Databases Reference
In-Depth Information
2.3.1
Pixel-OrientedVisualizationTechniques
A simple way to visualize the value of a dimension is to use a pixel where the color of
the pixel reflects the dimension's value. For a data set of
m
dimensions,
pixel-oriented
techniques
create
m
windows on the screen, one for each dimension. The
m
dimension
values of a record are mapped to
m
pixels at the corresponding positions in the windows.
The colors of the pixels reflect the corresponding values.
Inside a window, the data values are arranged in some global order shared by all
windows. The global order may be obtained by sorting all data records in a way that's
meaningful for the task at hand.
Example2.16
Pixel-oriented visualization.
AllElectronics
maintains a customer information table,
which consists of four dimensions:
income
,
credit limit
,
transaction volume
, and
age
. Can
we analyze the correlation between
income
and the other attributes by visualization?
We can sort all customers in income-ascending order, and use this order to lay out
the customer data in the four visualization windows, as shown in Figure 2.10. The pixel
colors are chosen so that the smaller the value, the lighter the shading. Using pixel-
based visualization, we can easily observe the following:
credit limit
increases as
income
increases; customers whose income is in the middle range are more likely to purchase
more from
AllElectronics
; there is no clear correlation between
income
and
age
.
In pixel-oriented techniques, data records can also be ordered in a query-dependent
way. For example, given a point query, we can sort all records in descending order of
similarity to the point query.
Filling a window by laying out the data records in a linear way may not work well for
a wide window. The first pixel in a row is far away from the last pixel in the previous row,
though they are next to each other in the global order. Moreover, a pixel is next to the
one above it in the window, even though the two are not next to each other in the global
order. To solve this problem, we can lay out the data records in a space-filling curve
(a)
income
(b)
credit_limit
(c)
transaction_volume
(d)
age
Figure2.10
Pixel-oriented visualization of four attributes by sorting all customers in
income
ascending
order.
