  Concurrent model synchronization is the task of restoring consistency between
two correlated models after they have been changed concurrently and
independently. To determine whether such concurrent model changes conflict with
each other and to resolve these conflicts taking domain- or user-specific
preferences into account is highly challenging. In this paper, we present a
framework for concurrent model synchronization algorithms based on Triple Graph
Grammars (TGGs). TGGs specify the consistency of correlated models using
grammar rules; these rules can be used to derive different consistency
restoration operations. Using TGGs, we infer a causal dependency relation for
model elements that enables us to detect conflicts non-invasively. Different
kinds of conflicts are detected first and resolved by the subsequent conflict
resolution process. Users configure the overall synchronization process by
orchestrating the application of consistency restoration fragments according to
several conflict resolution strategies to achieve individual synchronization
goals. As proof of concept, we have implemented this framework in the model
transformation tool eMoflon. Our initial evaluation shows that the runtime of
our presented approach scales with the size of model changes and conflicts,
rather than model size.
