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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| Published in | Computer graphics forum Vol. 33; no. 3; pp. 401 - 410 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.06.2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-7055 1467-8659 |
| DOI | 10.1111/cgf.12396 |
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| 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 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. |
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| AbstractList | 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. [PUBLICATION 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 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. 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. |
| Author | Bernard, Jürgen Lücke-Tieke, Hendrik May, Thorsten Mittelstädt, Sebastian Keim, Daniel Steiger, Martin Kohlhammer, Jörn |
| Author_xml | – sequence: 1 givenname: Martin surname: Steiger fullname: Steiger, Martin organization: Fraunhofer IGD, Germany – sequence: 2 givenname: Jürgen surname: Bernard fullname: Bernard, Jürgen organization: Fraunhofer IGD, Germany – sequence: 3 givenname: Sebastian surname: Mittelstädt fullname: Mittelstädt, Sebastian organization: University of Konstanz, Germany – sequence: 4 givenname: Hendrik surname: Lücke-Tieke fullname: Lücke-Tieke, Hendrik organization: Fraunhofer IGD, Germany – sequence: 5 givenname: Daniel surname: Keim fullname: Keim, Daniel organization: University of Konstanz, Germany – sequence: 6 givenname: Thorsten surname: May fullname: May, Thorsten organization: Fraunhofer IGD, Germany – sequence: 7 givenname: Jörn surname: Kohlhammer fullname: Kohlhammer, Jörn organization: Fraunhofer IGD, Germany |
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| Copyright | 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2014 The Eurographics Association and John Wiley & Sons Ltd. |
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| References | Bernard J., Wilhelm N., Krüger B., May T., Schreck T., Kohlhammer J.: MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation. IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 2257-2266. 4 Collins C., Penn G., Carpendale S.: Bubble sets: Revealing set relations with isocontours over existing visualizations. Visualization and Computer Graphics, IEEE Transactions on 15, 6 (2009), 1009-1016. 9 Zhao J., Forer P., Harvey A.S.: Activities, ringmaps and geovisualization of large human movement fields. Information Visualization 7, 3-4 (2008), 198-209. 3 Keim D.: Designing pixel-oriented visualization techniques: Theory and applications. IEEE Transactions on Visualization and Computer Graphics 6, 1 (2000). 6 Kruskal J.: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29, 1 (1964), 1-27. doi:10.1007/BF02289565. 3 Hadlak S., Schumann H., Cap C.H., Wollenberg T.: Supporting the Visual Analysis of Dynamic Networks by Clustering associated Temporal Attributes. Visualization and Computer Graphics, IEEE Transactions on 19, 12 (2013), 2267-2276. doi:10.1109/TVCG.2013.198. 3, 4 Bögl M., Aigner W., Filzmoser P., Lammarsch T., Miksch S., Rind A.: Visual Analytics for Model Selection in Time Series Analysis. IEEE Trans. Vis. Comput. Graph. 19, 12 (2013), 2237-2246. 3 Fu T.-C.: A Review on Time Series Data Mining. Eng. Appl. Artif. Intell. 24, 1 (Feb. 2011), 164-181. doi:10.1016/j.engappai.2010.09.007. 