Features

Latest revision as of 22:26, 12 July 2012

This page summarizes perhaps the most fundamental features of Wandora application.

[edit] Desktop application

Fast and reliable
Requires Java 1.6

[edit] Graphical user interface

Topic map browser
Graph visualization of a topic map
Treemap visualization of topics
Build your own interactive visualizations using embedded Processing language.

[edit] Layered storage of knowledge

Construct the knowledge using several different layers, each containing only part of the knowledge
You can view only parts of data and hide parts that don't interest you
Automatic merging of different and even distributed data sources
Protect some parts of topic map by allowing only read access
Use nested layer stacks to create tree like layer structures

[edit] Several data storage options

Memory based topic map for very fast processing of relatively small topic maps
Database storage makes it possible to use topic maps of virtually unlimited size

[edit] Import data in several formats

[edit] Extract topic maps from various sources

Over 50 different data format and web service extractors. Including

And many more...

[edit] Search and analyze topic maps

Search topics with regular expressions, find similar topics, and use Wandora's query language to perform more complex searches.
Compare topic maps.
Clustering coefficient for any topic set including average clustering coefficient of a topic map.
Topic map diameter and average path length.
Topic map connection count and distribution (Topic map degree distribution).
SOM classifier to analyze topics with associations.
R language integration makes Wandora a data source for R environment.

[edit] Export and publish

Wandora can export topic maps in XTM, LTM and JTM format.
Export topic map as a collection of static HTML pages.
Export topic maps as Graph Modeling Language, GraphML, and GraphXML graphs.
Embedded HTTP server allows you to publish topic maps as a dynamic HTML site, RSS feed or pretty much anything if you just write suitable templates for your output.
- Included outputs: HTML, RSS instance feed, ATOM topic, Wandora Web Service.
- We have created several Wandora Drupal extras to show you examples how Wandora can be used together with Drupal content management system.
- We have also created an extra for Joomla content management system to demonstrate how to publish Wandora content in Joomla sites.
- Build your own topic map application using Wandora's Web Service.
- You can also create new output modules to Wandora easily. See chapter Setting up new services at Embedded HTTP server.
Wandora Piccolo combination allows you to set up a large scale dynamic WWW site based on the topic maps.

[edit] Edit topic maps

Wandora contains a huge collection of topic map editing features. You can not only edit topics and associations directly but also transform topic's internal structures -- such as occurrences to other structures -- such as variant names. You can also choose to apply editing feature to not just one topic but a collection of topics which speeds up topic map refactoring substantially. See Wandora's documentation for details.

[edit] Generate simple topic map graphs

[edit] Plugin architecture makes it easy to add custom functionality

Tools that process topic map, for example cleaning or reconstructing data
Importing data in different formats, for example from existing relational database
Exporting data in different formats
Reading and automatic processing of metadata from a collection of files
Customized ways to represent the data in the topic map
Custom publishing methods

[edit] Upcoming features

Wandora Team releases new features frequently. Features under development have been reviewed in wiki page Upcoming features.

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@@ Line 11: / Line 11: @@
 * [[Graph topic panel|Graph visualization of a topic map]]
 * [[Treemap topic panel|Treemap visualization]] of topics
-* Build your own visualizations using [[Processing topic panel|embedded Processing language]].
+* Build your own interactive visualizations using [[Processing topic panel|embedded Processing language]].
 == [[Introduction to Layered Topic Maps|Layered storage of knowledge]] ==