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|Title:||Real-time Anomaly Detection from Environmental Data Streams|
|Authors:||SCHADE SVEN; BELMONTE Oscar; HUERTA Joaquin|
|Other Contributors:||TRILLES Sergi|
|Publisher:||Springer International Publishing|
|Type:||Articles in periodicals and books|
|Abstract:||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.|
|JRC Directorate:||Sustainable Resources|
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