Honorable Mention Poster Award for 'ThermalPlot: Visualizing Multi-Attribute Time-Series Data Using a Thermal Metaphor'
Sprache des Titels:
Multi-attribute time-series data plays a vital role in many different
domains. An important task when making sense of such data is
to provide users with an overview to identify items that show an
interesting development over time. However, this is not well sup-
ported by existing visualization techniques. To address this issue,
we present ThermalPlot, a visualization technique that summarizes
complex combinations of multiple attributes over time using an
item?s position, the most salient visual variable. More precisely, the
x-position in the ThermalPlot is based on a user-defined degree-of-
interest (DoI) function that combines multiple attributes over time.
The y-position is determined by the relative change in the DoI value
(delta DoI) within a user-specified time window. Animating this map-
ping via a moving time window gives rise to circular movements of
items over time?as in thermal systems. To help the user to iden-
tify important items that match user-defined temporal patterns and
to increase the technique?s scalability, we adapt the items? level of
detail based on the DoI value. We demonstrate the effectiveness of
our technique in a stock market usage scenario.