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, 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 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 10 Mar 2020, 08:47:36 EST Wed, 11 Mar 2020, 04:00:32 EST Wed, 11 Mar 2020, 05:04:16 EST Sat, 04 Apr 2020, 01:34:50 EST Wed, 08 Apr 2020, 11:43:56 EST Wed, 08 Apr 2020, 11:46:49 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 56 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Tue, 10 Mar 2020, 08:47:36 EST

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