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Fast Anomaly Detection on Multiple Multi-dimensional Data Streams

Conference Paper


Abstract


  • Multiple multi-dimensional data streams are ubiquitous

    in the modern world, such as IoT applications, GIS

    applications and social networks. Detecting anomalies in such

    data streams in real-time is an important and challenging task.

    It is able to provide valuable information from data and then

    assists decision-making. However, exiting approaches for anomaly

    detection in multi-dimensional data streams have not properly

    considered the correlations among multiple multi-dimensional

    streams. Moreover, for multi-dimensional streaming data, online

    detection speed is often an important concern. In this paper,

    we propose a fast yet effective anomaly detection approach

    in multiple multi-dimensional data streams. This is based on

    a combination of ideas, i.e., stream pre-processing, locality

    sensitive hashing and dynamic isolation forest. Experiments

    on real datasets demonstrate that our approach achieves a

    magnitude increase in its efficiency compared with state-of-theart

    approaches while maintaining competitive detection accuracy.

Authors


  •   Sun, Hongyu (external author)
  •   He, Qiang (external author)
  •   Liao, Kewen (external author)
  •   Sellis, Timoleon (external author)
  •   Guo, Longkun (external author)
  •   Zhang, Xuyun (external author)
  •   Shen, Jun
  •   Chen, Feifei (external author)

Publication Date


  • 2019

Citation


  • Sun, H., He, Q., Liao, K., Sellis, T., Guo, L., Zhang, X., Shen, J. & Chen, F. (2019). Fast Anomaly Detection on Multiple Multi-dimensional Data Streams. IEEE International Conference on Big Data (Big Data 2019) (pp. 1218-1223). United States: IEEE.

Start Page


  • 1218

End Page


  • 1223

Place Of Publication


  • United States

Abstract


  • Multiple multi-dimensional data streams are ubiquitous

    in the modern world, such as IoT applications, GIS

    applications and social networks. Detecting anomalies in such

    data streams in real-time is an important and challenging task.

    It is able to provide valuable information from data and then

    assists decision-making. However, exiting approaches for anomaly

    detection in multi-dimensional data streams have not properly

    considered the correlations among multiple multi-dimensional

    streams. Moreover, for multi-dimensional streaming data, online

    detection speed is often an important concern. In this paper,

    we propose a fast yet effective anomaly detection approach

    in multiple multi-dimensional data streams. This is based on

    a combination of ideas, i.e., stream pre-processing, locality

    sensitive hashing and dynamic isolation forest. Experiments

    on real datasets demonstrate that our approach achieves a

    magnitude increase in its efficiency compared with state-of-theart

    approaches while maintaining competitive detection accuracy.

Authors


  •   Sun, Hongyu (external author)
  •   He, Qiang (external author)
  •   Liao, Kewen (external author)
  •   Sellis, Timoleon (external author)
  •   Guo, Longkun (external author)
  •   Zhang, Xuyun (external author)
  •   Shen, Jun
  •   Chen, Feifei (external author)

Publication Date


  • 2019

Citation


  • Sun, H., He, Q., Liao, K., Sellis, T., Guo, L., Zhang, X., Shen, J. & Chen, F. (2019). Fast Anomaly Detection on Multiple Multi-dimensional Data Streams. IEEE International Conference on Big Data (Big Data 2019) (pp. 1218-1223). United States: IEEE.

Start Page


  • 1218

End Page


  • 1223

Place Of Publication


  • United States