  Data-driven workflows, of which IBM's Business Artifacts are a prime
exponent, have been successfully deployed in practice, adopted in industrial
standards, and have spawned a rich body of research in academia, focused
primarily on static analysis. The present research bridges the gap between the
theory and practice of artifact verification with VERIFAS, the first
implementation of practical significance of an artifact verifier with full
support for unbounded data. VERIFAS verifies within seconds linear-time
temporal properties over real-world and synthetic workflows of complexity in
the range recommended by software engineering practice. Compared to our
previous implementation based on the widely-used Spin model checker, VERIFAS
not only supports a model with richer data manipulations but also outperforms
it by over an order of magnitude. VERIFAS' good performance is due to a novel
symbolic representation approach and a family of specialized optimizations.
