University of Illinois Urbana-Champaign

Anomaly detection in GPS data based on visual analytics

Liao, Binbin

Content Files
Liao_Binbin.pdf

Permalink

https://hdl.handle.net/2142/16162

Description

Title
Anomaly detection in GPS data based on visual analytics
Author(s)
Liao, Binbin
Issue Date
2010-05-19T18:39:23Z
Director of Research (if dissertation) or Advisor (if thesis)
Yu, Yizhou
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2010-05-19T18:39:23Z
Keyword(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.
Graduation Semester
2010-5
Permalink
http://hdl.handle.net/2142/16162
Copyright and License Information
Copyright 2010 Binbin Liao

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