The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Achieving higher magnification in context
Proceedings of the 17th annual ACM symposium on User interface software and technology
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Two-Tone Pseudo Coloring: Compact Visualization for One-Dimensional Data
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Line graph explorer: scalable display of line graphs using Focus+Context
Proceedings of the working conference on Advanced visual interfaces
LiveRAC: interactive visual exploration of system management time-series data
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Visual Analytics: Scope and Challenges
Visual Data Mining
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Real-time visualization of network behaviors for situational awareness
Proceedings of the Seventh International Symposium on Visualization for Cyber Security
Graphical Perception of Multiple Time Series
IEEE Transactions on Visualization and Computer Graphics
Visualization of Time-Oriented Data
Visualization of Time-Oriented Data
Exploratory Analysis of Time-Series with ChronoLenses
IEEE Transactions on Visualization and Computer Graphics
RainMon: an integrated approach to mining bursty timeseries monitoring data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Monitoring computer networks often includes gathering vast amounts of time-series data from thousands of computer systems and network devices. Threshold alerting is easy to accomplish with state-of-the-art technologies. However, to find correlations and similar behaviors between the different devices is challenging. We developed a visual analytics application to tackle this challenge by integrating similarity models and analytics combined with well-known, but task-adapted, time-series visualizations. We show in a case study, how this system can be used to visually identify correlations and anomalies in large data sets and identify and investigate security-related events.