Matrix Zoom: A Visual Interface to Semi-External Graphs.
J. Abello, and F. van Ham. INFOVIS, page 183-190. IEEE Computer Society, (2004)
Abstract
In web data, telecommunications traffic and in epidemiological
studies, dense subgraphs correspond to subsets of subjects (i.e.
users, patients) that share a collection of attributes values (i.e. accessed
web pages, email-calling patterns or disease diagnostic profiles).
Visual and computational identification of these ”clusters”
becomes useful when domain experts desire to determine those factors
of major influence in the formation of access and communication
clusters or in the detection and contention of disease spread.
With the current increases in graphic hardware capabilities and
RAM sizes, it is more useful to relate graph sizes to the available
screen real estate S and the amount of available RAM M, instead of
the number of edges or nodes in the graph. We offer a visual interface
that is parameterized by M and S and is particularly suited for
navigation tasks that require the identification of subgraphs whose
edge density is above certain threshold. This is achieved by providing
a zoomable matrix view of the underlying data. This view is
strongly coupled to a hierarchical view of the essential information
elements present in the data domain. We illustrate the applicability
of this work to the visual navigation of cancer incidence data and to
an aggregated sample of phone call traffic.
%0 Conference Paper
%1 conf/infovis/AbelloH04
%A Abello, James
%A van Ham, Frank
%B INFOVIS
%D 2004
%E Ward, Matthew O.
%E Munzner, Tamara
%I IEEE Computer Society
%K algorithm architectures clustering externalmemory graph hierarchy matrix software tree visualization zoom
%P 183-190
%T Matrix Zoom: A Visual Interface to Semi-External Graphs.
%U http://dblp.uni-trier.de/db/conf/infovis/infovis2004.html#AbelloH04
%X In web data, telecommunications traffic and in epidemiological
studies, dense subgraphs correspond to subsets of subjects (i.e.
users, patients) that share a collection of attributes values (i.e. accessed
web pages, email-calling patterns or disease diagnostic profiles).
Visual and computational identification of these ”clusters”
becomes useful when domain experts desire to determine those factors
of major influence in the formation of access and communication
clusters or in the detection and contention of disease spread.
With the current increases in graphic hardware capabilities and
RAM sizes, it is more useful to relate graph sizes to the available
screen real estate S and the amount of available RAM M, instead of
the number of edges or nodes in the graph. We offer a visual interface
that is parameterized by M and S and is particularly suited for
navigation tasks that require the identification of subgraphs whose
edge density is above certain threshold. This is achieved by providing
a zoomable matrix view of the underlying data. This view is
strongly coupled to a hierarchical view of the essential information
elements present in the data domain. We illustrate the applicability
of this work to the visual navigation of cancer incidence data and to
an aggregated sample of phone call traffic.
%@ 0-7803-8779-1
@inproceedings{conf/infovis/AbelloH04,
abstract = {In web data, telecommunications traffic and in epidemiological
studies, dense subgraphs correspond to subsets of subjects (i.e.
users, patients) that share a collection of attributes values (i.e. accessed
web pages, email-calling patterns or disease diagnostic profiles).
Visual and computational identification of these ”clusters”
becomes useful when domain experts desire to determine those factors
of major influence in the formation of access and communication
clusters or in the detection and contention of disease spread.
With the current increases in graphic hardware capabilities and
RAM sizes, it is more useful to relate graph sizes to the available
screen real estate S and the amount of available RAM M, instead of
the number of edges or nodes in the graph. We offer a visual interface
that is parameterized by M and S and is particularly suited for
navigation tasks that require the identification of subgraphs whose
edge density is above certain threshold. This is achieved by providing
a zoomable matrix view of the underlying data. This view is
strongly coupled to a hierarchical view of the essential information
elements present in the data domain. We illustrate the applicability
of this work to the visual navigation of cancer incidence data and to
an aggregated sample of phone call traffic.},
added-at = {2012-05-31T19:30:08.000+0200},
author = {Abello, James and van Ham, Frank},
biburl = {https://www.bibsonomy.org/bibtex/2503e0ff1238d60384ae82a0c610ac6d6/rwoz},
booktitle = {INFOVIS},
crossref = {conf/infovis/2004},
editor = {Ward, Matthew O. and Munzner, Tamara},
ee = {http://doi.ieeecomputersociety.org/10.1109/INFVIS.2004.46},
interhash = {b75715b7069c32ba6075c20184e1db55},
intrahash = {503e0ff1238d60384ae82a0c610ac6d6},
isbn = {0-7803-8779-1},
keywords = {algorithm architectures clustering externalmemory graph hierarchy matrix software tree visualization zoom},
pages = {183-190},
publisher = {IEEE Computer Society},
timestamp = {2012-05-31T19:32:31.000+0200},
title = {Matrix Zoom: A Visual Interface to Semi-External Graphs.},
url = {http://dblp.uni-trier.de/db/conf/infovis/infovis2004.html#AbelloH04},
year = 2004
}