Abstract
Smart mobiles as the most affordable and practical ubiquitous devices participate heavily in the enhancement of our daily life by the use of many convenient applications. However, the significant number of mobile users in addition to their heterogeneity (different profiles and contexts) obligates developers to enhance the quality of their apps by making them more intelligent and more flexible. This is realized mainly by analyzing mobile user’s data. Machine learning (ML) technology provides the methodology and techniques needed to extract knowledge from data to facilitate decision-making. Therefore, both developers and researchers affirm the benefits of combining ML techniques and mobile technology in several application fields as e-health, e-learning, e-commerce, and e-coaching. Thus, the purpose of this paper is to have an overview of the use of ML techniques in the design and development of mobile applications. Therefore, we performed a systematic mapping study of papers published on this subject in the period between 1 January 2007 and 31 December 2019. A total number of 71 papers were selected, studied, and analyzed according to the following criteria, year, sources and channel of publication, research type, and methods, kind of collected data, and finally adopted ML models, tasks, and techniques.
Highlights
Today, no one can deny that the most used ubiquitous systems are mobile devices
9167 papers were excluded after applying the exclusion criteria, leading to the identification of 71 articles regarding the use of Machine learning (ML) techniques in intelligent mobile app design and development
ML provides promising techniques to extract knowledge from a large dataset; the objective is to obtain patterns and models that will help in developing intelligent applications that will learn from data collected about user context, profile, and interactions
Summary
No one can deny that the most used ubiquitous systems are mobile devices. Nowadays mobiles are widely used in several challenging domains such as e-learning, e-health, e-commerce, e-travel, and e-coaching. From a simple container of thin clients, mobiles are transformed to a container of a huge number of mobile applications offering various services of our daily life. E total number of mobile app downloads has increased from 63 billion in 2015 to 2054 billion in 2018. It is expected that in 2022 this number will reach 2582 billion [1]. Since 2015, mobile phone usage exceeded desktop Internet usage, and by 2025, connected users will reach 75% of the world’s population [2]. Erefore, it is expected that by 2025 mobile data will constitute 18% of the global Datasphere [2] Since 2015, mobile phone usage exceeded desktop Internet usage, and by 2025, connected users will reach 75% of the world’s population [2]. erefore, it is expected that by 2025 mobile data will constitute 18% of the global Datasphere [2]
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