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From "Nascif Abousalh-Neto (JIRA)" <j...@apache.org>
Subject [jira] Updated: (IVY-896) Add GDF as a report output format
Date Sat, 06 Sep 2008 03:38:44 GMT

     [ https://issues.apache.org/jira/browse/IVY-896?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Nascif Abousalh-Neto updated IVY-896:
-------------------------------------

    Description: 
We have been dealing with some very large graphs here (thousands of nodes, tens of thousands
of edges) and the information density is so high that the graph visualization tools supported
by Ivy, GraphViz and yED, were not up to the task.

Then we found out about GUESS (http://graphexploration.cond.org/), which takes a novel approach
which combines visualization support (using JUNG, an excellent library in itself) with a DSL
for graph manipulation based on Jython. This way a user can issue commands and run scripts
that manipulate the graph contents with immediate visual feedback. We have been using it and
has proven to be a great match for our use cases.

GUESS claims to suport graphml but it was not able to parse the graphml report generated by
Ivy. So I coded an XSL that creates GDF from the Ivy resolution report XML. Besides working
:-) this method keeps the relevant (but non-visual) attributes from the original report in
the GDF as well. These can be later used in GUESS to group nodes, create convex hulls, calculate
metrics, and so on.



  was:
We have been dealing with some very large graphs here (thousands of nodes, tens of thousands
of edges) and the information density is so high that the graph visualization tools supported
by Ivy, GraphViz and yED, were not up to the task.

Then we found out about GUESS (http://jung.sourceforge.net/applet/balloonlayout.html), which
takes a novel approach which combines visualization support (using JUNG, an excellent library
in itself) with a DSL for graph manipulation based on Jython. This way a user can issue commands
and run scripts that manipulate the graph contents with immediate visual feedback. We have
been using it and has proven to be a great match for our use cases.

GUESS claims to suport graphml but it was not able to parse the graphml report generated by
Ivy. So I coded an XSL that creates GDF from the Ivy resolution report XML. Besides working
:-) this method keeps the relevant (but non-visual) attributes from the original report in
the GDF as well. These can be later used in GUESS to group nodes, create convex hulls, calculate
metrics, and so on.



        Summary: Add GDF as a report output format  (was: Support for GDF as a report output
format)

> Add GDF as a report output format
> ---------------------------------
>
>                 Key: IVY-896
>                 URL: https://issues.apache.org/jira/browse/IVY-896
>             Project: Ivy
>          Issue Type: New Feature
>          Components: Core
>    Affects Versions: 2.0.0-beta-1
>            Reporter: Nascif Abousalh-Neto
>            Priority: Minor
>             Fix For: unspecified
>
>         Attachments: gdf.xsl
>
>
> We have been dealing with some very large graphs here (thousands of nodes, tens of thousands
of edges) and the information density is so high that the graph visualization tools supported
by Ivy, GraphViz and yED, were not up to the task.
> Then we found out about GUESS (http://graphexploration.cond.org/), which takes a novel
approach which combines visualization support (using JUNG, an excellent library in itself)
with a DSL for graph manipulation based on Jython. This way a user can issue commands and
run scripts that manipulate the graph contents with immediate visual feedback. We have been
using it and has proven to be a great match for our use cases.
> GUESS claims to suport graphml but it was not able to parse the graphml report generated
by Ivy. So I coded an XSL that creates GDF from the Ivy resolution report XML. Besides working
:-) this method keeps the relevant (but non-visual) attributes from the original report in
the GDF as well. These can be later used in GUESS to group nodes, create convex hulls, calculate
metrics, and so on.

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