Finding anomalies in time-series using visual correlation for interactive root cause analysis

ACM International Conference Proceeding Series

Volumn , Issue , 2013, Pages 65-72

  Stoffel, Florian ^a   Fischer, Fabian ^a   Keim, Daniel A ^a  

^a UNIVERSITY OF KONSTANZ   (Germany)

Author keywords

Anomalies; Correlation; Network security; Time series; Visual analytics

Indexed keywords

ANOMALIES; NETWORK DEVICES; ROOT CAUSE ANALYSIS; SIMILARITY MODELS; STATE-OF-THE-ART TECHNOLOGY; TIME-SERIES DATA; VISUAL ANALYTICS; VISUAL CORRELATIONS;

CORRELATION METHODS; NETWORK SECURITY;

VISUALIZATION;

EID: 84886394867     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2517957.2517966     Document Type: Conference Paper

References (18)

1. W. Aigner, S. Miksch, H. Schumann, and C. Tominski. Visualization of Time-Oriented Data. Human-Computer Interaction Series. Springer, 2011.
2. D. M. Best, S. Bohn, D. Love, A. Wynne, and W. A. Pike. Real-time visualization of network behaviors for situational awareness. In Proceedings of the Seventh International Symposium on Visualization for Cyber Security, VizSec '10, pages 79-90, New York, NY, USA, 2010. ACM.
3. P. Bloomfield. Fourier Analysis of Time Series: An Introduction. John Wiley and Sons, 2nd edition, 2000.
4. S. Carpendale, J. Ligh, and E. Pattison. Achieving higher magnification in context. In Proceedings of the 17th annual ACM symposium on User interface software and technology, UIST'04, pages 71-80. ACM, 2004.
5. G. Fink, C. North, A. Endert, and S. Rose. Visualizing Cyber Security: Usable Workspaces. In Visualization for Cyber Security, 2009. VizSec 2009. 6th International Workshop on, pages 45-56, 2009.
6. F. Fischer, J. Fuchs, and F. Mansmann. ClockMap: enhancing circular treemaps with temporal glyphs for time-series data. In M. Meyer and T. Weinkauf, editors, Proceedings of the Eurographics Conference on Visualization (EuroVis 2012 Short Papers), pages 97-101, Vienna, Austria, 2012.
7. J. Heer, N. Kong, and M. Agrawala. Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI'09, pages 1303-1312, New York, NY, USA, 2009. ACM.
8. W. Javed, B. McDonnel, and N. Elmqvist. Graphical perception of multiple time series. IEEE Transactions on Visualization and Computer Graphics, 16(6): 927-934, Nov. 2010.
9. G. Kaur, V. Saxena, and J. P. Gupta. Anomaly detection in network traffic and role of wavelets. In Computer Engineering and Technology (ICCET), 2010 2nd International Conference on, Volume 7, pages V7-46-V7-51, 2010.
10. D. A. Keim, F. Mansmann, J. Schneidewind, J. Thomas, and H. Ziegler. Visual data mining. chapter Visual Analytics: Scope and Challenges, pages 76-90. Springer-Verlag, Berlin, Heidelberg, 2008.
11. E. Keogh, J. Lin, and A. Fu. Hot sax: Efficiently finding the most unusual time series subsequence. In Proceedings of the Fifth IEEE International Conference on Data Mining, ICDM'05, pages 226-233, Washington, DC, USA, 2005. IEEE Computer Society.
12. R. Kincaid and H. Lam. Line graph explorer: scalable display of line graphs using focus+context. In Proceedings of the working conference on Advanced visual interfaces, AVI'06, pages 404-411, New York, NY, USA, 2006. ACM.
13. D. E. Knuth. The Art of Computer Programming: Seminumerical Algorithms, Volume 2. Addison-Wesley Longman Publishing Co., Inc., 3 edition, 1997.
14. P. McLachlan, T. Munzner, E. Koutsofios, and S. North. Liverac: interactive visual exploration of system management time-series data. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI'08, pages 1483-1492, New York, NY, USA, 2008. ACM.
15. D. B. Percival and A. T. Walden. Wavelet Methods for Time Series Analysis. Cambridge University Press, 2000.
16. T. Saito, H. N. Miyamura, M. Yamamoto, H. Saito, Y. Hoshiya, and T. Kaseda. Two-tone pseudo coloring: Compact visualization for one-dimensional data. In Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization, INFOVIS'05, pages 23-, Washington, DC, USA, 2005. IEEE Computer Society.
17. I. Shafer, K. Ren, V. N. Boddeti, Y. Abe, G. R. Ganger, and C. Faloutsos. Rainmon: an integrated approach to mining bursty timeseries monitoring data. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD'12, pages 1158-1166, New York, NY, USA, 2012. ACM.
18. J. Zhao, F. Chevalier, E. Pietriga, and R. Balakrishnan. Exploratory analysis of time-series with chronolenses. Visualization and Computer Graphics, IEEE Transactions on, 17(12): 2422-2431, 2011.