  Scientific workflows are becoming increasingly popular for compute-intensive
and data-intensive scientific applications. The vision and promise of
scientific workflows includes rapid, easy workflow design, reuse, scalable
execution, and other advantages, e.g., to facilitate "reproducible science"
through provenance (e.g., data lineage) support. However, as described in the
paper, important research challenges remain. While the database community has
studied (business) workflow technologies extensively in the past, most current
work in scientific workflows seems to be done outside of the database
community, e.g., by practitioners and researchers in the computational sciences
and eScience. We provide a brief introduction to scientific workflows and
provenance, and identify areas and problems that suggest new opportunities for
database research.
