Context Aware Mobile Application Pre-Launching Model using KNN Classifier
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Malini Alagarsamy Department of Computer Science, Thiagarajar College of Engineering, India This email address is being protected from spambots. You need JavaScript enabled to view it. |
Ameena Sahubar Sathik Infosys Pvt Ltd, India This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: Mobile applications are the application software which can be executed in mobile devices. The Performance of the mobile application is major factor to be considered while developing the application software. Usually, the user uses a sequence of applications continuously. So, pre-launching of the mobile application is the best methodology used to increase the launch time of the mobile application. In Android Operating System (OS) they use cache policies to increase the launch time. But whenever a new application enters into the cache it removes the existing application from the cache even it is repeatedly used by the user. So the removed application needs to be re-launched again. To rectify it, we suggest K number of applications for pre-launching by calculating the affinity between the applications. Because, the user may uses the set of applications together for more than one time. We discover those applications from the usage pattern based on Launch Delay (LD), Power Consumption (PC), App Affinity, Spatial and Temporal relations and also, a K-Nearest Neighbour (KNN) classifier machine learning algorithm is used to increase the accuracy of prediction.
Keywords: Mobile application, launch time, app affinity, pre-launch, context-aware.
Received October 10, 2020; accepted December 14, 2021