I happened across a very cool project on web data integration at the University of Leipzig. Their paper Dynamic Fusion of Web Data is worth a look. They're working towards a theory of on-the-fly data integration for mashup applications that they refer to as dynamic data fusion. Data integration in mashups is dynamic in that it occurs as runtime. This provides for a pay-as-you-go model, rather than a large up-front semantic mapping task that limits the scalability of traditional data integration methods like data warehouses.
They describe mashups as workflow-like. Do they mean mashups are programmatic as opposed to declarative? In place of SQL, this group's iFuice system uses a scripting language with "set operations (e.g., union, intersection, and difference) and data transformation (e.g., fuse, aggregate) which can be used to post-process query results". Other key features are instance-level mapping and accommodation of structured and unstructured data.
This definitely gets at what Firegoose is good for - using the web as a channel for structured data - an approach that does for data integration what loose coupling does for software. Firegoose, part of the Gaggle framework, is a toolbar for Firefox that allows data to be exchanged between desktop software and the web. Firegoose can read microformats, call web services, query databases, or even perform nasty dirty screen scraping. Unlike a mashup, data integration in Firegoose and Gaggle requires user participation, although the user never deals with schemas, only instances of the Gaggle data types - mainly lists of identifiers, matrices of numeric data, networks, and tuples. The identifiers serve in a role somewhat analogous to primary keys.
More papers in a similar vein