Real-time anomaly detection from environmental data streams
Impacto
Scholar |
Otros documentos de la autoría: Trilles, Sergio; Schade, Sven; Belmonte-Fernández, Óscar; Huerta, Joaquin
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/159451
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http://dx.doi.org/10.1007/978-3-319-16787-9_8 |
Metadatos
Título
Real-time anomaly detection from environmental data streamsFecha de publicación
2015Editor
Springer International PublishingISBN
978-3-319-16786-2Cita bibliográfica
Sergi Trilles, Sven Schade, Óscar Belmonte, Joaquín Huerta. Real-Time Anomaly Detection from Environmental Data Streams. In: F. Bacao, M. Santos, M. Painho (ed.) Agile 2015: Geographic Information Science as an enabler of smart cities and communities. Heidelberg: Springer, 2015. p.125-144Tipo de documento
info:eu-repo/semantics/bookPartVersión de la editorial
http://link.springer.com/chapter/10.1007%2F978-3-319-16787-9_8Palabras clave / Materias
Resumen
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. ... [+]
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. [-]
Descripción
AGILE 2015: Geographic Information Science as an Enabler of Smarter Cities and Communities. Ponencia presentada en el 18th AGILE Conference on Geographic Information Science, celebrado en Lisboa del 9 al 12 de junio ... [+]
AGILE 2015: Geographic Information Science as an Enabler of Smarter Cities and Communities. Ponencia presentada en el 18th AGILE Conference on Geographic Information Science, celebrado en Lisboa del 9 al 12 de junio de 2015. [-]
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