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

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 ove...

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Podrobná bibliografie
Vydáno v:	Computer graphics forum Ročník 33; číslo 3; s. 401 - 410
Hlavní autoři:	Steiger, Martin, Bernard, Jürgen, Mittelstädt, Sebastian, Lücke-Tieke, Hendrik, Keim, Daniel, May, Thorsten, Kohlhammer, Jörn
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Blackwell Publishing Ltd 01.06.2014
Témata:	Analysis Anomalies C.2.3 [Computer-Communication Networks]: Network Operations-Network monitoring Categories and Subject Descriptors (according to ACM CCS) Data visualization H.5.2 [Information Interfaces and Presentation]: User Interfaces-User-centered design I.3.6 [Computer Graphics]: Methodology and Techniques-Interaction techniques Information processing Networks Recording Sensors Studies Tasks Temporal logic Time series Visual Visualization Wireless networks
ISSN:	0167-7055, 1467-8659
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Popis
Shrnutí:	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. 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.
Bibliografie:	ark:/67375/WNG-2GHRJ008-P Supporting Information istex:810D01C7895CF2F9355F5FB8BBDE2E47E0034647 ArticleID:CGF12396 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0167-7055 1467-8659
DOI:	10.1111/cgf.12396