Predicting Apps usage has become an important task due to the proliferation of Apps, and the complex of Apps. However, the previous research works utilized a considerable number of different sensors as training data to infer Apps usage. To save the energy consumption for the task of predicting Apps usages, only the temporal information is considered in this paper. We propose a Temporal-based Apps Predictor (abbreviated as TAP) to dynamically predict the Apps which are most likely to be used. First, we extract three Apps usage features, global usage feature, temporal usage feature, and periodical usage feature from the Apps usage trace. Then, based on those explored features, we dynamically derive an Apps usage probability model to estimate the current usage probability of each App in each feature. Finally, we investigate the usage probability in each feature and select k Apps with highest usage probability from the probability model. In this paper, we propose two selection algorithms, MaxProb and MinEntropy. To evaluate the performance of TAP, we use two real mobile Apps usage traces and assess the accuracy and efficiency. The experimental results show that the proposed TAP with the MinEntropy selection algorithm could have shorter response time of Apps prediction. Moreover, the accuracy reaches to 80% when k is 5, and when k is 7, the accuracy achieves almost 100% in both of the two real datasets.

With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. The results based on a real dataset demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.