From computation models to models of provenance: The RWS approach

Scientific workflows often benefit from or even require advanced modeling constructs, e.g. nesting of subworkflows, cycles for executing loops, data-dependent routing, and pipelined execution. In such settings, an often overlooked aspect of provenance takes center stage: a suitable model of provenance (MoP) for scientific workflows should be based upon the underlying model of computation (MoC) used for executing the workflows. We can derive an adequate MoP from a MoC (such as Kahn's process networks) by taking into account the assumptions that a MoC entails, and by recording the observables which it affords. In this way, a MoP captures or at least better approximates 'real' data dependencies for workflows with advanced modeling constructs. As a specific instance, we elaborate on the Read-Write-ReSet model, a simple and flexible MoP suitable for a number of different MoCs.