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Real-time anomaly detection from environmental data streams
dc.contributor.author | Trilles, Sergio | |
dc.contributor.author | Schade, Sven | |
dc.contributor.author | Belmonte-Fernández, Óscar | |
dc.contributor.author | Huerta, Joaquin | |
dc.date.accessioned | 2016-07-29T09:35:52Z | |
dc.date.available | 2016-07-29T09:35:52Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | 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.125-144 | ca_CA |
dc.identifier.isbn | 978-3-319-16786-2 | |
dc.identifier.uri | http://hdl.handle.net/10234/162070 | |
dc.description | 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. | ca_CA |
dc.description.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. | ca_CA |
dc.format.extent | 19 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer International Publishing | ca_CA |
dc.rights | Copyright 2016 Springer International Publishing Switzerland | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | Big data and real-time analysis | ca_CA |
dc.subject | Environmental sensor data | ca_CA |
dc.subject | CUSUM | ca_CA |
dc.subject | STORM | ca_CA |
dc.title | Real-time anomaly detection from environmental data streams | ca_CA |
dc.type | info:eu-repo/semantics/bookPart | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.1007/978-3-319-16787-9_8 | |
dc.rights.accessRights | info:eu-repo/semantics/closedAccess | ca_CA |
dc.relation.publisherVersion | http://link.springer.com/chapter/10.1007%2F978-3-319-16787-9_8 | ca_CA |
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