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

Authors

690

  1. SC
    Saihua Cai

    Jiangsu University, China

    7 publications · 116 citations

    7

    h-index

  2. EA
    Elke A. Rundensteiner

    Worcester Polytechnic Institute, United States

    7 publications · 429 citations

    7

    h-index

  3. RS
    Ruizhi Sun

    China Agricultural University, China

    6 publications · 104 citations

    6

    h-index

  4. LC
    Lei Cao

    Worcester Polytechnic Institute, United States

    6 publications · 308 citations

    6

    h-index

  5. GY
    Gang Yuan

    China Agricultural University, China

    6 publications · 104 citations

    6

    h-index

1–5 of 690

Venues

  1. Expert Systems with Applications

    11 publications · 348 citations

    11

    h-index

  2. Information Sciences

    7 publications · 197 citations

    6

    h-index

  3. Knowledge-Based Systems

    5 publications · 151 citations

    5

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 5 publications · 227 citations

    5

    h-index

  5. Proceedings of the VLDB Endowment

    PVLDB · 5 publications · 291 citations

    5

    h-index

1–5