Abstract
The staggering growth in smartphone and wearable device use has led to a massive scale generation of personal (user-specific) data. To explore, analyze, and extract useful information and knowledge from the deluge of personal data, one has to leverage these devices as the data-mining platforms in ubiquitous, pervasive, and big data environments. This study presents the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity. An extensive review on recent literature has been conducted and a detailed taxonomy is presented. The performance evaluation metrics and their empirical evidences are sorted out in this paper. Finally, we have highlighted some future research directions and potentially emerging application areas for personal data mining using smartphones and wearable devices.
Highlights
Every day, billions of user-specific data points are generated by personal sensing devices (PSDs), such as smartphones and wearable devices, known as resource-constrained environments (RCEs) [1].The MIT Technology Review reports 99.5% of newly created digital data remains unanalyzed [2]
Accuracy is the basic criterion for the selection of classification algorithms, and classification time is the primary contributor in time complexity
Both of these metrics are usually considered in the performance evaluation of any classification algorithm
Summary
The MIT Technology Review reports 99.5% of newly created digital data remains unanalyzed [2] This technological advancement presents an opportunity to quantify each second of humane life, allowing information to be obtained by analyzing data from our bodies and daily activities. These personal data can be exploited by data mining algorithms to discover hidden knowledge patterns, which may include frequent activities, classification of physiological data, and clusters of mobile trajectories. On-board sensing data sources include a huge variety of sensors for sensing contextual and physiological information, locations, and environments [1]. Data preparation is performed at this layer by applying windowing models or distributing the data in sized chunks for further online data analysis
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