Ms. Susan L. Havre Profile

Susan L. Havre

at Pacific Northwest National Lab

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Proceedings Article | 16 January 2006 Paper

Diverse information integration and visualization

Susan Havre, Anuj Shah, Christian Posse, Bobbie-Jo Webb-Robertson

Proceedings Volume 6060, 60600M (2006) https://doi.org/10.1117/12.643492

KEYWORDS: Visualization, Prototyping, Information visualization, Data integration, Analytical research, Biological research, Information fusion, Proteins, Visual analytics, Biology

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This paper presents and explores a technique for visually integrating and exploring diverse information. Researchers and analysts seeking knowledge and understanding of complex systems have increasing access to related, but diverse, data. These data provide an opportunity to consider entities of interest from multiple informational perspectives not available from any single, data or information type. These multiple perspectives are derived from diverse, but related data and integrated for simultaneous analysis. Our approach visualizes multiple entities across multiple perspectives where each perspective, or dimension, is an alternate partitioning of the entities. The partitioning may be based on inherent or assigned attributes such as meta-data or prior knowledge captured in annotations. The partitioning may also be directly derived from entity data; for example, clustering, or unsupervised classification, can be applied to multi-dimensional vector entity data to partition the entities into groups, or clusters. The same entities may be clustered on data from different experiment types or processing approaches. This reduction of diverse data/information on an entity to a series of partitions, or discrete (and unit-less) categories, allows the user to view the entities across diverse data without concern for data types and units. Parallel coordinate plots typically visualize continuous data across multiple dimensions. We adapt parallel coordinate plots for discrete values such as partition names to allow the comparison of entity patterns across multiple dimension for identifying trends and outlier entities. We illustrate this approach through a prototype, Juxter (short for Juxtaposer).

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