Steiger, Martin ; Bernard, Jürgen ; Mittelstädt, Sebastian ; Lücke-Tieke, Hendrik ; Keim, Daniel A. ; May, Thorsten ; Kohlhammer, Jörn (2014)
Visual Analysis of Time-Series Similarities for Anomaly Detection in Sensor Networks.
In: Computer Graphics Forum, 33 (3)
doi: 10.1111/cgf.12396
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
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 patterns 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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2014 |
| Autor(en): | Steiger, Martin ; Bernard, Jürgen ; Mittelstädt, Sebastian ; Lücke-Tieke, Hendrik ; Keim, Daniel A. ; May, Thorsten ; Kohlhammer, Jörn |
| Art des Eintrags: | Bibliographie |
| Titel: | Visual Analysis of Time-Series Similarities for Anomaly Detection in Sensor Networks |
| Sprache: | Englisch |
| Publikationsjahr: | 2014 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Computer Graphics Forum |
| Jahrgang/Volume einer Zeitschrift: | 33 |
| (Heft-)Nummer: | 3 |
| DOI: | 10.1111/cgf.12396 |
| Kurzbeschreibung (Abstract): | 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 patterns 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. |
| Freie Schlagworte: | Business Field: Visual decision support, Research Area: Human computer interaction (HCI), Time series analysis, Multi sensor analysis, Data visualization |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 12 Nov 2018 11:16 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum