Despite an increasing interest in scientific workflow technologies in recent years, workflow design remains a challenging, slow, and often error-prone process, thus limiting the speed of further adoption of scientific workflows. Based on practical experience with data-driven workflows, we identify and illustrate a number of recurring scientific workflow design challenges, i.e., parameter-rich functions; data assembly, disassembly, and cohesion; conditional execution; iteration; and, more generally, workflow evolution. In conventional approaches, such challenges usually lead to the introduction of different types of "shims", i.e., intermediary workflow steps that act as adapters between otherwise incorrectly wired components. However, relying heavily on the use of shims leads to brittle (i.e., change-intolerant) workflow designs that are hard to comprehend and maintain. To this end, we present a general workflow design paradigm called virtual data assembly lines (VDAL). In this paper, we show how the VDAL approach can overcome common scientific workflow design challenges and improve workflow designs by exploiting (i) a semistructured, nested data model like XML, (ii) a flexible, statically analyzable configuration mechanism (e.g., an XQuery fragment), and (iii) an underlying virtual assembly line model that is resilient to workflow and data changes. The approach has been implemented as Kepler/COMAD, and applied to improve the design of complex, real-world workflows.
Stored only when both papers are in the corpus — 24 citations exist upstream.
Imported from OpenAlex (CC0) — nothing here is generated.