Search

• We perform a detailed analysis of mobile user behaviors. • We propose an app-prediction framework to effectively improve the prediction accuracy and reduce the training costs. • We conduct evaluations to verify the effectiveness of HiNextApp and analyze the overhead of the framework. A variety of applications (App) installed on mobile systems such as smartphones enrich our lives, but make it more difficult to the system management. For example, finding the specific Apps becomes more inconvenient due to more Apps installed on smartphones, and App response time could become longer because of the gap between more, larger Apps and limited memory capacity. Recent work has proposed several methods of predicting next used Apps in the immediate future (here in after app-prediction) to solve the issues, but faces the problems of the low prediction accuracy and high training costs. Especially, applying app-prediction to memory management (such as LMK) and App prelaunching has high requirements for the prediction accuracy and training costs. In this paper, we propose an app-prediction framework, named HiNextApp , to improve the app-prediction accuracy and reduce training costs in mobile systems. HiNextApp is based on contextual information, and can adjust the size of prediction periods adaptively. The framework mainly consists of two parts: non-uniform Bayes model and an elastic algorithm. The experimental results show that HiNextApp can effectively improve the prediction accuracy and reduce training times. Besides, compared with traditional Bayes model, the overhead of our framework is relatively low. [ABSTRACT FROM AUTHOR]