Browsing by Author "Humayoun, Shah Rukh"

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    Chord2DS: An Extension to Chord Diagram to Show Data Elements from Two Heterogeneous Data Sources
    (The Eurographics Association, 2022) Humayoun, Shah Rukh; Brahmadevara, Likhitha; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    The standard Chord diagram, a radial layout, shows data elements in a circular fashion from one data source. In this paper, we propose an extension to the standard Chord diagram to show data elements from two heterogeneous data sources into one single diagram. The main Chord diagram is used for showing data elements and the relations between them from one data source, while we use an outer layer to show data elements from the second data source. The relationships between data elements from both data sources are shown through visual cues. The proposed solution uses space efficiently compared to using multiple diagrams in the scenarios of two heterogeneous data sources.
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    A User-based Visual Analytics Workflow for Exploratory Model Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Cashman, Dylan; Humayoun, Shah Rukh; Heimerl, Florian; Park, Kendall; Das, Subhajit; Thompson, John; Saket, Bahador; Mosca, Abigail; Stasko, John; Endert, Alex; Gleicher, Michael; Chang, Remco; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an accurate predictive model for future use. In that case, users are more interested in generating of diverse and robust predictive models, verifying their performance on holdout data, and selecting the most suitable model for their usage scenario. In this paper, we consider the concept of Exploratory Model Analysis (EMA), which is defined as the process of discovering and selecting relevant models that can be used to make predictions on a data source. We delineate the differences between EMA and the well-known term exploratory data analysis in terms of the desired outcome of the analytic process: insights into the data or a set of deployable models. The contributions of this work are a visual analytics system workflow for EMA, a user study, and two use cases validating the effectiveness of the workflow. We found that our system workflow enabled users to generate complex models, to assess them for various qualities, and to select the most relevant model for their task.