Various studies have investigated the predictability of different aspects of human behavior
such as mobility patterns, social interactions, and shopping and online behaviors. However …

This thesis presents a mobile instrumentation middleware, AWARE, aimed at facilitating our
understanding of human behavior. We demonstrate how to use AWARE to build context …

The collection of huge amount of tracking data made possible by the widespread use of GPS
devices, enabled the analysis of such data for several applications domains, ranging from …

Data reflecting movements of people, such as GPS or GSM tracks, can be a source of
information about mobility behaviors and activities of people. Such information is required …

Predicting mobility-related behavior is an important yet challenging task. On the one hand,
factors such as one's routine or preferences for a few favorite locations may help in …

There is relatively little work on the investigation of large-scale human data in terms of
multimodality for human activity discovery. In this paper, we suggest that human interaction …

We provide a novel algorithm for the discovery of mobility patterns and prediction of users'
destination locations, both in terms of geographic coordinates and semantic meaning. We …

This paper introduces a framework for inferring human activities in mobile devices by
computing spatial contexts, temporal contexts, spatiotemporal contexts, and user contexts. A …

The massive amounts of geolocation data collected from mobile phone records has sparked
an ongoing effort to understand and predict the mobility patterns of human beings. In this …

Human mobility is important for understanding the evolution of size and structure of urban
areas, the spatial distribution of facilities, and the provision of transportation services. Until …