Fast Anomaly Detection in Multiple Multi-Dimensional Data Streams

Sun, Hongyu, He, Qiang, Liao, Kewen, Sellis, Timos, Guo, Longkun, Zhang, Xuyun, Shen, Jun and Chen, Feifei 2019, Fast Anomaly Detection in Multiple Multi-Dimensional Data Streams, in Big Data 2019 : Proceedings of the IEEE International Conference on Big Data, IEEE, Piscataway, N.J., pp. 1218-1223, doi: 10.1109/bigdata47090.2019.9006354.

Attached Files
Name Description MIMEType Size Downloads

Title Fast Anomaly Detection in Multiple Multi-Dimensional Data Streams
Author(s) Sun, Hongyu
He, Qiang
Liao, Kewen
Sellis, Timos
Guo, Longkun
Zhang, Xuyun
Shen, Jun
Chen, FeifeiORCID iD for Chen, Feifei orcid.org/0000-0001-5455-3792
Conference name IEEE International Conference on Big Data (2019 : Los Angeles, California)
Conference location Los Angeles, California
Conference dates 12-19 Dec. 2019
Title of proceedings Big Data 2019 : Proceedings of the IEEE International Conference on Big Data
Publication date 2019
Start page 1218
End page 1223
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Anomaly Detection
Multi-Dimensional Data Streams
Locality Sensitive Hashing
Isolation Forest
Unsupervised Learning
Language eng
DOI 10.1109/bigdata47090.2019.9006354
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30135536

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 56 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Tue, 10 Mar 2020, 08:47:36 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.