Modeling Users' Behavior from Large Scale Smartphone Data Collection.

A large volume of research in ubiquitous systems has been devoted to using data, that has been sensed from users’ smartphones, to infer their current high level context and activities. However, mining users’ diverse longitudinal behavioral patterns, which can enable exciting new context-aware applications, has not received much attention. In this paper, we focus on learning and identifying such behavioral patterns from large-scale data collected from users’ smartphones. To this end, we develop a unified infrastructure and implement several novel approaches for building diverse behavioral models of users. Examples of generated models include classifying users’ semantic places and predicting their availability for accepting calls etc. We evaluate our work on real-world data of 200 users, from the DeviceAnalyzer dataset, consisting of 365 million data points and show that our algorithms and approaches can model user behavior with high accuracy and outperform existing approaches.