In scenarios where many different, independent and dynamic data sources need to be brought together, mediated data integration at runtime is rapidly gaining interest. In a global-as-view approach, schema mappings express how to get data from each data source according to the global schema of the mediator. Key issues include the effort required to include and map new data sources, and the very need of data sources for the global schema to be expressed. It has been argued that the principles of Linked Data can be used to spread the cost of adding new sources in a pay-as-you-go model. We contribute by describing a data integration framework able to mitigate these issues, by relating data sources under a global schema which is implicit and only partly known at the time a new data source joins. Mappings over a data source only require partial knowledge of it and of the part of the global schema that it will affect. Pay-as-you go can then be employed to guarantee eventual schema compliance. This approach was adopted in a large-scale data integration system for Smart Cities, where it allowed short time-to-publish for new data and iterative schema refinements.