The most common approach to support analysis of graphs with associated
time series data include: overlay of data on graph vertices for one
timepoint at a time by manipulating a visual property (e.g. color)
of the vertex, along with sliders or some such mechanism to animate
the graph for other timepoints. Alternatively, data from all the
timepoints can be overlaid simultaneously by embedding small charts
into graph vertices. These graph visualizations may also be linked
to other visualizations (e.g., parallel co-ordinates) using brushing
and linking. This paper describes a study performed to evaluate and
rank graph+timeseries visualization options based on users' performance
time and accuracy of responses on predefined tasks. The results suggest
that overlaying data on graph vertices one timepoint at a time may
lead to more accurate performance for tasks involving analysis of
a graph at a single timepoint, and comparisons between graph vertices
for two distinct timepoints. Overlaying data simultaneously for all
the timepoints on graph vertices may lead to more accurate and faster
performance for tasks involving searching for outlier vertices displaying
different behavior than the rest of the graph vertices for all timepoints.
Single views have advantage over multiple views on tasks that require
topological information. Also, the number of attributes displayed
on nodes has a non trivial influence on accuracy of responses, whereas
the number of visualizations affect the performance time.
%0 Conference Paper
%1 Saraiya2005
%A Saraiya, Purvi
%A Lee, P.
%A North, C.
%B Information Visualization, 2005. INFOVIS 2005. IEEE Symposium on
%D 2005
%K analysis; animation; computational computer data geometry; graph information; manipulation; overlay; property series series; theory; time topological vertex; visual visualisation; visualization;
%P 225 - 232
%R 10.1109/INFVIS.2005.1532151
%T Visualization of graphs with associated timeseries data
%X The most common approach to support analysis of graphs with associated
time series data include: overlay of data on graph vertices for one
timepoint at a time by manipulating a visual property (e.g. color)
of the vertex, along with sliders or some such mechanism to animate
the graph for other timepoints. Alternatively, data from all the
timepoints can be overlaid simultaneously by embedding small charts
into graph vertices. These graph visualizations may also be linked
to other visualizations (e.g., parallel co-ordinates) using brushing
and linking. This paper describes a study performed to evaluate and
rank graph+timeseries visualization options based on users' performance
time and accuracy of responses on predefined tasks. The results suggest
that overlaying data on graph vertices one timepoint at a time may
lead to more accurate performance for tasks involving analysis of
a graph at a single timepoint, and comparisons between graph vertices
for two distinct timepoints. Overlaying data simultaneously for all
the timepoints on graph vertices may lead to more accurate and faster
performance for tasks involving searching for outlier vertices displaying
different behavior than the rest of the graph vertices for all timepoints.
Single views have advantage over multiple views on tasks that require
topological information. Also, the number of attributes displayed
on nodes has a non trivial influence on accuracy of responses, whereas
the number of visualizations affect the performance time.
@inproceedings{Saraiya2005,
abstract = { The most common approach to support analysis of graphs with associated
time series data include: overlay of data on graph vertices for one
timepoint at a time by manipulating a visual property (e.g. color)
of the vertex, along with sliders or some such mechanism to animate
the graph for other timepoints. Alternatively, data from all the
timepoints can be overlaid simultaneously by embedding small charts
into graph vertices. These graph visualizations may also be linked
to other visualizations (e.g., parallel co-ordinates) using brushing
and linking. This paper describes a study performed to evaluate and
rank graph+timeseries visualization options based on users' performance
time and accuracy of responses on predefined tasks. The results suggest
that overlaying data on graph vertices one timepoint at a time may
lead to more accurate performance for tasks involving analysis of
a graph at a single timepoint, and comparisons between graph vertices
for two distinct timepoints. Overlaying data simultaneously for all
the timepoints on graph vertices may lead to more accurate and faster
performance for tasks involving searching for outlier vertices displaying
different behavior than the rest of the graph vertices for all timepoints.
Single views have advantage over multiple views on tasks that require
topological information. Also, the number of attributes displayed
on nodes has a non trivial influence on accuracy of responses, whereas
the number of visualizations affect the performance time.},
added-at = {2010-06-23T08:39:21.000+0200},
author = {Saraiya, Purvi and Lee, P. and North, C.},
biburl = {https://www.bibsonomy.org/bibtex/2dd75e97c8b6778f8a059dc553ac96ec4/joelotz},
booktitle = {Information Visualization, 2005. INFOVIS 2005. IEEE Symposium on},
doi = {10.1109/INFVIS.2005.1532151},
interhash = {a23d2776255dbcad7e0933796411e779},
intrahash = {dd75e97c8b6778f8a059dc553ac96ec4},
keywords = {analysis; animation; computational computer data geometry; graph information; manipulation; overlay; property series series; theory; time topological vertex; visual visualisation; visualization;},
month = {23-25},
owner = {joe},
pages = { 225 - 232},
timestamp = {2010-06-23T08:39:22.000+0200},
title = {Visualization of graphs with associated timeseries data},
year = 2005
}