Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data
| Autor: | Hans Hagen, Mark D. Biggin, Sean Ahern, Michael B. Eisen, Angela H. DePace, Gunther H. Weber, Peter Messmer, Hank Childs, Daniela Ushizima, E. Wes Bethel, Jitendra Malik, Jeremy S. Meredith, Cameron Geddes, Kesheng Wu, Charless C. Fowlkes, Estelle Cormier-Michel, Prabhat, Soile V.E. Keranen, Min-Yu Huang, Bernd Hamann, Oliver Rubel, Chris L. Luengo Hendriks, David W. Knowles |
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| Rok vydání: | 2013 |
| Předmět: |
Scientific visualization
Discovery science Measure (data warehouse) Data exploration Multi-dimensional data Computer science business.industry Data management Data analysis Data science Article Visualization Information visualization Knowledge extraction Biological data visualization General Earth and Planetary Sciences business Laser wakefield particle acceleration 3D gene expression General Environmental Science |
| Zdroj: | ICCS |
| ISSN: | 1877-0509 |
| Popis: | Knowledge discovery from large and complex scientific data is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. The combination and close integration of methods from scientific visualization, information visualization, automated data analysis, and other enabling technologies—such as efficient data management—supports knowledge discovery from multi-dimensional scientific data. This paper surveys two distinct applications in developmental biology and accelerator physics, illustrating the effectiveness of the described approach. |
| Databáze: | OpenAIRE |
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