  Although message-based (business) application integration is based on
orchestrated message flows, current modeling languages exclusively cover (parts
of) the control flow, while under-specifying the data flow. Especially for more
data-intensive integration scenarios, this fact adds to the inherent data
processing weakness in conventional integration systems.
  We argue that with a more data-centric integration language and a relational
logic based implementation of integration semantics, optimizations from the
data management domain(e.g., data partitioning, parallelization) can be
combined with common integration processing (e.g., scatter/gather,
splitter/gather). With the Logic Integration Language (LiLa) we redefine
integration logic tailored for data-intensive processing and propose a novel
approach to data-centric integration modeling, from which we derive the
control-and data flow and apply them to a conventional integration system.
