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July-Aug. 2012 (vol. 32 no. 4)
pp. 23-25
Pak Chung Wong, Pacific Northwest National Laboratory
Han-Wei Shen, Ohio State University
Valerio Pascucci, University of Utah
Extreme-scale visual analytics (VA) is about applying VA to extreme-scale data. The articles in this special issue examine advances related to extreme-scale VA problems, their analytical and computational challenges, and their real-world applications.

1. P.C. Wong and J. Thomas, "Visual Analytics," IEEE Computer Graphics and Applications, vol. 24, no. 5, 2004, pp. 20–21.
2. J.J. Thomas and K.A. Cook eds., Illuminating the Path—, the Research and Development Agenda for Visual Analytics, IEEE CS, 2005.
3. P.C. Wong et al., "The Top 10 Challenges in Extreme-Scale Visual Analytics," IEEE Computer Graphics and Applications, vol. 32, no. 4, 2012, pp. 63–67.
4. S. Ashby et al., The Opportunities and Challenges of Exascale Computing: Summary Report of the Advanced Scientific Computing Advisory Committee (ASCAC) Subcommittee, US Dept. of Energy Office of Science, 2010; pdf/reportsExascale_subcommittee_report.pdf.
5. "Cisco Visual Networking Index: Forecast and Methodology, 2010–2015," Cisco, 2011; ns341/ns525/ns537/ns705/ns827white_paper_c11-481360_ns827_Networking_Solutions_White_Paper.html.

Index Terms:
Special issues and sections,Visual analytics,Large-scale systems,turbulent flow,Special issues and sections,Visual analytics,Large-scale systems,flow fields,extreme-scale visual analytics,visual analytics,computer graphics,Scientific Discovery through Advanced Computing,SciDAC,graph algebra
Pak Chung Wong, Han-Wei Shen, Valerio Pascucci, "Extreme-Scale Visual Analytics," IEEE Computer Graphics and Applications, vol. 32, no. 4, pp. 23-25, July-Aug. 2012, doi:10.1109/MCG.2012.73
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