Diverse Information Integration and Visualization

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

Title: Diverse Information Integration and Visualization

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Abstract

This paper presents and explores a technique for visually integrating and exploring diverse information. Society produces, collects, and processes ever larger and diverse data including semi- and un-structured text, as well as transaction, communication, and scientific data. It is no longer sufficient to analyze one type of data or information in isolation. Users need to explore their data/information in the context of related information to discover often hidden, but meaningful, complex relationships. Our approach visualizes multiple, like entities across multiple dimensions where each dimension is a partitioning of the entities. The partitioning may be based on inherent or assigned attributes of the entities (or entity data) such as meta-data or prior knowledge captured in annotations. The partitioning may also be derived from entity data. For example, clustering, or unsupervised classification, can be applied to arrays of multidimensional entity data to partition the entities into groups of similar entities, 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 a variety of data without concern for data typesmore »« less

Authors:: Havre, Susan L; Shah, Anuj; Posse, Christian; Webb-Robertson, Bobbie-Jo M

Publication Date:: Mon Jan 16 00:00:00 EST 2006

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 891147

Report Number(s):: PNNL-SA-44998
TRN: US200621%%47

DOE Contract Number:: AC05-76RL01830

Resource Type:: Conference

Resource Relation:: Conference: Visualization and Data Analysis 2006, 15-19 January 2006, San Jose, California, USA. Proceedings of SPIE , 6060

Country of Publication:: United States

Language:: English

Subject:: 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; DATA ANALYSIS; INFORMATION RETRIEVAL; COMPUTER GRAPHICS; information visualization, categorical data, parallel coordinates

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                    Havre, Susan L, Shah, Anuj, Posse, Christian, and Webb-Robertson, Bobbie-Jo M. Diverse Information Integration and Visualization.  United States: N. p., 2006. 
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                    Havre, Susan L, Shah, Anuj, Posse, Christian, & Webb-Robertson, Bobbie-Jo M. Diverse Information Integration and Visualization.  United States.

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                    Havre, Susan L, Shah, Anuj, Posse, Christian, and Webb-Robertson, Bobbie-Jo M. 2006.  
        "Diverse Information Integration and Visualization".  United States.

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@article{osti_891147,

  title        = {Diverse Information Integration and Visualization},

  author       = {Havre, Susan L and Shah, Anuj and Posse, Christian and Webb-Robertson, Bobbie-Jo M},

  abstractNote = {This paper presents and explores a technique for visually integrating and exploring diverse information. Society produces, collects, and processes ever larger and diverse data including semi- and un-structured text, as well as transaction, communication, and scientific data. It is no longer sufficient to analyze one type of data or information in isolation. Users need to explore their data/information in the context of related information to discover often hidden, but meaningful, complex relationships. Our approach visualizes multiple, like entities across multiple dimensions where each dimension is a partitioning of the entities. The partitioning may be based on inherent or assigned attributes of the entities (or entity data) such as meta-data or prior knowledge captured in annotations. The partitioning may also be derived from entity data. For example, clustering, or unsupervised classification, can be applied to arrays of multidimensional entity data to partition the entities into groups of similar entities, 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 a variety of data without concern for data types and units. Parallel coordinates visualize entity data across multiple dimensions of typically continuous attributes. We adapt parallel coordinates for dimensions with discrete attributes (partitions) to allow the comparison of entity partition patterns for identifying trends and outlier entities. We illustrate this approach through a prototype, Juxter (short for Juxtaposer).},

  doi          = {},

  url          = {https://www.osti.gov/biblio/891147},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Jan 16 00:00:00 EST 2006},

  month        = {Mon Jan 16 00:00:00 EST 2006}

}

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