Dex is an application for interacting with data. Dex provides data extraction, transformation, visualization, and predictive capabilities.
Major capabilities include:
|Data Extraction||Dex has the ability to read data from a variety of sources such as CSV, files, databases and even processes and expect driven scripts.|
|Transformation||Dex can transform this data into other forms via Groovy, Jython as was as a custom language specific to Dex called TMI (Too Much Information).|
|Data Persistence||Dex can save the transformed data to file or database.|
|Visualization||Dex provides 40+ visualization tools to help explore your data. In most cases the visualization can be saved into self-contained and interactive HTML5 content which can be viewed with or without a WebServer through a HTML5 compliant browser such as Firefox, Chrome, Safari or later versions of Internet Explorer. More visualizations are being added in each release.|
|R Integration||Dex can integrate directly with a running instance of R to tap into complex statistical analysis and predictive analytics.|
This list is by no means complete, but here is a short list folks who have helped Dex through inspiration and perspiration:
Click on any of the images for a live version.
NVD3 is an amazing set of charting capabilities which extends the capabilities of D3 to provide some very powerful visualization capabilities.
Time lapse, or motion charts were inspired by Hans Rosling's Gapminder project and are used to depict complex relationships over time.
Groovy templates allow data transformation into any format. The following examples show a variety of transformations to HTML tables with differing capabilities such as highlighting and search capabilities. These views are not limited to HTML. Once generated, they can be saved as independent HTML files outside of Dex.
All of the usual charting components are available. Many have multiple implementations with different capabilities.
Force diagrams and Force Trees are a fun way to show connections. They use physics to determine an optimal layout for a large number of nodes.
Radar charts and Parallel Lines are great ways to depict relationships across a vast amount of numerical data.
Dendograms, hive plots, indented trees and node link trees are effectives ways to depict hierarchy and relationships.