Smartphones nowadays have become indispensable personal gadgets to support our
activities in almost every aspect of our lives. Thanks to the tremendous advancement of …

The trucking industry urgently requires comprehensive methods to evaluate driver safety,
given the high incidence of serious traffic accidents involving trucks. The concept of a “truck …

Location-based social networks (LBSNs) offer researchers rich data to study people's online
activities and mobility patterns. One important application of such studies is to provide …

Real-life mobile phone data may contain noisy instances, which is a fundamental issue for
building a prediction model with many potential negative consequences. The complexity of …

This work introduces a set of scalable algorithms to identify patterns of human daily
behaviors. These patterns are extracted from multivariate temporal data that have been …

Mobile phones can record individual's daily behavioral data as a time-series. In this paper,
we present an effective time-series segmentation technique that extracts optimal time …

Mobile recommender systems target on recommending the right product or information to
the right mobile users at anytime and anywhere. It is well known that the contextual …

The study of the use of mobile Apps plays an important role in understanding the user
preferences, and thus provides the opportunities for intelligent personalized context-based …

MOOCs attract diverse users with varying habits. Identifying those patterns through
clickstream analysis could enable more effective personalized support for student …

Predicting Apps usage has become an important task due to the proliferation of Apps, and
the complex of Apps. However, the previous research works utilized a considerable number …