Skip to main content
SpringerLink
Log in
Book cover

Encyclopedia of Database Systems pp 108–111Cite as

Anomaly Detection on Streams

  • Reference work entry
  • First Online:

  • 40 Accesses

Definition

Anomaly detection generally refers to the process of automatically detecting events or behaviors which deviate from those considered normal. It is an unsupervised process, and can thus detect anomalies which have not been previously encountered. It is based on estimating a model of typical behavior from past observations and consequently comparing current observations against this model. It can be performed either on a single stream or among multiple streams. Anomaly detection encompasses outlier detection as well as change detection and therefore is closely related to forecasting and clustering methods.

Historical Background

Anomaly detection in streams has close connections to traditional outlier detection, as well as to change detection. The former is a common and widely studied topic in statistics [11]. The latter emerged in the context of statistical monitoring and control for continuous processes and the widely used CUSUM algorithm was proposed as early as 1954 [9]....

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   6,499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Recommended Reading

  1. Aggarwal CC, Han J, Wang J, and Yu PS. A Framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases; 2003. p. 81–92.

    Chapter  Google Scholar 

  2. Aggarwal CC, Yu PS. A survey of synopsis construction in data streams. In: Data streams: models and algorithms. New York: Springer; 2007.

    Chapter  MATH  Google Scholar 

  3. Cormode G, Muthukrishnan S. What’s new: finding significant differences in network data streams. IEEE/ACM Trans Netw. 2005;13(6):1219–32.

    Article  Google Scholar 

  4. Guha S, Meyerson A, Mishra N, Motwani R, O’Callaghan L. Clustering data streams: theory and practice. IEEE Trans Knowl Data Eng. 2003;15(3):515–28.

    Article  Google Scholar 

  5. Hulten G, Spencer L, and Domingos P. Mining time-changing data streams. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2001. p. 97–106.

    Google Scholar 

  6. Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM Comput Surv. 1999;31(3):264–323.

    Article  Google Scholar 

  7. Kleinberg J. Bursty and hierarchical structure in streams. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2002. p. 91–101.

    Google Scholar 

  8. Lee W, Stolfo SJ, Mok KW. Adaptive intrusion detection: a data mining approach. Artif Intell Rev. 2000;14(6):533–67.

    Article  MATH  Google Scholar 

  9. Page ES. Continuous inspection schemes. Biometrika. 1954;41(1):100–15.

    Article  MathSciNet  MATH  Google Scholar 

  10. Papadimitriou S, Sun J, and Faloutsos C. Streaming pattern discovery in multiple time-series. In: Proceedings of the 31st International Conference on Very Large Data Bases; 2005. p. 697–708.

    Google Scholar 

  11. Peter JR, Annick ML. Robust regression and outlier detection. New York: Wiley; 1987.

    MATH  Google Scholar 

  12. Wang H, Fan W, Yu PS, and Han J. Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2003. p. 226–35.

    Google Scholar 

  13. Zhu Y and Shasha D. StatStream: statistical monitoring of thousands of data streams in real time. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002. p. 358–69.

    Chapter  Google Scholar 

  14. Zhu Y and Shasha D. Efficient elastic burst detection in data streams. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2003. p. 336–45.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IBM T.J. Watson Research Center, Hawthorne, NY, USA

    Spiros Papadimitriou

Authors
  1. Spiros Papadimitriou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Spiros Papadimitriou .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. AT&T Labs - Research, AT&T, Bedminster, NJ, USA

    Divesh Srivastava

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Papadimitriou, S. (2018). Anomaly Detection on Streams. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_18

Download citation

Publish with us

Policies and ethics

Search

Navigation