CandidTree: visualizing structural uncertainty in similar hierarchies

  • Authors:

  • Bongshin Lee;George G. Robertson;Mary Czerwinski;Cynthia Sims Parr

  • Affiliations:

  • Microsoft Research, One Microsoft Way, Redmond, WA;Microsoft Research, One Microsoft Way, Redmond, WA;Microsoft Research, One Microsoft Way, Redmond, WA;Human-Computer Interaction Lab, Institute for Advanced Computer Studies, University of Maryland, College Park, MD

  • Venue:
  • Information Visualization
  • Year:
  • 2007

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Abstract

Most visualization systems fail to convey uncertainty within data. To provide a way to show uncertainty in similar hierarchies, we interpreted the differences between two tree structures as uncertainty. We developed a new interactive visualization system called CandidTree that merges two trees into one and visualizes two types of structural uncertainty: location and sub-tree structure uncertainty. Since CandidTree can visualize the differences between two tree structures, we conducted a series of user studies with tree-comparison tasks. First a usability study was conducted to identify major usability issues and evaluate how our system works. Another qualitative user study was conducted to see if biologists, who regularly work with hierarchically organized names, are able to use CandidTree, and to assess the 'uncertainty' metric we used. A controlled experiment with software engineers was conducted to compare CandidTree with WinDiff, a traditional files and folders comparison tool. The results showed that users performed better with CandidTree. Furthermore, CandidTree received better satisfaction ratings and all users preferred CandidTree to WinDiff.