Introduction to Context-Aware Machine Learning and Mobile Data Analytics

Iqbal H. Sarker, Alan Colman, Jun Han and Paul Watters
Additional contact information
Iqbal H. Sarker: Swinburne University of Technology
Alan Colman: Swinburne University of Technology
Jun Han: Swinburne University of Technology
Paul Watters: Macquarie University

Chapter Chapter 1 in Context-Aware Machine Learning and Mobile Data Analytics, 2021, pp 3-13 from Springer

Abstract: 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.

Keywords: Context-aware computing; Machine learning; Smart decision-making; User behavior modeling; Data analytics; Rule-based systems; Intelligent applications (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-88530-4_1

Ordering information: This item can be ordered from
http://www.springer.com/9783030885304

DOI: 10.1007/978-3-030-88530-4_1

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().