Browsing by Author "Xu, Shenyu"

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    QUESTO: Interactive Construction of Objective Functions for Classification Tasks
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Das, Subhajit; Xu, Shenyu; Gleicher, Michael; Chang, Remco; Endert, Alex; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Building effective classifiers requires providing the modeling algorithms with information about the training data and modeling goals in order to create a model that makes proper tradeoffs. Machine learning algorithms allow for flexible specification of such meta-information through the design of the objective functions that they solve. However, such objective functions are hard for users to specify as they are a specific mathematical formulation of their intents. In this paper, we present an approach that allows users to generate objective functions for classification problems through an interactive visual interface. Our approach adopts a semantic interaction design in that user interactions over data elements in the visualization are translated into objective function terms. The generated objective functions are solved by a machine learning solver that provides candidate models, which can be inspected by the user, and used to suggest refinements to the specifications. We demonstrate a visual analytics system QUESTO for users to manipulate objective functions to define domain-specific constraints. Through a user study we show that QUESTO helps users create various objective functions that satisfy their goals.
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    Resolving Conflicting Insights in Asynchronous Collaborative Visual Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Li, Jianping Kelvin; Xu, Shenyu; Ye, Yecong (Chris); Ma, Kwan-Liu; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Analyzing large and complex datasets for critical decision making can benefit from a collective effort involving a team of analysts. However, insights and findings from different analysts are often incomplete, disconnected, or even conflicting. Most existing analysis tools lack proper support for examining and resolving the conflicts among the findings in order to consolidate the results of collaborative data analysis. In this paper, we present CoVA, a visual analytics system incorporating conflict detection and resolution for supporting asynchronous collaborative data analysis. By using a declarative visualization language and graph representation for managing insights and insight provenance, CoVA effectively leverages distributed revision control workflow from software engineering to automatically detect and properly resolve conflicts in collaborative analysis results. In addition, CoVA provides an effective visual interface for resolving conflicts as well as combining the analysis results. We conduct a user study to evaluate CoVA for collaborative data analysis. The results show that CoVA allows better understanding and use of the findings from different analysts.