  Modeling and verification of dynamic systems operating over a relational
representation of states are increasingly investigated problems in AI, Business
Process Management, and Database Theory. To make these systems amenable to
verification, the amount of information stored in each relational state needs
to be bounded, or restrictions are imposed on the preconditions and effects of
actions. We introduce the general framework of relational action bases (RABs),
which generalizes existing models by lifting both these restrictions: unbounded
relational states can be evolved through actions that can quantify both
existentially and universally over the data, and that can exploit numerical
datatypes with arithmetic predicates. We then study parameterized safety of
RABs via (approximated) SMT-based backward search, singling out essential
meta-properties of the resulting procedure, and showing how it can be realized
by an off-the-shelf combination of existing verification modules of the
state-of-the-art MCMT model checker. We demonstrate the effectiveness of this
approach on a benchmark of data-aware business processes. Finally, we show how
universal invariants can be exploited to make this procedure fully correct.
