on how records are mapped to marks. For example, selecting a bar in an ordinal–quantitative pane will result in a bar
chart, whereas selecting a line mark results in a line chart.
The mark set currently supported in Polaris includes the
rectangle, circle, glyph, text, Gantt bar, line, polygon, and
image. There are two types of marks; single tuple marks and
multituple marks. Multituple marks form a single graphical entity from a set of marks; an example is a polygon mark
where each vertex of the polygon is a single tuple.
Following Cleveland, 8 we further structure the space of
graphics by the number of independent and dependent
variables. For example, a graphic where both axes encode
independent variables is different than a graphic where one
axis encodes an independent variable and the other encodes
a dependent variable ( y = f (x)). By default, dimensions of
the database are interpreted as independent variables and
measures as dependent variables. We briefly discuss the
defining characteristics of the three families within our categorization. It should be noted that these rules allow us to
automatically choose a default mark given the types of the
fields on the axes.
ordinal–ordinal graphics: The characteristic member of
this family is the table, either of numbers or of marks encoding attributes of the source records.
In ordinal–ordinal graphics, the axis variables are typically independent of each other, and the task is focused on
understanding patterns and trends in some function f (O ,O )
AE R, where R represents the fields encoded in the retinal
properties of the marks. This can be seen in the heatmap in
Figure 3, where the analyst is studying gene expression as a
function of experiment and gene. Figure 6(a) shows another
example where lines of source code are color-encoded with
the number of cache misses attributable to that line.
ordinal–Quantitative graphics: Well-known representatives
of this family of graphics are the bar chart, the dot plot, and
the Gantt chart.
In an ordinal–quantitative graphic, the quantitative variable is often dependent on the ordinal variable, and the
analyst is trying to understand or compare the properties of
some set of functions f (O) AE Q. The cardinality of the record
set affects the structure of the graphics in this family: When
the cardinality of the record set is one, the graphics are simple bar charts or dot plots. When the cardinality is greater
than one, additional structure may be introduced to accommodate the additional records (e.g., a stacked or clustered
The ordinal and quantitative values may be independent
variables, such as in a Gantt chart. Here, each pane represents all events in a category; each event has a type as well as
a beginning and end time. Figure 6(c) shows a table of Gantt
charts, with each Gantt chart displaying the thread scheduling and locking activity on a CPU within a multiprocessor
Quantitative–Quantitative graphics: Graphics of this type
are used to understand the distribution of data as a function
of one or both quantitative variables and to discover causal
relationships between the two quantitative variables, such
as in a scatterplot matrix. Figure 3 illustrates another example of a quantitative–quantitative graphic: the map. In this
figure, the analyst is studying election results by county.
3. 3. Visual mappings
Each record in a pane is mapped to a mark. There are two
components to the visual mapping. The first component,
described in Section 3. 2, determines the type of graphic and
mark. The second component encodes fields of the records
into visual or retinal properties of the selected mark. The
visual properties in Polaris are based on Bertin’s retinal
variables: 4 shape, size, orientation, color (value and hue),
Allowing analysts to explicitly encode different fields of
the data to retinal properties of the display greatly enhances
the data density and the variety of displays that can be generated. However, in order to keep the specification succinct,
analysts should not be required to construct the mappings.
Instead, they should be able to simply specify that a field be
figure 3: the families of graphics within our taxonomy with examples of well-known charts from each family. the taxonomy structures
the space of graphics into three families by the types of fields assigned to their axes and then further structures each family by the
number of independent and dependent variables. using this taxonomy we can derive the type of graphic within each pane from the table
axes expressions and the mark type.