Browsing by Author "Choi, In Kwon"

Now showing 1 - 6 of 6
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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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    Geo-Temporal Visualization for Tourism Data Using Color Curves
    (2019-05) Choi, In Kwon; Fang, Shiaofen; Xia, Yuni; Zheng, Jiang-Yu
    For individuals in the tourism industry and other businesses, the department of tourism in the government, or the individuals who are planning a travel, the data of tourist population movement can be a valuable resource that can uncover insights that could bring more profit and more tourists, or make the trip more enjoyable. As visualization is an effective way of conveying information with multiple dimensions, we would like to visualize the geo-temporal floating population data of tourists and residents in Jeju island in the Republic of Korea in two-dimensional space. In this study, we introduce the two methods we have implemented for visualizing the geo-temporal data using color curves as the representation of time dimension. We use the dots as the markers of floating population, and each color of dots represents the 24 hours of a day. In the first method, we plot the colored dots directly on the map, thereby coloring the area the data represents. In the second method, we plot the same dots inside a semi-transparent circle divided into arcs that represent each month of a year. The user can compare the population of tourists and residents between the different times of a day, the different months and the weather conditions to analyze the floating population in the given area.
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    TopMSV: A Web-Based Tool for Top-Down Mass Spectrometry Data Visualization
    (American Chemical Society, 2021) Choi, In Kwon; Jiang, Tianze; Kankara, Sreekanth Reddy; Wu, Si; Liu, Xiaowen; BioHealth Informatics, School of Informatics and Computing
    Top-down mass spectrometry (MS) investigates intact proteoforms for proteoform identification, characterization, and quantification. Data visualization plays an essential role in top-down MS data analysis because proteoform identification and characterization often involve manual data inspection to determine the molecular masses of highly charged ions and validate unexpected alterations in identified proteoforms. While many software tools have been developed for MS data visualization, there is still a lack of web-based visualization software designed for top-down MS. Here, we present TopMSV, a web-based tool for top-down MS data processing and visualization. TopMSV provides interactive views of top-down MS data using a web browser. It integrates software tools for spectral deconvolution and proteoform identification and uses analysis results of the tools to annotate top-down MS data.
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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.
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    Visual (dis)Confirmation: Validating Models and Hypotheses with Visualizations
    (IEEE, 2019-07) Choi, In Kwon; Raveendranath, Nirmal Kumar; Westerfield, Jared; Reda, Khairi; Human-Centered Computing, School of Informatics and Computing
    Data visualization provides a powerful way for analysts to explore and make data-driven discoveries. However, current visual analytic tools provide only limited support for hypothesis-driven inquiry, as their built-in interactions and workflows are primarily intended for exploratory analysis. Visualization tools notably lack capabilities that would allow users to visually and incrementally test the fit of their conceptual models and provisional hypotheses against the data. This imbalance could bias users to overly rely on exploratory analysis as the principal mode of inquiry, which can be detrimental to discovery. In this paper, we introduce Visual (dis) Confirmation, a tool for conducting confirmatory, hypothesis-driven analyses with visualizations. Users interact by framing hypotheses and data expectations in natural language. The system then selects conceptually relevant data features and automatically generates visualizations to validate the underlying expectations. Distinctively, the resulting visualizations also highlight places where one's mental model disagrees with the data, so as to stimulate reflection. The proposed tool represents a new class of interactive data systems capable of supporting confirmatory visual analysis, and responding more intelligently by spotlighting gaps between one's knowledge and the data. We describe the algorithmic techniques behind this workflow. We also demonstrate the utility of the tool through a case study.