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University of Illinois at Urbana-Champaign

*Multidimensional Analysis of Moving Object Data

Abstract

dc:description

To this end, we present our studies in this thesis. With regards to anomaly detection, we present three models to automatically detect moving object anomaly, traffic anomaly, and subspace anomaly. The last of which detects anomalies in a multidimensional space, which is often the case in real world datasets. Additionally, we also address problems that could occur due to sampling in a multidimensional space and how to summarize moving object trajectories for more efficient processing.

Degree

thesis:*
Name thesis:degree_name
Ph.D.
Level thesis:degree_level
Dissertation
Discipline thesis:degree_discipline
Computer Science
Grantor
University of Illinois at Urbana-Champaign
Year dc:date
2015

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Li, Xiaolei
Contributors dc:contributor
  • Han, Jiawei

Subjects

dc:subject × 1

Rights

Language dc:language
eng

Identifiers

dc:identifier.*
Identifier
(MiAaPQ)AAI3314964
OAI identifier oai:identifier
oai:www.ideals.illinois.edu:2142/81818

Chain of custody

source
Harvested from
University of Illinois - Urbana-Champaign
Base URL
www.ideals.illinois.edu/oai-pmh
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Li, Xiaolei. *Multidimensional Analysis of Moving Object Data. Dissertation thesis, University of Illinois at Urbana-Champaign, 2015. http://hdl.handle.net/2142/81818