| CircleView: a new approach for visualizing time-related multidimensional data sets |
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Proceedings of the working conference on Advanced visual interfaces
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Gallipoli, Italy
SESSION: Improving visualization
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Pages: 179 - 182
Year of Publication: 2004
ISBN:1-58113-867-9
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Downloads (6 Weeks): 19, Downloads (12 Months): 73, Citation Count: 1
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ABSTRACT
This paper introduces a new approach for visualizing multidimensional time-referenced data sets, called Circle View. The Circle View technique is a combination of hierarchical visualization techniques, such as treemaps [6], and circular layout techniques such as Pie Charts and Circle Segments [2]. The main goal is to compare continuous data changing their characteristics over time in order to identify patterns, exceptions and similarities in the data.To achieve this goal Circle View is a intuitive and easy to understand visualization interface to enable the user very fast to acquire the information needed. This is an important feature for fast changing visualization caused by time related data streams. Circle View supports the visualization of the changing characteristics over time, to allow the user the observation of changes in the data. Additionally it provides user interaction and drill down mechanism depending on user demands for a effective exploratory data analysis. There is also the capability of exploring correlations and exceptions in the data by using similarity and ordering algorithms.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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I. POPIVANOV, R. M. Similarity search over time series data using wavelets. In ICDE 2002 (2002).
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KEIM, D. A., HAO, M. C., DAYAL, U., AND HSU, M. Pixel bar charts: A visualization technique for very large multi-attribute data sets. Visualization, San Diego 2001, extended version in: IEEE Transactions on Visualization and Computer Graphics 7, 2002 (2002).
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Y. ZHU, D. S. Statstream: Statistical monitoring of thousands of data streams in real time. In VLDB 2002 (2002), pp. 358--369.
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CITED BY
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Michèle Sebag , Nicolas Tarrisson , Olivier Teytaud , Julien Lefevre , Sylvain Baillet, A multi-objective multi-modal optimization approach for mining stable spatio-temporal patterns, Proceedings of the 19th international joint conference on Artificial intelligence, p.859-864, July 30-August 05, 2005, Edinburgh, Scotland
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