A global local modeling of internet usage in large mobile societies

  • Authors:

  • Abdullah Almutairi;Manas Somaiya;Saeed Moghaddam;Sanjay Ranka;Ahmed Helmy

  • Affiliations:

  • University of Florida, Gainesville, FL, USA;University of Florida, Gainesville, FL, USA;University of Florida, Gainesville, FL, USA;University of Florida, Gainesville, FL, USA;University of Florida, Gainesville, FL, USA

  • Venue:
  • Proceedings of the 7th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

Real-world wireless Internet usage data for any user is typically generated via an overlap of many correlations. These correlations could be based on hobbies (e.g. sports fan), profession (e.g. work e-mail), day-to-day activities (e.g. news, Internet banking), communication (e.g. instant messaging, social networks), etc. The likelihood of appearance of these correlations in usage data may be influenced by the type of location the user is in. Hobbies and communication related web sites would be more likely to be accessed at home, Profession related web sites would usually be accessed at work. Understanding and capturing this generative process that is based on human interests, behavior and location is the key to the design of future mobile networks. We propose a novel Bayesian mixture model called the "Global Local' model based on the "POWER" model that can realistically describe Internet usage and correlations with various locations inside a large mobile society. The "POWER" model is a new class of mixture models where components compete to produce a single data point, this model allows for the discovery of complex overlapping patterns of user's Internet behavior. The "Global Local" model learns a global template of user's Internet behavior patterns using the "POWER" model first, then learns correlations between the templates and locations inside a large mobile society. We design a learning algorithm that can effectively learn the "Global Local" model from Internet usage data, and demonstrate its capabilities using synthetic data. Finally, we analyze a real-world Internet usage data for thousands of users collected via wireless LAN traces and discover many interesting correlations that can be explained very intuitively.