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Issue No.04 - July-Aug. (2013 vol.33)
pp: 6-13
To tackle the onset of big data, visual analytics seeks to marry the human intuition of visualization with mathematical models' analytical horsepower. A critical question is, how will humans interact with and steer these complex models? Initially, users applied direct manipulation to such models the same way they applied it to simpler visualizations in the premodel era--using control panels to directly manipulate model parameters. However, opportunities are arising for direct manipulation of the model outputs, where the users' thought processes take place, rather than the inputs. This article presents this new agenda for direct manipulation for visual analytics.
Visual analytics, Mathematical model, Analytical models, Visualization, Computational modeling, Data visualization,computer graphics, visual analytics, direct manipulation, visualization
A. Endert, L. Bradel, C. North, "Beyond Control Panels: Direct Manipulation for Visual Analytics", IEEE Computer Graphics and Applications, vol.33, no. 4, pp. 6-13, July-Aug. 2013, doi:10.1109/MCG.2013.53
1. B. Shneiderman and C. Plaisant, Designing the User Interface: Strategies for Effective Human-Computer Interaction, 4th ed., Pearson, 2005.
2. C. Andrews, A. Endert, and C. North, “Space to Think: Large, High-Resolution Displays for Sensemaking,” Proc. 2010 ACM Conf. Human Factors in Computing Systems (CHI 10), ACM, 2010 pp. 55-64.
3. W. Wright et al., “The Sandbox for Analysis: Concepts and Methods,” Proc. 2006 ACM Conf. Human Factors in Computing Systems (CHI 06), ACM, 2006 pp. 801-810.
4. F. Shipman and C. Marshall, “Formality Considered Harmful: Experiences, Emerging Themes, and Directions on the Use of Formal Representations in Interactive Systems,” Computer Supported Cooperative Work, vol. 8, no. 4, 1999 pp. 333-352.
5. F. Tyndiuk et al., “Cognitive Comparison of 3D Interaction in Front of Large vs. Small Displays,” Proc. 2005 ACM Symp. Virtual Reality Software and Technology (VAST 05), ACM, 2005 pp. 117-123.
6. J.A. Wise et al., “Visualizing the Non-visual: Spatial Analysis and Interaction with Information for Text Documents,” Proc. 1995 IEEE Symp. Information Visualization (InfoVis 95), IEEE CS, 1999 p. 51.
7. J. Alsakran et al., “Streamit: Dynamic Visualization and Interactive Exploration of Text Streams,” Proc. 2011 IEEE Pacific Visualization Symp. (PacificVis 11), IEEE, 2011 pp. 131-138.
8. D.H. Jeong et al., “iPCA: An Interactive System for PCA-Based Visual Analytics,” Computer Graphics Forum, vol. 28, no. 3, 2009 pp. 767-774.
9. J.S. Yi et al., “Dust & Magnet: Multivariate Information Visualization Using a Magnet Metaphor,” Information Visualization, vol. 4, no. 4, 2005 pp. 239-256.
10. A. Endert, P. Fiaux, and C. North, “Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering,” IEEE Trans. Visualization and Computer Graphics, vol. 18, no. 12, 2012 pp. 2879-2888.
11. A. Endert et al., “Observation-Level Interaction with Statistical Models for Visual Analytics,” Proc. 2011 IEEE Conf. Visual Analytics Science and Technology (VAST 11), IEEE, 2011 pp. 121-130.
12. E.T. Brown et al., “Dis-Function: Learning Distance Functions Interactively,” Proc. 2012 IEEE Conf. Visual Analytics Science and Technology (VAST 11), IEEE, 2012 pp. 83-92.
13. A.C. Robinson, “Design for Synthesis in Geo­visualization,” PhD thesis, Dept. of Geography, Pennsylvania State Univ., 2008.
14. S. Drucker, D. Fisher, and S. Basu, “Helping Users Sort Faster with Adaptive Machine Learning Recommendations,” Human-Computer Interaction—Interact 2011, LNCS 6948, Springer, 2011 pp. 187-203.
15. R. Heuer, Psychology of Intelligence Analysis, Center for the Study of Intelligence, 1999.
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