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<article article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="aggregator">72010604</journal-id>
      <journal-title>Electronic Imaging</journal-title>
      <issn pub-type="ppub">2470-1173</issn><issn pub-type="epub"></issn>
      <publisher>
        <publisher-name>Society for Imaging Science and Technology</publisher-name>
        <publisher-loc>7003 Kilworth Lane, Springfield, VA 22151 USA</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.2352/ISSN.2470-1173.2020.1.VDA-388</article-id>
      <article-id pub-id-type="sici">2470-1173(20200126)2020:1L.3881;1-</article-id>
      <article-id pub-id-type="publisher-id">ei_24701173_v2020n1_input/s6.xml</article-id>
      <article-id pub-id-type="other">/ist/ei/2020/00002020/00000001/art00006</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Visualization of Search Results of Large Document Sets</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Anderson</surname>
            <given-names>James D</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Wischgoll</surname>
            <given-names>Thomas</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>26</day>
        <month>01</month>
        <year>2020</year>
      </pub-date>
      <volume>2020</volume>
      <issue>1</issue>
      <fpage>388-1</fpage>
      <lpage>388-7</lpage>
      <permissions>
        <copyright-year>2020</copyright-year>
      </permissions>
      <abstract>
        <p>
          <italic>When presented with many search results, finding information or patterns within the data poses a challenge. This paper presents the design, implementation and evaluation of a visualization enabling users to browse through voluminous information and comprehend the data. Implemented
 with the JavaScript library Data Driven Documents (D3), the visualization represents the search as clusters of similar documents grouped into bubbles with the contents depicted as word-clouds. Highly interactive features such as touch gestures and intuitive menu actions allow for expeditious
 exploration of the search results. Other features include drag-and-drop functionality for articles among bubbles, merging nodes, and refining the search by selecting specific terms or articles to receive more similar results. A user study consisting of a survey questionnaire demonstrated that
 in comparison to a standard text-browser for viewing search results, the visualization performs commensurate or better on most metrics.</italic>
        </p>
      </abstract>
      <kwd-group>
        <kwd>Search Visualization</kwd>
        <kwd>Document Visualization</kwd>
        <kwd>Wordclouds</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
