dowsing-machine.github.io/index.html at main · Dowsing-Machine/dowsing-machine.github.io · GitHub

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<html>

<head>
    <title>Dowsing</title>
</head>

<body>
    <header>
        <h1></h1>
        <h2>Dowsing: A Task-Driven Approach for Multiple-View Visualizations Dynamic Recommendation</h2>
        <hr>
    </header>
    <div>
        <img id="main_pic" width="100%" src="fig1.jpg">

    </div>

    <p>
        Most users are able to obtain their explorable ideas for a data table but cannot clearly
        declare their analysis tasks as visual queries. Visualization recommendation methods can reduce the demand for
        data and design knowledge of visual analysis by extracting or referring information in existing high-quality
        views.
    </p>
    <p>
        However, most solutions need users to articulate a precise constraint when they try to steer
        recommendations with their intentions and insights. To address this limitation, we allowed participants to
        create and alter multiple views for data exploration in a Workshop to further examine the analytical
        requirements entailed by users constantly generating charts. Our deep learning model was built using
        triangulation from Workshop to dynamically perceive the users’ analytical tasks in the editing sequence on data
        and views.
    </p>
    <p>We provided Dowsing, a mixed-initiative recommendation system for multiple-view visualizations. It
        can utilize and expose the user’s potential analysis tasks to recommend visualizations during the explorative
        building phase. Meanwhile, Dowsing allows users to confirm and edit their intentions as they explore further to
        quickly adapt to changing the analysis requirements. We evaluated our deep learning model through quantitative
        experiments and verified the effectiveness of Dowsing by user study using vega-datasets.
    </p>
    <img width="100%" src="workflow.jpg">
    <p>The workflow of Dowsing is shown as above. The system can be devided into two main parts: the perception and the
        recommendation generation. The preception uses LSTM model, together with statistical methods, to predict user
        intent. The user intent, represented as a set of constraints, is mixed with data facts and generic visualization
        rules and used
        together with the ASP solver in recommendation generation, which in turn yields a series of recommended views.
    </p>
    <p>You can watch our overview video below.</p>
    <video style="width: 100%;" controls
        src="https://dowsing-1256314557.cos.ap-chengdu.myqcloud.com/%231125_Dowsing_Demo%20Video.mp4"></video>
    <a href="http://dowsing-machine.com">Demo</a>
    <a href="https://github.com/Dowsing-Machine/Dowsing">Github</a>
</body>

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