3 Janetzko H., Stoffel F., Mittelstädt S., Keim D.A.: Anomaly Detection for Visual Analytics of Power Consumption Data. Computer & Graphics 38 (2014), 27-37. 3 Aigner W., Miksch S., Schumann H., Tominski C.: Visualization of Time-Oriented Data. Springer, London, UK, 2011. doi:10.1007/978-0-85729-079-3. 3 Salvador S., Chan P.: Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis 11, 5 (2007), 561-580. 4 Bernard J., Wilhelm N., Scherer M., May T., Schreck T.: TimeSeriesPaths: Projection-Based Explorative Analysis of Multivarate Time Series Data. Journal of WSCG 20, 2 (2012), 97-106. 3 Ding H., Trajcevski G., Scheuermann P., Wang X., Keogh E.: Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures. Proc. VLDB Endow. 1, 2 (Aug. 2008), 1542-1552. 3 Sips M., Neubert B., Lewis J.P., Hanrahan P.: Selecting good views of high-dimensional data using class consistency. Computer Graphics Forum 28, 3 (2009), 831-838. doi:10.1111/j.1467-8659.2009.01467.x. 3, 4 Wong P.C., Schneider K., Mackey P., Foote H., Chin G., Guttromson R., Thomas J.: A Novel Visualization Technique for Electric Power Grid Analytics. IEEE Transactions on Visualization and Computer Graphics 15, 3 (2009), 410-423. doi:10.1109/TVCG.2008.197. 3 Andrienko G., Andrienko N., Demsar U., Dransch D., Dykes J., Fabrikant S.I., Jern M., Kraak M.-J., Schumann H., Tominski C.: Space, Time and Visual Analytics. Int. J. Geogr. Inf. Sci. 24, 10 (Oct. 2010), 1577-1600. doi:10.1080/13658816.2010.508043. 3 Jain A.K., Dubes R.C.: Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1988. 5 Joia P., Paulovich F.V., Coimbra D., Cuminato J.A., Nonato L.G.: Local affine multidimensional projection. IEEE Transactions on Visualization and Computer Graphics 17, 12 (2011), 2563-2571. 3, 4 Schreck T., Panse C.: A new metaphor for projection-based visual analysis and data exploration. Proc. SPIE 6495 (2007). doi:10.1117/12.697879. 7, 9 Bertini E., Tatu A., Keim D.A.: Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization. IEEE Symposium on Information Visualization (InfoVis) 17, 12 (Dec. 2011), pages 2203-2212. 3 Berndt D.J., Clifford J.: Using Dynamic Time Warping to Find Patterns in Time Series. In KDD workshop (1994), vol. 10/16, Seattle, WA, pp. 359-370. 4 Torgerson W.S.: Multidimensional scaling: I. Theory and Method. Psychometrika 17, 4 (1952), 401-419. doi:10.1007/BF02288916. 3 1994; 10/16 2007; 6495 2011; 30/3 2012 2000; 6 1964; 29 2011 2010 2009 2008 2008; 7 2007 2006; 3843 2005 2002 2011; 17 2008; 1 1952; 17 2007; 11 2009; 28 1999 2013; 19 2009; 5417 2010; 24 2014; 38 2011; 24 2013 2012; 20 2009; 15 1988 e_1_2_9_30_2 e_1_2_9_10_2 e_1_2_9_12_2 e_1_2_9_31_2 e_1_2_9_11_2 e_1_2_9_32_2 Jain A.K. (e_1_2_9_19_2) 1988 e_1_2_9_14_2 e_1_2_9_37_2 e_1_2_9_38_2 e_1_2_9_16_2 e_1_2_9_35_2 e_1_2_9_15_2 e_1_2_9_36_2 e_1_2_9_18_2 e_1_2_9_17_2 Tominski C. (e_1_2_9_33_2) 2008 e_1_2_9_21_2 e_1_2_9_20_2 e_1_2_9_23_2 Berndt D.J. (e_1_2_9_5_2) 1994; 10 e_1_2_9_22_2 e_1_2_9_7_2 Bertini E. (e_1_2_9_9_2) 2011; 17 Bernard J. (e_1_2_9_13_2) 2012; 20 e_1_2_9_6_2 e_1_2_9_4_2 e_1_2_9_3_2 e_1_2_9_2_2 e_1_2_9_8_2 e_1_2_9_25_2 e_1_2_9_24_2 Van Wijk J.J. (e_1_2_9_34_2) 1999 e_1_2_9_27_2 e_1_2_9_26_2 e_1_2_9_29_2 e_1_2_9_28_2 |
