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A Literature Review on Context-Aware Machine Learning and Mobile Data Analytics

In: Context-Aware Machine Learning and Mobile Data Analytics

Author

Listed:
  • Iqbal H. Sarker

    (Swinburne University of Technology
    Chittagong University of Engineering & Technology)

  • Alan Colman

    (Swinburne University of Technology)

  • Jun Han

    (Swinburne University of Technology)

  • Paul Watters

    (Macquarie University
    Cyberstronomy Pty Ltd)

Abstract

This chapter presents a review and discussion of related work from various areas within the scope of this book, which is based on the elements of the context-aware machine learning framework presented in the earlier chapter. It covers contextual information in mobile phone data, context discretization, and time-series modeling techniques, rule discovery techniques including association rules and classification rules, dynamic rule updating and management techniques including incremental rule mining, and recent log-based mining techniques with relevant applications for the end mobile phone users. The study also identifies the key research areas where current solutions fall short of the requirements for identifying contextual behavioral rules of individual mobile phone users. We also highlight the limitations of previous work in the field of context-aware computing, which motivates the need for further study based on machine learning techniques.

Suggested Citation

  • Iqbal H. Sarker & Alan Colman & Jun Han & Paul Watters, 2021. "A Literature Review on Context-Aware Machine Learning and Mobile Data Analytics," Springer Books, in: Context-Aware Machine Learning and Mobile Data Analytics, chapter 0, pages 23-56, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-88530-4_3
    DOI: 10.1007/978-3-030-88530-4_3
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