

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