| References_xml | – reference: Wong P.C., Schneider K., Mackey P., Foote H., Chin G., Guttromson R., Thomas J.: A Novel Visualization Technique for Electric Power Grid Analytics. IEEE Transactions on Visualization and Computer Graphics 15, 3 (2009), 410-423. doi:10.1109/TVCG.2008.197. 3 – reference: Salvador S., Chan P.: Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis 11, 5 (2007), 561-580. 4 – reference: Hadlak S., Schumann H., Cap C.H., Wollenberg T.: Supporting the Visual Analysis of Dynamic Networks by Clustering associated Temporal Attributes. Visualization and Computer Graphics, IEEE Transactions on 19, 12 (2013), 2267-2276. doi:10.1109/TVCG.2013.198. 3, 4 – reference: Sips M., Neubert B., Lewis J.P., Hanrahan P.: Selecting good views of high-dimensional data using class consistency. Computer Graphics Forum 28, 3 (2009), 831-838. doi:10.1111/j.1467-8659.2009.01467.x. 3, 4 – reference: Bernard J., Wilhelm N., Krüger B., May T., Schreck T., Kohlhammer J.: MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation. IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 2257-2266. 4 – reference: Fu T.-C.: A Review on Time Series Data Mining. Eng. Appl. Artif. Intell. 24, 1 (Feb. 2011), 164-181. doi:10.1016/j.engappai.2010.09.007. 3 – reference: Bögl M., Aigner W., Filzmoser P., Lammarsch T., Miksch S., Rind A.: Visual Analytics for Model Selection in Time Series Analysis. IEEE Trans. Vis. Comput. Graph. 19, 12 (2013), 2237-2246. 3 – reference: Janetzko H., Stoffel F., Mittelstädt S., Keim D.A.: Anomaly Detection for Visual Analytics of Power Consumption Data. Computer & Graphics 38 (2014), 27-37. 3 – reference: Aigner W., Miksch S., Schumann H., Tominski C.: Visualization of Time-Oriented Data. Springer, London, UK, 2011. doi:10.1007/978-0-85729-079-3. 3 – reference: Kruskal J.: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29, 1 (1964), 1-27. doi:10.1007/BF02289565. 3 – reference: Torgerson W.S.: Multidimensional scaling: I. Theory and Method. Psychometrika 17, 4 (1952), 401-419. doi:10.1007/BF02288916. 3 – reference: Collins C., Penn G., Carpendale S.: Bubble sets: Revealing set relations with isocontours over existing visualizations. Visualization and Computer Graphics, IEEE Transactions on 15, 6 (2009), 1009-1016. 9 – reference: Keim D.: Designing pixel-oriented visualization techniques: Theory and applications. IEEE Transactions on Visualization and Computer Graphics 6, 1 (2000). 6 – reference: Andrienko G., Andrienko N., Demsar U., Dransch D., Dykes J., Fabrikant S.I., Jern M., Kraak M.-J., Schumann H., Tominski C.: Space, Time and Visual Analytics. Int. J. Geogr. Inf. Sci. 24, 10 (Oct. 2010), 1577-1600. doi:10.1080/13658816.2010.508043. 3 – reference: Jain A.K., Dubes R.C.: Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1988. 5 – reference: Joia P., Paulovich F.V., Coimbra D., Cuminato J.A., Nonato L.G.: Local affine multidimensional projection. IEEE Transactions on Visualization and Computer Graphics 17, 12 (2011), 2563-2571. 3, 4 – reference: Berndt D.J., Clifford J.: Using Dynamic Time Warping to Find Patterns in Time Series. In KDD workshop (1994), vol. 10/16, Seattle, WA, pp. 359-370. 4 – reference: Bertini E., Tatu A., Keim D.A.: Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization. IEEE Symposium on Information Visualization (InfoVis) 17, 12 (Dec. 2011), pages 2203-2212. 3 – reference: Bernard J., Wilhelm N., Scherer M., May T., Schreck T.: TimeSeriesPaths: Projection-Based Explorative Analysis of Multivarate Time Series Data. 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| SubjectTerms | 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 |
| Title | Visual Analysis of Time-Series Similarities for Anomaly Detection in Sensor Networks |
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