Database Reference
In-Depth Information
1. View configuration: The system must permit the customization of views so
as to offer multiple means of understanding and visually querying the data. It
should allow for a change of mapping between data and visual dimensions.
The system should also provide smooth transitions between visual configura-
tions. Hence, the user will be able to visually track patterns between different
view configurations.
2. View organization and navigation: The system must also permit the display
of multiple views. The user must be able to visually compare different visual
configurations of the data set. This can be done with a matrix scatterplot or
juxtaposed views.
3. View filtering: The system must allow the user to filter out trajectories and
then reduce cluttering.
4. Trajectory selections and Boolean operations: The system must enable the
user to select trajectories and combine them in order to perform complex
queries. Some systems allow multiple selections sometimes called “layers.”
Users can combine layers with Boolean operation by applying an “and”
operation when they try to group differently selected trajectories.
12.4.2 Implementation Instance: FromDaDy
We have developed
FromDaDy
(
Hurter et al.
,
2009
) (which stands for “From
Data to Display”), a visualization tool that tackles the challenge of representing
and interactingwith numerous trajectories (several million trajectories composed
of up to 10 million points). FromDaDy employs a simple paradigm to explore
multidimensional data based on scatterplots, brushing, “pick and drop,” juxta-
posed views, and rapid visual configuration. Together with a finely tuned mix
between design customization and simple interaction, users can filter, remove,
and add trajectories in an incremental manner until they extract a set of relevant
data, thus formulating complex queries.
12.4.3 Views Organization and Navigation
A FromDaDy session starts with a view displaying all the data in one scatterplot.
The visualization employs a default visual configuration, for example, the map-
ping between data dimensions and visual variables. The view is inside a window,
and occupies a cell in a virtual infinite grid that extends from the four sides of
the cell. The user can configure the two axes of each scatterplot and use other
visual variables such as color and line width to display data set dimensions. For
instance, in Figure
12.3
, the user attached the data set field latitude to the
y
axis,
and the field longitude to the
x
axis. The user also chose to use the altitude to
color trajectory sections, showing, low altitudes in green and high altitudes in
blue.
