International audienceData rather than functionality are the sources of competitive advantage for Web2.0 applications such as wikis, blogs and social networking websites. This valuable information might need to be capitalized by third-party applications or be subject to migration or data analysis. Model-Driven Engineering (MDE) can be used for these purposes. However, MDE first requires obtaining models from the wiki/blog/website database (a.k.a. model harvesting). This can be achieved through SQL scripts embedded in a program. However, this approach leads to laborious code that exposes the iterations and table joins that serve to build the model. By contrast, a Domain-Specific Language (DSL) can hide these "how" concerns, leaving the designer to focus on the "what", i.e. the mapping of database schemas to model classes. This paper introduces Schemol, a DSL tailored for extracting models out of databases which considers Web2.0 specifics. Web2.0 applications are often built on top of general frameworks (a.k.a. engines) that set the database schema (e.g.,MediaWiki, Blojsom). Hence, table names offer little help in automating the extraction process. In addition, Web2.0 data tend to be annotated. User-provided data (e.g., wiki articles, blog entries) might contain semantic markups which provide helpful hints for model extraction. Unfortunately, these data end up being stored as opaque strings. Therefore, there exists a considerable conceptual gap between the source database and the target metamodel. Schemol offers extractive functions and view-like mechanisms to confront these issues. Examples using Blojsom as the blog engine are available for download

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Wikis' organic growth inevitably leads to wiki degradation and the need for regular wiki refactoring. So far, wiki refactoring is a manual, time-consuming and error-prone activity. We strive to ease wiki refactoring by using mind maps as a graphical representation of the wiki structure, and mind map manipulations as a way to express refactoring. This paper (i) defines the semantics of common refactoring operations based on Wikipedia best practices, (ii) advocates for the use of mind maps as a visualization of wikis for refactoring, and (iii) introduces a DSL for wiki refactoring built on top of FreeMind, a mind mapping tool. Thus, wikis are depicted as FreeMind maps, and map manipulations are interpreted as refactoring operations over the wiki. The rationales for the use of a DSL are based not only on reliability grounds but also on facilitating end-user participation.

Form-intensive Web applications are common among institutions that collect bulks of data in a piecemeal fashion. European funding programs or income tax return illustrate these scenarios. Very often, most of this data is already digitalized in terms of documents, spreadsheets or databases. The task of manually filling Web forms out of these resources is not only cumbersome but also prone to typos. It does not benefit from the fact that the data is already in electronic format. Alternatively, externally-fed autofilling scripts can be programmed (e.g. using iMacros and Visual Basic) to code once, and enact many times. Unfortunately, this approach is programming intensive and fragile upon upgrades in either the website or the structure of the external source. This moves these tools away from users with scarce programming skills. We strive to empower these users by abstracting the way feeding solutions are realised. Since external sources tend to be structured, they offer the chance to be abstracted in terms of models. Autofilling scripts can then be generated as weavings between the external data model and the website model. We describe WebFeeder, a plugin for iMacros that introduces autofilling-script models as first-class artifacts in iMacros. The synthesis, enactment and maintenance of these script models are handled without leaving iMacros, minimizing users' cognitive load and involvement.