Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data
Oliver Rübel1, Sean Ahern, E Wes Bethel
1Computational Research Division, Lawrence Berkeley National Laboratory (LBNL), One Cyclotron Road, Berkeley, CA, 94720, USA ; International Research Training Group 1131, University of Kaiserslautern, Germany.
Procedia Computer Science
|June 14, 2013
Summary
Discovering knowledge from complex scientific data is hard. Integrating visualization, data analysis, and management methods aids knowledge discovery in multi-dimensional datasets, as shown in developmental biology and accelerator physics.
Area of Science:
- Scientific data analysis
- Knowledge discovery
- Multi-dimensional data
Background:
- Large and complex scientific datasets pose challenges for analysis and exploration.
- Increasing data dimensions and objects strain current data analysis methods.
- Effective knowledge discovery requires advanced tools and techniques.
Purpose of the Study:
- To survey methods for knowledge discovery from multi-dimensional scientific data.
- To illustrate the effectiveness of integrated approaches in specific applications.
- To highlight the role of scientific and information visualization, automated data analysis, and data management.
Main Methods:
- Integration of scientific visualization and information visualization.
- Application of automated data analysis techniques.
- Utilizing efficient data management strategies.
Main Results:
- Demonstrated effectiveness of the integrated approach in developmental biology.
- Showcased successful application in accelerator physics.
- Validated the utility of combined visualization and analysis methods for complex data.
Conclusions:
- Integrated methods combining visualization, analysis, and management effectively support knowledge discovery.
- The surveyed applications confirm the approach's value for multi-dimensional scientific data.
- Advanced tools are crucial for tackling the challenges of modern scientific data exploration.
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