People around the world are more connected today than ever before. By making phone calls, sending text messages and participating in online chats, mobile users are frequently interacting with their social connections through multiple communication channels. This trend is expected to continue with the emergence of immensely popular communication apps on mobile devices. Intuitively, these interactions on users' mobile phones can reveal valuable information regarding their social relationship with their phone contacts. Understanding such relationship can help provide new services and improve users' mobile experience. In this paper, we explore the opportunity to deeply understand these social relationship through mining mobile communication data. By building an on-device mining framework called Commsense, we show that automatically learning and understanding such relationship can efficiently support useful applications such as categorizing mobile contacts, identifying their relative importance, and automatically managing mobile contacts with very little human interference.

Smartphones can collect considerable context data about the user, ranging from apps used to places visited. Frequent user patterns discovered from longitudinal, multi-modal context data could help personalize and improve overall user experience. Our long term goal is to develop novel middleware and algorithms to efficiently mine user behavior patterns entirely on the phone by utilizing idle processor cycles. Mining patterns on the mobile device provides better privacy guarantees to users, and reduces dependency on cloud connectivity. As an important step in this direction, we develop a novel general-purpose service called MobileMiner that runs on the phone and discovers frequent co-occurrence patterns indicating which context events frequently occur together. Using longitudinal context data collected from 106 users over 1--3 months, we show that MobileMiner efficiently generates patterns using limited phone resources. Further, we find interesting behavior patterns for individual users and across users, ranging from calling patterns to place visitation patterns. Finally, we show how our co-occurrence patterns can be used by developers to improve the phone UI for launching apps or calling contacts.

Energy conservation on mobile devices is now more important than ever due to the increasing benefits that smartphones and tablets provide to our daily life. However, most existing power management approaches either focus narrowly on a particular sub-system of the mobile device such as the sensor system, the LCD display, or the communication system, or use heuristic approaches to maximize energy efficiency at the cost of user experience. In this paper, we present Boe, a context-aware global power management scheme for mobile devices Balancing battery outage and user experience. To meet the mobile device's expected battery life while sacrificing end user experience as little as possible. Boe takes into account the users' phone usage patterns and activities to dynamically adjust the device's global power management policy to minimize outage time and maximize user experience. We demonstrate our proposed technique by controlling display brightness level and GPS sampling rate on smartphones. We evaluate our approach through real world smartphone data from 10 users over two months. Compared to the best fixed user experience policies, we show that: (i) Boe eliminates all frustrating battery outage events for light, moderate, and heavy phone users, and (ii) Boe improves user experience by 20% for light users, maintains the same user experience for moderate users, and degrades user experience by 23% for heavy smartphone users.

Ubiquitous, context-aware computer systems may ultimately enable computer applications that naturally and usefully respond to a user's everyday activity. Although new algorithms that can automatically detect context from wearable and environmental sensor systems show promise, many of the most flexible and robust systems use probabilistic detection algorithms that require extensive libraries of training data with labeled examples. In this paper, we describe the need for such training data and some challenges we have identified when trying to collect it while testing three context-detection systems for ubiquitous computing and mobile applications.