SEDiL is a software platform created in the context of the ANR Marmota Project and the PASCAL pump-priming project: "Learning Stochastic Edit Distances from Structured Data". This platform aims at sharing many learning algorithms of probabilistic edit distances between structured data (sequences and trees).
The initiative of the creation of this platform is mainly due to Marc Sebban. Most of the development has been originally done by Yann Esposito and is now improved during the phd thesis of Laurent Boyer.
Edit distances are used in many domains such as bio-informatic, image, sound and music recognition, WEB mining ...
The Edit Distance represents the minimal number of necessary modifications (edit operations) to transform an input structured data into an output one.
In real world applications, the edit distances parameters are manually tuned. In domains where the level of knowledge is insufficient, it seems useful to automatically learn those parameters.
Because XML is becoming the new standard for information storage. Morever, trees provide more information than single sequences.