A workflow PROV-corpus based on taverna and wings

We describe a corpus of provenance traces that we have collected by executing 120 real world scientific workflows. The workflows are from two different workflow systems: Taverna [5] and Wings [3], and 12 different application domains (see Figure 1). Table 1 provides a summary of this PROV-corpus.

Detecting common scientific workflow fragments using templates and execution provenance

Provenance plays a major role when understanding and reusing the methods applied in a scientic experiment, as it provides a record of inputs, the processes carried out and the use and generation of intermediate and nal results. In the specic case of in-silico scientic experiments, a large variety of scientic workflow systems (e.g., Wings, Taverna, Galaxy, Vistrails) have been created to support scientists. All of these systems produce some sort of provenance about the executions of the workflows that encode scientic experiments. However, provenance is normally recorded at a very low level of detail, which complicates the understanding of what happened during execution. In this paper we propose an approach to automatically obtain abstractions from low-level provenance data by finding common workflow fragments on workflow execution provenance and relating them to templates. We have tested our approach with a dataset of workflows published by the Wings workflow system. Our results show that by using these kinds of abstractions we can highlight the most common abstract methods used in the executions of a repository, relating different runs and workflow templates with each other.

Common motifs in scientific workflows: An empirical analysis

While workflow technology has gained momentum in the last decade as a means for specifying and enacting computational experiments in modern science, reusing and repurposing existing workflows to build new scientific experiments is still a daunting task. This is partly due to the difficulty that scientists experience when attempting to understand existing workflows, which contain several data preparation and adaptation steps in addition to the scientifically significant analysis steps. One way to tackle the understandability problem is through providing abstractions that give a high-level view of activities undertaken within workflows. As a first step towards abstractions, we report in this paper on the results of a manual analysis performed over a set of real-world scientific workflows from Taverna and Wings systems. Our analysis has resulted in a set of scientific workflow motifs that outline i) the kinds of data intensive activities that are observed in workflows (data oriented motifs), and ii) the different manners in which activities are implemented within workflows (workflow oriented motifs). These motifs can be useful to inform workflow designers on the good and bad practices for workflow development, to inform the design of automated tools for the generation of workflow abstractions, etc.

Common motifs in scientific workflows: An empirical analysis

Workflow technology continues to play an important role as a means for specifying and enacting computational experiments in modern science. Reusing and re-purposing workflows allow scientists to do new experiments faster, since the workflows capture useful expertise from others. As workflow libraries grow, scientists face the challenge of finding workflows appropriate for their task, understanding what each workflow does, and reusing relevant portions of a given workflow.We believe that workflows would be easier to understand and reuse if high-level views (abstractions) of their activities were available in workflow libraries. As a first step towards obtaining these abstractions, we report in this paper on the results of a manual analysis performed over a set of real-world scientific workflows from Taverna, Wings, Galaxy and Vistrails. Our analysis has resulted in a set of scientific workflow motifs that outline (i) the kinds of data-intensive activities that are observed in workflows (Data-Operation motifs), and (ii) the different manners in which activities are implemented within workflows (Workflow-Oriented motifs). These motifs are helpful to identify the functionality of the steps in a given workflow, to develop best practices for workflow design, and to develop approaches for automated generation of workflow abstractions.