An Empirical Comparison of Three Commercial Information Visualization Systems Alfred Kobsa University of California, Irvine kobsa@uci.edu Abstract 1 An empirical comparison of three commercial infor- mation visualization systems on three different databases is presented. The systems use different paradigms for visualizing data. Tasks were selected to be "ecologically relevant", i.e. meaningful and interesting in the respec- tive domains. Users of one system turned out to solve problems significantly faster than users of the other two, while users of another system would supply significantly more correct answers. Reasons for these results and general observations about the studied systems are discussed. 1. Introduction This paper describes an empirical comparison of three commercially available visualization systems for multi- dimensional data. The three systems are Eureka (formerly TableLens) [6], InfoZoom (formerly Focus) [8, 9], and Spotfire [1]. 2 Each of them provides different means for visualizing data. Eureka offers a single visualization, which is table-like with rows being the objects and columns the dimensions (i.e., the attributes of objects). Figure 1a shows a Eureka visualization of one of the databases from our studies, containing self-descriptions of users of an online dating service. Nominal and ordinal data (like the answer to “Have you ever cheated on your boyfriend/girlfriend?” in column two, or the religion in column six) is depicted as color-coded bars. Continuous data is depicted as blue bars whose lengths correspond to their values. Eureka’s representation follows a Focus + Context paradigm [3], allowing one to view details within the surrounding context. A column may be sorted in ascend- ing or descending order by clicking on the category label, and if done so, the other columns will rearrange them- 1 I would like to thank Mike Lin, Sumera Razak and Sherry Sung for evaluating the experimental data, and Gloria Mark for helping with their analysis. 2 The software versions used were Eureka 1.1 from Inxight Software, Inc. (www.inxight.com), InfoZoom 3.24 EN Professional from humanIT AG (www.humanIT.com), and Spotfire.net Desktop 5.0 from Spotfire, Inc. (www.spotfire.com). The data sets used are available from http://www.ics.uci.edu/~kobsa/visexp/ . selves accordingly to make each row consistent to the same object. Positive and negative correlations between numerical categories can be detected in this way. Moving two columns to the far left groups their entries, as is the case for the columns “Gender” and “Did you cheat?” in Figure 1. It is also possible to filter out certain entries, and to highlight them. InfoZoom presents data in three different views. The wide view shows the current data set in a table format, with rows being the attributes and columns the objects. The compressed view packs the current data set horizon- tally to fit the window width. Numeric data values are plotted as horizontal cell-wide bars whose distance from the row bottom corresponds to their values. A row may be sorted in ascending or descending order, with the values in the other rows being rearranged accordingly to make each column consistent to the same object. Hierarchical sorting of two or more attributes is possible as well. Dependencies between characteristics (like correlations between numeric attributes and differences in the distribution of numeric attributes in dependence of one or more non-numeric attributes) can thereby be displayed. In the overview mode, the values in the rows become detached from their objects. Rows here represent the value distributions of attributes in ascending or descending order, and are independent of each other. Figure 1b shows that the people currently displayed are predominantly domiciled in California (attribute “State”, row 6), weigh between 88 and 190 pounds (“Weight”, row 14) and want their partners to be educated (“Partner educated?”, row 17). An important characteristic of all three views is that values of (identical adjacent) attributes become textually, numerically or symbolically displayed whenever space permits this. This considerably facilitates understanding the contents of databases. The central operation in InfoZoom is “zooming” into information subspaces by double-clicking on attribute values, or sets/ranges of values. InfoZoom thereupon shows records only that contain the specific attribute value(s). Slow-motion animation makes it easier to moni- tor the changes in the other attributes. In Figure 1b, for instance, the user has zoomed in on the “Yes” entries in the category “Did you cheat?” (row 2 from bottom). Info- Zoom also allows one to define new variables in depen- dence of existing ones, highlight extreme values, and create a variety of charts (mostly for reporting purposes). Presented at IEEE Symposium on Information Visualization October 22 - October 23, 2001 San Diego Paradise Point Hotel, San Diego