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Visualization has become integral to the knowledge discovery process across various domains. However, challenges remain in the effective use of visualization techniques, especially when displaying, exploring and analyzing large, multidimensional datasets, such as weather and meteorological data. Direct visualizations of such datasets tend to produce images that are cluttered with excess detail and are ineffective at communicating information at higher levels of abstraction. To address this problem we provide a visual summarization framework to intuitively reduce the data to its important and relevant characteristics. Summarization is performed in three broad steps. First, high-relevance data elements and clusters of similar data attributes are identified to reduce a dataset's size and dimensionality. Next, patterns, relationships and outliers are extracted from the reduced data. Finally, the extracted summary characteristics are visualized to the user. Such visualizations reduce excess visual detail and are more suited to the rapid comprehension of complex data. Users can interactively guide the summarization process gaining insight into both how and why the summary results are produced. Our framework improves the benefits of mathematical analysis and interactive visualization by combining the strengths of the computer and the user to generate high-quality summaries. Initial results from applying our framework to large weather datasets have been positive, suggesting that our approach could be beneficial for a wide range of domains and applications.
Date of Conference: 11-14 Oct. 2009