Files in this item

Files Description Format
untranslated Liao_Binbin.pdf (1MB) (no description provided) PDF

Description

Title: Anomaly detection in GPS data based on visual analytics
Author(s): Liao, Binbin
Advisor(s): Yu, Yizhou
Department / Program: Computer Science
Discipline: Computer Science
Degree Granting Institution: University of Illinois at Urbana-Champaign
Degree: M.S.
Genre: Thesis
Subject(s): Visual Analytics
Anomaly Detection
Conditional Random Fields
Active Learning
Information Visualization
Human-Computer Interaction
Abstract: Modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction, while human experts hold the advantage of possessing high-level intelligence and domain-specific expertise. We combine the power of the two for anomaly detection in GPS data by integrating them through a visualization and human-computer interaction interface. In this thesis we introduce GPSvas (GPS Visual Analytics System), a system that detects anomalies in GPS data using the approach of visual analytics: a conditional random field (CRF) model is used as the machine learning component for anomaly detection in streaming GPS traces. A visualization component and a user-friendly interaction interface are built to visualize the data stream, display significant analysis results (i.e., anomalies or uncertain predications) and hidden information extracted by the anomaly detection model, which enable human experts to observe the real-time data behavior and gain insights into the data flow. Human experts further provide guidance to the machine learning model through the interaction tools; the learning model is then incrementally improved through an active learning procedure.
Issue Date: 2010-05-19
URI: http://hdl.handle.net/2142/16162
Rights Information: Copyright 2010 Binbin Liao
Date Available in IDEALS: 2010-05-19
Date Deposited: May 2010


This item appears in the following Collection(s)

Item Statistics

  • Total Downloads: 331
  • Downloads this Month: 5
  • Downloads Today: 2