For a given data type, there are many visual representation techniques to choose from. Often, a technique contributes to the finding of one visual feature, while another visually extracts a different visual feature. Fortunately, the spreadsheet environment assists in the organization and display of various visual representations. Because our system can be easily extended to handle new techniques, it allows us to quickly prototype and compare several representation techniques. Here we show this flexibility in all three data domains.
By constructing several modules for different visual representations of matrices, we used the SIV spreadsheet to answer specific scientific questions on protein residue substitution time-series matrices. For example, we used it to discover several novel patterns in these matrices. In Figure 2, the cube representation used in the first column shows the interesting variation of the diagonal entries more clearly than the other representation methods. The entry represented by the orange cube varies more than any other entry. The carpet plot technique used in the second column shows that the matrices have different ranges of values (i.e. the colors get brighter and brighter from top to bottom). In the third column, the bar-plot technique makes comparing a specific entry from matrix to matrix easy, and shows shows the overall trend of most off-diagonal entries to decrease.
The algorithm visualization of Figure 3 shows several different visual representations of a random 3D point generator. Row 1 shows the results of the random point generator as 3D scatter plots. We can see the spread of the points quite well in this representation. Row 2 shows the same data using transparent tetrahedra after 3D Delaunay triangulation has been performed on the point sets. Through interactive rotation, this representation gives a better view of the relative placement of the points. It also shows the convex hulls of the point sets, and how the hulls change between steps of the algorithm. Row 3 represents the Delaunay triangulation as edges rather than tetrahedra, thus giving a better view of the interior structure of the triangulation.
Like SIV, SSR also allows changing of visual representation for similarity reports. A mapping tool enables the user to choose the geometric representation used by the cell(s). In Figure 1, the cells in Row C and D contain the same datasets as the corresponding cells in Row A, but we changed the mapping in Row C and D to show different variables of the similarity report. In this organization, the cells in a given column represent the same value; however, each row offers a different view of the data. The ability to map different variables to different axes in different cells results in an improved ability to see more variables simultaneously. (In SSR, all these operations are accomplished via a click-and-point interface. The user loads the columns with data one column at a time, and changes the mapping of the data of each row using the mapping tool dialog box. Shown in Figure 4, the mapping tool is implemented as a pull-down menu for each axis.)
Our experience shows the elegant organization of the spreadsheet allows interesting ways of combining different visual representations of the underlying data. Users can compare and visually extract different features from the different representations. The spreadsheet environment equips users with the necessary tools to explore the representation space.