Browsing by Author "Childers, Taylor"

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    Concept-Driven Visual Analytics: an Exploratory Study of Model- and Hypothesis-Based Reasoning with Visualizations
    (Association for Computer Machinery, 2019) Choi, In Kwon; Childers, Taylor; Raveendranath, Nirmal Kumar; Mishra, Swati; Harris, Kyle; Reda, Khairi
    Visualization tools facilitate exploratory data analysis, but fall short at supporting hypothesis-based reasoning. We conducted an exploratory study to investigate how visualizations might support a concept-driven analysis style, where users can optionally share their hypotheses and conceptual models in natural language, and receive customized plots depicting the fit of their models to the data. We report on how participants leveraged these unique affordances for visual analysis. We found that a majority of participants articulated meaningful models and predictions, utilizing them as entry points to sensemaking. We contribute an abstract typology representing the types of models participants held and externalized as data expectations. Our findings suggest ways for rearchitecting visual analytics tools to better support hypothesis- and model-based reasoning, in addition to their traditional role in exploratory analysis. We discuss the design implications and reflect on the potential benefits and challenges involved.
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    Concept-Driven Visual Analytics: an Exploratory Study of Model- and Hypothesis-Based Reasoning with Visualizations
    (ACM, 2019-05) Choi, In Kwon; Childers, Taylor; Raveendranath, Nirmal Kumar; Mishra, Swati; Harris, Kyle; Reda, Khairi; Human-Centered Computing, School of Informatics and Computing
    Visualization tools facilitate exploratory data analysis, but fall short at supporting hypothesis-based reasoning. We conducted an exploratory study to investigate how visualizations might support a concept-driven analysis style, where users can optionally share their hypotheses and conceptual models in natural language, and receive customized plots depicting the fit of their models to the data. We report on how participants leveraged these unique affordances for visual analysis. We found that a majority of participants articulated meaningful models and predictions, utilizing them as entry points to sensemaking. We contribute an abstract typology representing the types of models participants held and externalized as data expectations. Our findings suggest ways for rearchitecting visual analytics tools to better support hypothesis- and model-based reasoning, in addition to their traditional role in exploratory analysis. We discuss the design implications and reflect on the potential benefits and challenges involved.
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    Dynamic Glyphs: Appropriating Causality Perception in Multivariate Visual Analysis
    (2019) Reda, Khairi; Potts, Caleb; Childers, Taylor; Human-Centered Computing, School of Informatics and Computing
    We investigate how to co-opt the perception of causality to aid the analysis of multivariate data. We propose Dynamic Glyphs (DyGs), an animated extension to traditional glyphs. DyGs encode data relations through seemingly physical interactions between glyph parts. We hypothesize that this representation gives rise to impressions of causality, enabling observers to reason intuitively about complex, multivariate dynamics. In a crowdsourced experiment, participants' accuracy with DyGs exceeded or was comparable to non-animated alternatives. Moreover, participants showed a propensity to infer higher-dimensional relations with DyGs. Our findings suggest that visual causality can be an effective 'channel' for communicating complex data relations that are otherwise difficult to think about. We discuss the implications and highlight future research opportunities.
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    Towards Concept-Driven Visual Analytics
    (IEEE, 2018) Choi, In Kwon; Mishra, Swati; Harris, Kyle; Raveendranath, Nirmal Kumar; Childers, Taylor; Reda, Khairi
    Visualizations of data provide a proven method for analysts to explore and make data-driven discoveries. However, current visualization tools provide only limited support for hypothesis-driven analyses, and often lack capabilities that would allow users to visually test the fit of their conceptual models against the data. This imbalance could bias users to overly rely on exploratory visual analysis as the principal mode of inquiry, which can be detrimental to discovery. To address this gap, we propose a new paradigm for 'concept-driven' visual analysis. In this style of analysis, analysts share their conceptual models and hypotheses with the system. The system then uses those inputs to drive the generation of visualizations, while providing plots and interactions to explore places where models and data disagree. We discuss key characteristics and design considerations for concept-driven visualizations, and report preliminary findings from a formative study.