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Mobile phones have evolved from communication devices to indispensable accessories with access to real-time content. The increasing reliance on dynamic content comes at the cost of increased latency to pull the content from the Internet before the user can start using it. While prior work has explored parts of this problem, they ignore the bandwidth costs of prefetching, incur significant training overhead, need several sensors to be turned on, and do not consider practical systems issues that arise from the limited background processing capability supported by mobile operating systems. In this paper, we make app prefetch practical on mobile phones. Our contributions are twofold. First, we design an app prediction algorithm, APPM, that requires no prior training, adapts to usage dynamics, predicts not only which app will be used next but also when it will be used, and provides high accuracy without requiring additional sensor context. Second, we perform parallel prefetch on screen unlock, a mechanism that leverages the benefits of prediction while operating within the constraints of mobile operating systems. Our experiments are conducted on long-term traces, live deployments on the Android Play Market, and user studies, and show that we outperform prior approaches to predicting app usage, while also providing practical ways to prefetch application content on mobile phones.

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The number of available mobile applications is steadily increasing. People have rapidly adopted application stores as means to customize their devices with various functionalities that go beyond communication. Understanding the principles of mobile application usage is crucial for supporting users within this new ecosystem. In this paper, we investigate how people organize applications they have installed on their devices. We asked more than 130 participants for their habits for icon arrangement and collected more than 1,400 screenshots of their devices' menus to further ground our findings. Based on this data we can distinguish five different concepts for arranging icons on smartphone menus, e.g. based on application usage frequency and applications' functional relatedness. Additionally, we investigated how these concepts emerge in relation to frequency of application installations, removals and icon rearrangements, as well as users' experience levels. Finally we discuss implications for the design of smartphone launchers, and highlight differences to icon arrangement on stationary computers.