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AGILE 2015 pp 125–144Cite as

Real-Time Anomaly Detection from Environmental Data Streams

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Modern sensor networks monitor a wide range of phenomena. They are applied in environmental monitoring, health care, optimization of industrial processes, social media, smart city solutions, and many other domains. All in all, they provide a continuously pulse of the almost infinite activities that are happening in the physical space—and in cyber space. The handling of the massive amounts of generated measurements poses a series of (Big Data) challenges. Our work addresses one of these challenges: the detection of anomalies in real-time. In this paper, we propose a generic solution to this problem, and introduce a system that is capable of detecting anomalies, generating notifications, and displaying the recent situation to the user. We apply CUSUM a statistical control algorithm and adopt it so that it can be used inside the Storm framework—a robust and scalable real-time processing framework. We present a proof of concept implementation from the area of environmental monitoring.


  • Big data and real-time analysis
  • Environmental sensor data

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  • DOI: 10.1007/978-3-319-16787-9_8
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  1. 1.

    Storm, Apache Incubator,

  2. 2.

    Apache S4, Apache Incubator,

  3. 3.

    Samza, Apache Incubator,

  4. 4.

    Spark Streaming, Apache Incubator,

  5. 5.

    Storm Trident, Apache Incubator,

  6. 6.

    ActiveMQ framework, Apache Software Foundation,

  7. 7.

    OpenWire protocol, Apache Incubator,

  8. 8.

    STOMP protocol, Apache Incubator,

  9. 9.

  10. 10.

    Leaflet: An Open-Source JavaScript Library for Mobile-Friendly Interactive Maps,

  11. 11.


  12. 12.

    Bootstrap, Twitter

  13. 13.

    jQuery, jQuery Foundation

  14. 14.

    Highcharts JS, Highcharts AS


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This work has been supported in part by European Commission and Generalitat Valenciana government (grants ACIF/2012/112 and BEFPI/2014/067).

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Correspondence to Sergio Trilles .

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Trilles, S., Schade, S., Belmonte, Ó., Huerta, J. (2015). Real-Time Anomaly Detection from Environmental Data Streams. In: Bacao, F., Santos, M., Painho, M. (eds) AGILE 2015. Lecture Notes in Geoinformation and Cartography. Springer, Cham.

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