Data Warehousing for Social Networking and Human Mobility Research and Detecting Real-World Events in Space and Time Using Feature Clustering

Doctoral Dissertation

Abstract

In this dissertation, we address two problems associated with the development of an emergency response system that utilizes a cell phone network as a sensor network to facilitate the initiation and online validation of predictive simulations. Schoenharl (2007) describes the simulation component of the emergency response system; this dissertation addresses two other significant components of the system: the historical data source and the detection and alert system.

The historical data source is a data warehouse designed to facilitate the development of additional simulation models for the predictive component of the system and has wider applications for scientific research on social networks and human mobility.

The detection and alert system provides an automatic mechanism for initiating the simulation system without intervention by emergency response managers. This system identifies potential emergency events in both time and space, allowing the simulations to begin shortly after an event as well as focus on the area affected by the event, reducing the computational costs significantly.

Attributes

Attribute NameValues
URN
  • etd-12102010-080012

Author Alec Pawling
Advisor Greg Madey
Contributor Amitabh Chaudhary, Committee Member
Contributor Greg Madey, Committee Chair
Contributor Christian Poellabauer, Committee Member
Contributor Nitesh Chawla, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Computer Science and Engineering
Degree Name PhD
Defense Date
  • 2009-12-07

Submission Date 2010-12-10
Country
  • United States of America

Subject
  • data warehousing

  • data streams

  • data clustering

Publisher
  • University of Notre Dame

Language
  • English

Record Visibility and Access Public
Content License
  • All rights reserved

Departments and Units

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