Prashant Kumar Ray
Uncovering Latent Patterns In Service-Level Spatiotemporal Mobile Traffic.
Rel. Luca Vassio. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2023
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Abstract
Personal mobile communication technologies are amongst the most successful innovations of the 21st century. The widespread adoption of mobile services has resulted in an exponential surge in mobile traffic, which effectively mirrors human behavior. Mobile devices maintain a continuous interaction with network infrastructure, and the associated geo-referenced events can be easily logged by service providers, for different purposes, including billing and resource management. This leads to the implicit potential of monitoring a significant portion of the entire population at a minimal cost which no other technology provides an equivalent coverage. In this context, analyzing mobile traffic along space, time, and app dimensions can provide actionable insights for improving user experience, optimizing resource allocation, enhancing security, and driving business success in the mobile app and service industry.
In this thesis, we employ a tensor decomposition technique, specifically Tucker decomposition, to reveal latent factors (i.e., patterns) spanning the dimensions of space, time, and applications
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