Streaming Outlier Detection
Streaming Outlier Detection is a method for identifying anomalous data instances in continuous, high-velocity data streams where observations are processed sequentially and often in real time, distinguishing it from batch outlier detection by its constraints on memory, computational efficiency, and non-stationary data distribution.
244
Publications
11.7K
Citations
690
Authors
323
Institutions
Publications per year
2017–2026
132
Parents
244
Density-based clustering for real-time stream data
Yixin Chen, Li Tu · 2007 · 564 citations
Anomaly Detection in Streams with Extreme Value Theory
Alban Siffer, Pierre-Alain Fouque, Alexandre Termier et al. · 2017 · 517 citations · Full text
Computing, Finance, Data Mining +12
Detecting distance-based outliers in streams of data
Fabrizio Angiulli, Fabio Fassetti · 2007 · 239 citations
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323

Worcester Polytechnic Institute
Worcester, United States
7 h-index
16 publications
1.2K citations
China Agricultural University
Beijing, China
6 h-index
29 publications
509 citations

Indian Institute of Technology Roorkee
Roorkee, India
5 h-index
10 publications
272 citations
Aristotle University of Thessaloniki
Thessaloniki, Greece
5 h-index
24 publications
1.6K citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
5 h-index
11 publications
286 citations
Lancaster University
Lancaster, United Kingdom
5 h-index
7 publications
404 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
11 publications
890 citations
The University of Texas at Dallas
Richardson, United States
4 h-index
14 publications
763 citations
1–8 of 323
11 publications · 348 citations
11
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7 publications · 197 citations
6
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5 publications · 151 citations
5
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TKDE · 5 publications · 227 citations
5
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PVLDB · 5 publications · 291 citations
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