This brief surveys existing techniques to address the problem of long delays and high power consumption for web browsing on smartphones, which can be due to the local computational limitation at the smartphone (e.g., running java scripts or flash objects) level. To address this issue, an architecture called Virtual-Machine based Proxy (VMP) is introduced, shifting the computing from smartphones to the VMP which may reside in the cloud. Mobile Web Browsing Using the Cloud illustrates the feasibility of deploying the proposed VMP system in 3G networks through a prototype using Xen virtual machines (in cloud) and Android Phones with ATT UMTS network. Techniques to address scalability issues, resource management techniques to optimize the performance of the VMs on the proxy side, compression techniques to further reduce the bandwidth consumption, and adaptation techniques to address poor network conditions on the smartphone are also included.

This volume focuses on predicting users' attendance at a future event at specific time and location based on their common interests. Event attendance prediction has attracted considerable attention because of its wide range of potential applications. By predicting event attendance, events that better fit users' interests can be recommended, and personalized location-based or topic-based services related to the events can be provided to users. Moreover, it can help event organizers estimating the event scale, identifying conflicts, and help manage resources. This book first surveys existing techniques on event attendance prediction and other related topics in event-based social networks. It then introduces a context-aware data mining approach to predict the event attendance by learning how users are likely to attend future events. Specifically, three sets of context-aware attributes are identified by analyzing users' past activities, including semantic, temporal, and spatial attributes. This book illustrates how these attributes can be applied for event attendance prediction by incorporating them into supervised learning models, and demonstrates their effectiveness through a real-world dataset collected from event-based social networks.