CHRONOVIEW: VISUALIZATION TECHNIQUE FOR LARGE AMOUNTS OF TEMPORAL DATA - Information Visualisation: Techniques, Usability and Evaluation - page 69

large number of events at the same time, we can, for example, compare
events that share common features. In addition, we can find events with
different characteristics.
Related work
In this section we discuss related studies that visualize events associated
with time. There are different aspects into which the arrangements of time
adopted by visualization techniques can be classified: linear and cyclic [3].
The former often uses a straight line for the time axis, whereas the latter
often uses circular or spiral-type visualizations to represent periodicity.
Linear visualization
Line plots are the most common linear time axis method and there are
many variations and extensions of them for time series data. Aris et al. [4]
treated unevenly spaced time series data and developed several
visualization methods. Buono et al. [5] developed a visualization tool for
the interactive analysis of time series data. These tools help us to know
when events occurred and to find the co-occurrence of several events by
visualizing individual time-stamps. Beard et al. [6] proposed EventViewer
as a framework for visualization and exploration of events. The framework
supports user investigation and comparison of event patterns across space,
theme, and time. Vrotsou et al. [7] developed ActiviTree, a system that
facilitates the exploration of sequences in event-based data. Krstajiü et al.
[8] developed CloudLines to deal with time-based representations of large
and dynamic event datasets in a limited space. Their representation adapts
to data by employing a decay function that lets items fade away according
to their relevance.
Lee et al. [9] developed SparkClouds, a tag cloud with sparklines [10],
to represent the evolution of keywords. Nguyen et al. [11] developed
visualization techniques for temporally referenced tags that exploit their
text size and brightness. They used the background colour of tags and
colour-coded cells, as in a calendar.
Visualizing large amounts of temporal data is a challenge in
information visualization because it requires a large number of records to
be squeezed into a few pixels. One way to do this is by aggregate
visualization [12]. Lifeline2 by Wang et al. [13] and LifeFlow by
Wongsuphasawat et al. [14] render aggregate visualization of temporal
data. ChronoView also renders aggregate visualization, but in a different
way.