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Related Experiment Videos

Visualization of heterogeneous data.

Mike Cammarano1, Xin Luna Dong, Bryan Chan

  • 1Stanford University, USA. mcammara@stanford.edu

IEEE Transactions on Visualization and Computer Graphics
|October 31, 2007
PubMed
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We developed an automatic technique to map data attributes to visualization attributes, eliminating manual integration for graph data visualization. This approach simplifies exploring large, unfamiliar schemas in semantic web technologies like Resource Description Framework (RDF).

Area of Science:

  • Computer Science
  • Data Visualization
  • Semantic Web Technologies

Background:

  • Resource Description Framework (RDF) and Maya Viz u-forms model data as graphs.
  • Current visualization systems require manual mapping of data attributes to visualization roles.
  • This manual process hinders exploratory visualization, especially with large or unfamiliar schemas.

Purpose of the Study:

  • To propose an automatic technique for mapping data attributes to visualization attributes.
  • To eliminate the need for costly up-front data integration in graph data visualization.
  • To facilitate exploratory visualization of complex data models.

Main Methods:

  • Formulating the problem as a schema matching task.
  • Identifying appropriate paths within the data model.

Related Experiment Videos

  • Mapping data attributes to required visualization attributes within a template.

    • Main Results:

    • An automated method for attribute mapping in graph data visualization.
    • Reduced effort in preparing data for visualization.
    • Enabled more efficient exploratory analysis of semantic web data.

    Conclusions:

    • Automatic attribute mapping significantly simplifies exploratory visualization.
    • The proposed schema matching approach is effective for Resource Description Framework (RDF) data.
    • This technique lowers the barrier to entry for visualizing complex graph-based datasets.