Visual Analysis of Time-Series Similarities for Anomaly Detection in Sensor Networks

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dc.contributor.authorSteiger, Martinen_US
dc.contributor.authorBernard, Jürgenen_US
dc.contributor.authorMittelstädt, Sebastianen_US
dc.contributor.authorLücke-Tieke, Hendriken_US
dc.contributor.authorKeim, Danielen_US
dc.contributor.authorMay, Thorstenen_US
dc.contributor.authorKohlhammer, Jörnen_US
dc.contributor.editorH. Carr, P. Rheingans, and H. Schumannen_US
dc.date.accessioned2015-03-03T12:36:31Z
dc.date.available2015-03-03T12:36:31Z
dc.date.issued2014en_US
dc.description.abstractWe present a system to analyze time-series data in sensor networks. Our approach supports exploratory tasks for the comparison of univariate, geo-referenced sensor data, in particular for anomaly detection. We split the recordings into fixed-length patterns and show them in order to compare them over time and space using two linked views. Apart from geo-based comparison across sensors we also support different temporal patterns to discover seasonal effects, anomalies and periodicities. The methods we use are best practices in the information visualization domain. They cover the daily, the weekly and seasonal and patterns of the data. Daily patterns can be analyzed in a clustering-based view, weekly patterns in a calendar-based view and seasonal patters in a projection-based view. The connectivity of the sensors can be analyzed through a dedicated topological network view. We assist the domain expert with interaction techniques to make the results understandable. As a result, the user can identify and analyze erroneous and suspicious measurements in the network. A case study with a domain expert verified the usefulness of our approach.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/cgf.12396en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12396en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleVisual Analysis of Time-Series Similarities for Anomaly Detection in Sensor Networksen_US
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33-Issue 3
EuroVis14: Eurographics Conference on Visualization