CHI '86 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exploring large tables with the table lens
CHI '95 Conference Companion on Human Factors in Computing Systems
Interactive visualization of serial periodic data
Proceedings of the 11th annual ACM symposium on User interface software and technology
IEEE Transactions on Visualization and Computer Graphics
Dynamic Aggregation with Circular Visual Designs
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Recursive Pattern: A Technique for Visualizing Very Large Amounts of Data
VIS '95 Proceedings of the 6th conference on Visualization '95
Visualizing Time-Series on Spirals
INFOVIS '01 Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01)
Demystifying Venture Capital Investing
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
Proceedings of the 35th conference on Winter simulation: driving innovation
BinX: Dynamic Exploration of Time Series Datasets Across Aggregation Levels
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Importance-Driven Visualization Layouts for Large Time Series Data
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Assumption-free anomaly detection in time series
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
Representing unevenly-spaced time series data for visualization and interactive exploration
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
Data Vases: 2D and 3D Plots for Visualizing Multiple Time Series
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Visual analysis of dynamic data streams
Information Visualization
Interactive visual exploration of neighbor-based patterns in data streams
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Visual analysis of news streams with article threads
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
A review on time series data mining
Engineering Applications of Artificial Intelligence
Visual exploration of stream pattern changes using a data-driven framework
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Better drilling through sensor analytics: a case study in live operational intelligence
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Visual signatures for financial time series
Proceedings of the 2011 Visual Information Communication - International Symposium
Visual exploration of frequent patterns in multivariate time series
Information Visualization - Special issue on Visualization and Data Analysis 2011
Towards a net-zero data center
ACM Journal on Emerging Technologies in Computing Systems (JETC)
Density displays for data stream monitoring
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
A visual analytics approach for peak-preserving prediction of large seasonal time series
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
A fractal-based 2D expansion method for multi-scale volume data visualization
Journal of Visualization
An application of sensor and streaming analytics to oil production
Proceedings of the 17th International Conference on Management of Data
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Time series are a data type of utmost importance in many domains such as business management and service monitoring. We address the problem of visualizing large time-related data sets which are difficult to visualize effectively with standard techniques given the limitations of current display devices. We propose a framework for intelligent time- and data-dependent visual aggregation of data along multiple resolution levels. This idea leads to effective visualization support for long time-series data providing both focus and context. The basic idea of the technique is that either data-dependent or application-dependent, display space is allocated in proportion to the degree of interest of data subintervals, thereby (a) guiding the user in perceiving important information, and (b) freeing required display space to visualize all the data. The automatic part of the framework can accommodate any time series analysis algorithm yielding a numeric degree of interest scale. We apply our techniques on real-world data sets, compare it with the standard visualization approach, and conclude the usefulness and scalability of the approach.