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.
LeeBParrCSCampbellDBedersonBB. How users interact with biodiversity information using taxontree. In: CostabileMF (Ed). Proceedings of Advanced Visual Interfaces 2004 (Gallipoli, Italy), ACM Press: New York. 2004; 320–327.
3.
LeeBRobertsonGCzerwinskiMParrCS. CandidTree: visualizing structural uncertainty in similar hierarchies. In: BaranauskasMCCPalanquePAAbascalJBarbosaSDJ (Eds). Proceedings of Interact 2007. Lecture Notes in computer Science, Vol. 4663 (Rio de Janeiro, Brazil), Springer-Verlag: New York, NY, USA, 2007; 250–263.
4.
GershonND. Visualization of an imperfect world. IEEE Computer Graphics and Applications1998; 18: 43–45.
5.
JohnsonCRSandersonAR. A next step: Visualizing errors and uncertainty. IEEE Computer Graphics and Applications2003; 23: 6–10.
6.
PangA. Visualizing uncertainty in geo-spatial data. In: Workshop on the Intersections between Geospatial Information and Information Technology, prepared for the National Academies committee of the Computer Science and Telecommunications Board, 2001.
7.
PangATWittenbrinkCMLodhaSK. Approaches to uncertainty visualization. The Visual Computer1997; 13: 370–390.
8.
TaylorBNKuyattCE. Guidelines for evaluating and expressing the uncertainty of NIST measurement results. NIST Technical Note 1297, 1994.
9.
ClevelandWS. The Elements of Graphing Data.1985.
10.
EatonCPlaisantCDrizdT. Visualizing missing data: graph interpretation user study. In: CostabileMFPaternóF (Eds). Proceedings of the Interact 2005, Lecture Notes in Computer Science, Vol. 3585 (Rome, Italy), Springer-Verlag: New York, NY, USA, 2005; 861–872.
11.
OlstonCMackinlayJD. Visualizing data with bounded uncertainty. In: IEEE Symposium on Information Visualization 2002 (Boston, MA, USA), IEEE Computer Society Press: Chicago, 2002; 37–40.
12.
SuloREickSGrossmanR. DaVis: A tool for visualizing data quality. Posters Compendium of Information Visualization2005 Minneapolis, MN, USA.
13.
TukeyJW. Exploratory data analysis, 1977, Addison–Wesley: Reading, MA, USA.
14.
FegeasRGCascioJLLazarRA. An Overview of FIPS 173, the spatial data transfer standard. Cartography and Geographic Information Systems1992; 19: 278–293.
15.
MacEachrenAMRobinsonAHopperSGardnerSMurrayRGaheganMHetzlerE. Visualizing geospatial information uncertainty: What we know and what we need to know. Cartography and Geographic Information Science2005; 32: 139–160.
16.
MoelleringH. Continuing research needs resulting from the SDTS development effort. Cartography and Geographic Information Systems1994; 21: 180–189.
17.
MorrisonJ. The proposed standard for digital cartographic data. American Cartographer1988; 15: 9–140.
18.
ThomsonJHetzlerBMacEachrenAGaheganMPavelM. A typology for visualizing uncertainty. In: ErbacherRFChenPCRobertsJCGroehnMBörnerK (Eds). Proceedings of the SPIEVDA (San Jose, CA, USA), The Society for Imaging Science and Technology (IS & T): Springfield, VA, USA, 2005; 146–157.
19.
LodhaSKPangASheehanREWittenbrinkCM. UFLOW: Visualizing uncertainty in fluid flow. In: IEEE Symposium on Visualization 1996.IEEE Computer Society Press: Chicago, 1996; 249–255.
20.
WittenbrinkCMPangATLodhaSK. Glyphs for visualizing uncertainty in vector fields. IEEE Transactions on Visualization and Computer Graphics1996; 2: 266–279.
21.
GrigoryanGRheingansP. Point-based probabilistic surfaces to show surface uncertainty. IEEE Transactions on Visualization and Computer Graphics2004; 10: 564–573.
22.
GershonND. Visualization of fuzzy data using generalized animation. In: KaufmanAENielsonGM (Eds). IEEE Symposium on Visualization 1992 (Boston, MA, USA), IEEE Computer Society Press: Chicago, 1992; 268–273.
23.
LodhaSKWilsonCMSheehanRE. LISTEN: Sounding uncertainty visualization. In: IEEE Symposium on Visualization 1996.IEEE Computer Society Press: Chicago, 1996; 189–196.
24.
GrietheHSchumannH. The Visualization of Uncertain Data: Methods and Problems. In: SchulzeTHortonGPreimBSchlechtwegS (Eds). Proceedings of SimVis2006 (Magdeburg, Germany), 143–156.
25.
PangAFreemanA. Methods for comparing 3D surface attributes, Proceedings of the SPIE-VDA1996; 58–64.
26.
NyeTMWLioPGilksWR. A novel algorithm and web-based tool for comparing two alternative phylogenetic trees. Bioinformatics2006; 22: 117–119.
27.
MunznerTGuimbretiereFTasiranSZhangLZhouY. TreeJuxtaposer: Scalable tree comparison using Focus+Context with Guaranteed Visibility. Proceedings of SIGGRAPH 2003, published as ACM Transactions on Graphics2003; 22: 453–462.
28.
SpenkeMBeilkenC. Visualization of trees as highly compressed tables with infoZoom. Posters Compendium of Information Visualization2003; 122–123.
29.
MorseDRYtowNRobertsDMSatoA. Comparison of multiple taxonomic hierarchies using taxoNote. Posters Compendium of Information Visualization2003 (Seattle, WA, USA), 126–127.
30.
HongJYD'AndriesJRichmanMWestfallM. Zoomology: comparing two large hierarchical trees. Posters Compendium of Information Visualization2003 (Seattle, WA, USA), 120–121.
31.
LevenshteinVI. Binary codes capable of correcting deletions, insertions, and reversals. Doklady Akademii Nauk SSSR1965; 163: 845–848 (in Russian); English translation in Soviet Physics Doklady 1966; 10: 707–710.
32.
FurnasGW. Generalized fisheye views. In: ManteiMOrbetonP (Eds). Proceedings of the CHI 1986 (Boston, MA, USA), ACM Press: New York, 1986; 16–23.
33.
BedersonBBGrosjeanJMeyerJ. Toolkit design for interactive structured graphics. IEEE Transactions on Software Engineering2004; 30: 535–546.
34.
WalkerQII. A node-positioning algorithm for general trees. Software: Practice and Experience1990; 20: 685–705.
