A data stream is an infinite sequence of data points generated from a source continuously at a fast rate, which is characterized by the transiency of the data points, the temporal relationship among the data points, concept drift, and multi-dimensionality of data points. Outlier detection in data streams thus needs to deal with the characteristics of Big Data applications such as volume, velocity, and variety. The problem of detecting outliers in multiple concurrent data streams introduces additional challenges to the problem. In this paper, we propose a parallel outlier detection technique CODS to detect Contextual Outliers in multiple concurrent independent multi-dimensional Data Streams using a Graphics Processing Unit (GPU). The proposed algorithm addresses all the aforesaid characteristics of data streams. A set of experiments demonstrates reasonable outlier detection accuracy and scalability of CODS with the number of data streams.
|Original language||English (US)|
|Title of host publication||Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021|
|Editors||Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - 2021|
|Event||2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States|
Duration: Dec 15 2021 → Dec 18 2021
|Name||Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021|
|Conference||2021 IEEE International Conference on Big Data, Big Data 2021|
|Period||12/15/21 → 12/18/21|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work is supported in part by the National Foundation under Grant No. 1302439 and 1302423.
This work is supported in part by the National Science Foundation under Grant No. 1302439 and 1302423.
© 2021 IEEE.
- Contextual Outlier
- Data Stream
- Outlier Detection