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Study Area Recommendation via Network Log Analytics

With the current surge in the number of devices connected to the Internet all over the world, usage of network bandwidth has also seen a huge increase. This has led to network traffic congestion and slowdowns. It is increasingly important to anticipate future network usage, which would help prepare and provide adequate infrastructure or service support. In this paper, we propose a model that uses machine learning techniques to analyze network logs of access points installed throughout the campus of San José State University (SJSU) to predict its network usage at a future point in time. We also propose to build a classification model to classify user's browsing behavior as either study or leisure, based on the content of the pages they view. Hence, the final model will be able to predict network throughput of access points and the type of activity they will be used for. We utilize these models in a novel mobile phone application we designed that helps students choose an appropriate place to study or hang out at a chosen time in the near future. Our application combines the use of mobile phone sensors for localization with the power of machine learning models executed in the cloud to provide a seamless user experience for students and faculty alike.