Abstract: A method, system, and computer program product for a new data visualization tool for determining distribution weights that represent values of a categorical variable and then mapping a distinct color to each of the weights so as to visually represent the different values of the categorical variable (or data attribute) in a scatter plot. The distinct colors of a splat are based on the distribution of categorical variable values in a corresponding bin, the distribution of which is represented by a vector. The vector contains as many locations as the number of different values for the categorical variable. The value stored in each location is typically a weight or percentage for that particular value of the categorical variable. Each location in the vector is also associated with a distinct color.

Abstract: A method, system, and computer program product are provided for a new data visualization tool that visually approximates a scatter plot and provides smooth animation of graphics by using interpolated data. An interpolation process is performed over pre-processed bins of scattered data points. A user can smoothly animate a plot along several dimensions by using an external query device to vary a selected data attribute. If the external query device (or slider) control is positioned in between discrete positions of the slider, the displayed plot corresponds to interpolated data. Adjacent data structures are determined corresponding to the position of the external query device. The adjacent data structures are merged together, then aggregated the using the spatial columns of the data structure as a unique key. An interpolated bin is generated, where the weight of the bin is interpolated and the dependent attribute is also interpolated and weighted.

Abstract: A method, system, and computer program product are provided for visually approximating a scatter plot. Bins of scattered data points are formed. Each axis of a scatter plot is discretized according to a binning resolution. Bin positions along each discretized scatter plot axis are determined from the bin numbers. The bins, which represent a cloud of scattered data points, are volume rendered as splats. The opacity of each splat is a function of the number (count) of data points in a corresponding bin. In one example, the opacity of a splat is determined by the following equation:opacity=1-exp.sup.(-u*count),where count is the number of scattered data points in a corresponding bin, u is a global scale factor which can be varied by a slider, and exp is an exponential function. The color of the splat represents a data attribute associated with the data points in a corresponding bin.