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Visual analytic tools aim to support the cognitively demanding task of sensemaking. Their success often depends on the ability to leverage capabilities of mathematical models, visualization, and human intuition through flexible, usable, and expressive interactions. Spatially clustering data is one effective metaphor for users to explore similarity and relationships between information, adjusting the weighting of dimensions or characteristics of the dataset to observe the change in the spatial layout. Semantic interaction is an approach to user interaction in such spatializations that couples these parametric modifications of the clustering model with users' analytic operations on the data (e.g., direct document movement in the spatialization, highlighting text, search, etc.). In this paper, we present results of a user study exploring the ability of semantic interaction in a visual analytic prototype, ForceSPIRE, to support sensemaking. We found that semantic interaction captures the analytical reasoning of the user through keyword weighting, and aids the user in co-creating a spatialization based on the user's reasoning and intuition.
inference mechanisms, data visualisation, keyword weighting, semantic interaction, sensemaking, analytical reasoning, model steering, visual analytic tools, cognitively demanding task, mathematical models, visualization, human intuition, expressive interactions, spatially clustering data, parametric modifications, clustering model, visual analytic prototype, ForceSPIRE, Semantics, User interfaces, Analytical models, Visual analytics, Mathematical model, visual analytics, User Interaction, visualization, sensemaking, analytic reasoning

C. North, A. Endert and P. Fiaux, "Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering," in IEEE Transactions on Visualization & Computer Graphics, vol. 18, no. , pp. 2879-2888, 2012.
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