Anomaly detection in GPS data based on visual analytics

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2010

Author dc:creator

• Liao, Binbin

Contributors dc:contributor

• Yu, Yizhou

• Visual Analytics
• Anomaly Detection
• Conditional Random Fields
• Active Learning
• Information Visualization
• Human-Computer Interaction

Statement dc:rights

• Copyright 2010 Binbin Liao

Language dc:language

en

Handle dc:identifier

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

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/16162