2026-09-24T04:38:19Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/81818 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Han, Jiawei Li, Xiaolei 2015-09-25T20:20:34Z 2015-09-25T20:20:34Z 10000-01-01 2008 2008 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. Made available in DSpace on 2015-09-25T20:20:34Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3314964.pdf: 2246747 bytes, checksum: d901f3e1a2e1141532f7659814caff6e (MD5) Previous issue date: 2008 Embargo set by: Seth Robbins for item 83099 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 143 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. http://hdl.handle.net/2142/81818 (MiAaPQ)AAI3314964 eng Computer Science *Multidimensional Analysis of Moving Object Data text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.