When wrestling with a gnarly problem, it's interesting to compare notes with others who've faced the same dilemma. Having worked on an interoperability framework, a system called Gaggle, I had a feeling of familiarity when I came across this well-thought-out paper:
The Scalable Adapter Design Pattern: Enabling Interoperability between Educational Software Tools, Harrer, Pinkwart, McLaren, Scheuer, IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, 2008
The paper describes a design pattern for getting heterogeneous software tools to interoperate with each other by exchanging data. Each application is augmented with an adapter that can interact with a shared representation. This hub-and-spokes model makes sense because it reduces the effort from writing n(n-1) adapters to connect all pairs of applications to writing one adapter per application.
Scalability refers to the composite design pattern, implementing (what I would call) a more general concept, that of hierarchically structured data. If you've ever worked with large XML documents, calling them scalable might seem like an overstatement, but I see their point. XML nicely represents small objects, like events, as well as moderately sized data documents. The same can be said of JSON.
Applications remain decoupled from the data structure, with an adapter mediating between the two. The adapter also provides events when the shared data structure is updated. A nice effect of the hierarchical data structure is that client applications can register their interest in receiving events at different levels of the tree structure.
The Scalable Adapter pattern combines of well established patterns - Adapter, Composite and Publish-subscribe yielding a flexible way for arbitrary application data to be exchanged at different levels of granularity.
The main difference between Scalable Adapter and Gaggle is that Gaggle focused on message passing rather than centrally stored data. The paper says, "it is critical that both the syntax and semantics of the data represented in the composite data structure...", but they don't really address what belongs in the "Basic Element" - the data to be shared. Gaggle solves this problem by explicitly defining a handful of universal data structures. Applications are free to implement their own data model, achieving interoperability by mapping (also in an adapter) their internal data structures onto the shared Gaggle data types.
The Scalable Adapter paper breaks the problem down systematically in terms of design patterns, while Gaggle was motivated by the software engineering strategies of separation of concerns and parsimony, plus the linguistic concept of semantic flexibility. It's remarkable that the two systems worked out quite similarly, given the different domains they were built for.