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A visual analytics agenda
Abstract
Researchers have made significant progress in disciplines such as scientific and information visualization, statistically based exploratory and confirmatory analysis, data and knowledge representations, and perceptual and cognitive sciences. Although some research is being done in this area, the pace at which new technologies and technical talents are becoming available is far too slow to meet the urgent need. National Visualization and Analytics Center's goal is to advance the state of the science to enable analysts to detect the expected and discover the unexpected from massive and dynamic information streams and databases consisting of data of multiple types and from multiple sources, even though the data are often conflicting and incomplete. Visual analytics is a multidisciplinary field that includes the following focus areas: (i) analytical reasoning techniques, (ii) visual representations and interaction techniques, (iii) data representations and transformations, (iv) techniques to support production, presentation, and dissemination of analytical results. The R&D agenda for visual analytics addresses technical needs for each of these focus areas, as well as recommendations for speeding the movement of promising technologies into practice. This article provides only the concise summary of the R&D agenda. We encourage reading, discussion, and debate as well as active innovation toward the agenda for visual analysis.
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Cited by
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Visual learning analytics of educational data: A systematic literature review and research agenda
Vieira, C., Parsons, P., & Byrd, V. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.03.018 · Cited by 316
Software traceability: trends and future directions
Cleland-Huang, J., Gotel, O. C. Z., Huffman Hayes, J., Mäder, P., & Zisman, A. (2014) · Future of Software Engineering Proceedings · doi:10.1145/2593882.2593891 · Cited by 309
Healthcare Expenditure and Economic Performance: Insights From the United States Data
Raghupathi, V., & Raghupathi, W. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00156 · Cited by 304
Visual Methods for Analyzing Time-Oriented Data
Aigner, W., Miksch, S., Muller, W., Schumann, H., & Tominski, C. (2008) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2007.70415 · Cited by 284
Metadata
- Authors
- Thomas, J. J., & Cook, K. A.
- Venue
- IEEE Computer Graphics and Applications
- Published
- 2006-01
- Volume
- 26
- Issue
- 1
- Pages
- 10-13
- ISSN
- 0272-1716 (ISSN-L)
- OpenAlex
- W2047257058
- Cited by
- 562
- References
- 0
- FWCI
- 19.21
- Type
- journal article
- Language
- en
- Open access
- closed
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