Predictive modeling of everyday behavior from large-scale data

Modeling Users' Behavior from Large Scale Smartphone Data Collection

A large volume of research in ubiquitous systems has been devoted to using data, that has been sensed from users’ smartphones, to infer their current high level context and activities. However, mining users’ diverse longitudinal behavioral patterns, which can enable exciting new context-aware applications, has not received much attention. In this paper, we focus on learning and identifying such...

Statistical Modeling of Large-Scale Simulation Data

With the advent of fast computer systems, scientists are now able to generate terabytes of simulation data. Unfortunately, the sheer size of these data sets has made efficient exploration of them impossible. To aid scientists in gleaning insight from their simulation data, we have developed an ad-hoc query infrastructure. Our system, called AQSim (short for Ad-hoc Queries for Simulation) reduce...

Chapter 19: Predictive Modelling of Student Behavior Using Granular Large-Scale Action Data

User Behavior Modeling with Large-Scale Graph Analysis

Can we model how fraudsters work to distinguish them from normal users? Can we predict not just which movie a person will like, but also why? How can we find when a student will become confused or where patients in a hospital system are getting infected? How can we effectively model large attributed graphs of complex interactions? In this dissertation we understand user behavior through modelin...

An Efficient Data Replication Strategy in Large-Scale Data Grid Environments Based on Availability and Popularity

The data grid technology, which uses the scale of the Internet to solve storage limitation for the huge amount of data, has become one of the hot research topics. Recently, data replication strategies have been widely employed in distributed environment to copy frequently accessed data in suitable sites. The primary purposes are shortening distance of file transmission and achieving files from ...