Using Visual Representations of Data to Enhance Sensemaking in Data Exploration Tasks

摘要

This paper explains how visual representations of data enable individual sensemaking in data exploration tasks. We build upon ntheories of human perception and cognition, including Cognitive Fit Theory, to explain what aspects of visual representations nfacilitate sensemaking for the viewer. We make three primary contributions. First, we give a general characterization of visual nrepresentations that would be used for data exploration tasks. These representations consist of a scene, objects within the scene, nand the characteristics of those objects. Second, we extend Cognitive Fit Theory into the data exploration task domain. We nexplain that the data exploration task has a number of spatial subtasks including observing data points, looking for patterns or noutliers, making inferences, comparing observed facts or patterns to one’s own knowledge, generating hypotheses about the data, nand drawing analogies from the context being observed to another context. Third, we offer a set of theoretical propositions about nhow visual representations of data can serve the sensemaking goal. Specifically, visual representations best facilitate sensemaking nin data exploration tasks when they (1) support the four basic human visual perceptual approaches of association, differentiation, nordered perception, and quantitative perception, (2) have strong Gestalt properties, (3) are consistent with the viewer’s stored nknowledge, and (4) support analogical reasoning. We propose that visual representations should possess several of these four naspects to make them well-suited for the task of data exploration.