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

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

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STEIGER, Martin, Jürgen BERNARD, Sebastian MITTELSTÄDT, Hendrik LÜCKE-TIEKE, Daniel KEIM, Thorsten MAY, Jörn KOHLHAMMER, 2014. Visual Analysis of Time-Series Similarities for Anomaly Detection in Sensor Networks. In: Computer Graphics Forum. 33(3), pp. 401-410. ISSN 0167-7055. eISSN 1467-8659

@article{Steiger2014Visua-29865, title={Visual Analysis of Time-Series Similarities for Anomaly Detection in Sensor Networks}, year={2014}, doi={10.1111/cgf.12396}, number={3}, volume={33}, issn={0167-7055}, journal={Computer Graphics Forum}, pages={401--410}, author={Steiger, Martin and Bernard, Jürgen and Mittelstädt, Sebastian and Lücke-Tieke, Hendrik and Keim, Daniel and May, Thorsten and Kohlhammer, Jörn} }

Bernard, Jürgen Steiger, Martin Keim, Daniel Steiger, Martin 2014 Visual Analysis of Time-Series Similarities for Anomaly Detection in Sensor Networks Mittelstädt, Sebastian Kohlhammer, Jörn We 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.<br /><br />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. 2015-02-18T19:24:16Z May, Thorsten 2015-02-18T19:24:16Z Mittelstädt, Sebastian Keim, Daniel Lücke-Tieke, Hendrik eng Lücke-Tieke, Hendrik May, Thorsten Kohlhammer, Jörn Bernard, Jürgen

Dateiabrufe seit 18.02.2015 (Informationen über die Zugriffsstatistik)

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