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dc.contributor.authorTrilles Oliver, Sergi
dc.contributor.authorSchade, Sven
dc.contributor.authorBelmonte Fernández, Óscar
dc.contributor.authorHuerta, Joaquín
dc.date.accessioned2016-07-29T09:35:52Z
dc.date.available2016-07-29T09:35:52Z
dc.date.issued2015
dc.identifier.citationSergi 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.125-144ca_CA
dc.identifier.isbn978-3-319-16786-2
dc.identifier.urihttp://hdl.handle.net/10234/162070
dc.descriptionAGILE 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.ca_CA
dc.description.abstractModern 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.ca_CA
dc.format.extent19 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringer International Publishingca_CA
dc.rightsCopyright 2016 Springer International Publishing Switzerlandca_CA
dc.subjectBig data and real-time analysisca_CA
dc.subjectEnvironmental sensor dataca_CA
dc.subjectCUSUMca_CA
dc.subjectSTORMca_CA
dc.titleReal-time anomaly detection from environmental data streamsca_CA
dc.typeinfo:eu-repo/semantics/bookPartca_CA
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-319-16787-9_8
dc.rights.accessRightsinfo:eu-repo/semantics/closedAccessca_CA
dc.relation.publisherVersionhttp://link.springer.com/chapter/10.1007%2F978-3-319-16787-9_8ca_CA


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