Readings in information visualization
Visualizing the non-visual: spatial analysis and interaction with information from text documents
INFOVIS '95 Proceedings of the 1995 IEEE Symposium on Information Visualization
An Insight-Based Longitudinal Study of Visual Analytics
IEEE Transactions on Visualization and Computer Graphics
Information Visualization: Design for Interaction (2nd Edition)
Information Visualization: Design for Interaction (2nd Edition)
Toward a Deeper Understanding of the Role of Interaction in Information Visualization
IEEE Transactions on Visualization and Computer Graphics
A Framework of Interaction Costs in Information Visualization
IEEE Transactions on Visualization and Computer Graphics
Building and applying a human cognition model for visual analytics
Information Visualization
Information Visualization
Semantic interaction for visual text analytics
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Developing guidelines for assessing visual analytics environments
Information Visualization - Special issue on Evaluation for Information Visualization
Fluid interaction for information visualization
Information Visualization - Special issue on State of the Field and New Research Directions
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With the growing need for visualization to aid users in understanding large, complex datasets, the ability for users to interact and explore these datasets is critical. As visual analytic systems have advanced to leverage powerful computational models and data analytics capabilities, the modes by which users engage and interact with the information are limited. Often, users are taxed with directly manipulating parameters of these models through traditional GUIs (e.g., using sliders to directly manipulate the value of a parameter). However, the purpose of user interaction in visual analytic systems is to enable visual data exploration -- where users can focus on their task, as opposed to the tool or system. As a result, users can engage freely in data exploration and decision-making, for the purpose of gaining insight. In this position paper, we discuss how evaluating visual analytic systems can be approached through user interaction analysis, where the goal is to minimize the cognitive translation between the visual metaphor and the mode of interaction (i.e., reducing the "interaction junk"). We motivate this concept through a discussion of traditional GUIs used in visual analytics for direct manipulation of model parameters, and the importance of designing interactions the support visual data exploration.