Graphical exploratory data analysis
Graphical exploratory data analysis
Visualizing document authorship using n-grams and latent semantic indexing
NPIV '97 Proceedings of the 1997 workshop on New paradigms in information visualization and manipulation
Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management
Cluster and Calendar Based Visualization of Time Series Data
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
A taxonomy of glyph placement strategies for multidimensional data visualization
Information Visualization
Proceedings of the 35th conference on Winter simulation: driving innovation
Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event n-Grams
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
PlanningLines: Novel Glyphs for Representing Temporal Uncertainties and Their Evaluation
IV '05 Proceedings of the Ninth International Conference on Information Visualisation
Visualizing time-oriented data-A systematic view
Computers and Graphics
Visualization and exploration of time-varying medical image data sets
GI '07 Proceedings of Graphics Interface 2007
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Toward a Deeper Understanding of the Role of Interaction in Information Visualization
IEEE Transactions on Visualization and Computer Graphics
VizTree: a tool for visually mining and monitoring massive time series databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Visual Methods for Analyzing Time-Oriented Data
IEEE Transactions on Visualization and Computer Graphics
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet
IEEE Transactions on Visualization and Computer Graphics
Importance-Driven Time-Varying Data Visualization
IEEE Transactions on Visualization and Computer Graphics
NgViz: detecting DNS tunnels through n-gram visualization and quantitative analysis
Proceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research
SignalLens: Focus+Context Applied to Electronic Time Series
IEEE Transactions on Visualization and Computer Graphics
Case study: visualization and information retrieval techniques for network intrusion detection
EGVISSYM'01 Proceedings of the 3rd Joint Eurographics - IEEE TCVG conference on Visualization
Interaction spaces in data and information visualization
VISSYM'04 Proceedings of the Sixth Joint Eurographics - IEEE TCVG conference on Visualization
Generating time lines with virtual words for time-varying data visualization
Proceedings of the 5th International Symposium on Visual Information Communication and Interaction
Visualization and analysis of 3D time-varying simulations with time lines
Journal of Visual Languages and Computing
| Hi-index | 0.00 |
Time-series data is a common target for visual analytics, as they appear in a wide range of application domains. Typical tasks in analyzing time-series data include identifying cyclic behavior, outliers, trends, and periods of time that share distinctive shape characteristics. Many methods for visualizing time series data exist, generally mapping the data values to positions or colors. While each can be used to perform a subset of the above tasks, none to date is a complete solution. In this paper we present a novel approach to time-series data visualization, namely creating multivariate data records out of short subsequences of the data and then using multivariate visualization methods to display and explore the data in the resulting shape space. We borrow ideas from text analysis, where the use of N-grams is a common approach to decomposing and processing unstructured text. By mapping each temporal N-gram to a glyph, and then positioning the glyphs via PCA (basically a projection in shape space), many different kinds of patterns in the sequence can be readily identified. Interactive selection via brushing, in conjunction with linking to other visualizations, provides a wide range of tools for exploring the data. We validate the usefulness of this approach with examples from several application domains and tasks, comparing our methods with traditional time-series visualizations.