Abstract
Scientific workflows exist in many different domains and for many different computing
platforms. As these systems have proliferated, they have also become increasingly complex
and harder to maintain. Furthermore, these systems often exist as self-sufficient islands of
capability that can be over-specialized and locked into a specific domain. Some commonality
exists and three major workflow types are readily apparent in (i) modeling and simulation,
(ii) high-throughput data analysis, and (iii) optimization. A far more detailed understanding
of different workflow types is required to determine how large, interdisciplinary workflows
that span the types and multiple computing facilities can be created and executed. This work
presents a new model of scientific workflows that attempts to create such an understanding
with a formal, machine-readable ontology that can be used to answer design questions
about interoperability for workflows that need to be executed across distributed workflow
management systems. Example instances are presented for simple workflows that do
not require decision making, more complicated workflows that can split decision making
between external agents and internal state transitions in finite state machines, and purely
conceptual workflows that represent notional if not exactly executable workflows purely for
communicating ideas. Finally, a perspective on interoperability for workflow systems is
presented in the context of the ontology.