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Skalierbar Anomalien erkennen für Smart City Infrastrukturen

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Big Data

Part of the book series: Edition HMD ((EHMD))

Zusammenfassung

In diesem Kapitel wird ein Informationssystem beschrieben, welches Anomalien in großen Netzwerken erkennen kann. Ein solches Netzwerk ist beispielsweise das Wasserversorgungsnetz einer Stadt. Anhand eines Prototyps wird aufgezeigt, wie potenzielle Anomalien dynamisch und in Echtzeit entdeckt werden können.

Dieser Kapitel basiert auf dem Artikel „Scalable Anomaly Detection for Smart City Infrastructure Networks“, Internet Computing, IEEE 17(6):47, 2013.

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Notes

  1. 1.

    http://www.isa.org/ISA100-11a

  2. 2.

    http://www.ieee802.org/15/pub/TG4.html

  3. 3.

    http://www.zigbee.org/

  4. 4.

    http://www.scidb.org/

  5. 5.

    http://www.scilens.org/

  6. 6.

    http://www.rasdaman.com/

  7. 7.

    https://storm.apache.org

  8. 8.

    http://emps.exeter.ac.uk/engineering/research/cws/

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Author information

Authors and Affiliations

  1. Universität Fribourg, Fribourg, Schweiz

    Djellel Eddine Difallah & Philippe Cudré-Mauroux

  2. IBM Research Smarter Cities Technology Center, Dublin, Irland

    Sean A. McKenna

  3. Scigility AG, Marly, Schweiz

    Daniel Fasel

Authors
  1. Djellel Eddine Difallah
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  2. Philippe Cudré-Mauroux
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  3. Sean A. McKenna
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  4. Daniel Fasel
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Corresponding author

Correspondence to Djellel Eddine Difallah .

Editor information

Editors and Affiliations

  1. Scigility AG, Marly, Switzerland

    Daniel Fasel

  2. Institut für Informatik, Univ. Fribourg, Fribourg, Switzerland

    Andreas Meier

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Difallah, D.E., Cudré-Mauroux, P., McKenna, S.A., Fasel, D. (2016). Skalierbar Anomalien erkennen für Smart City Infrastrukturen. In: Fasel, D., Meier, A. (eds) Big Data. Edition HMD. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-11589-0_14

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