Enhancing and abstracting scientific workflow provenance for data publishing
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Detecting common scientific workflow fragments using templates and execution provenance
Proceedings of the seventh international conference on Knowledge capture
On assisting scientific data curation in collection-based dataflows using labels
WORKS '13 Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science
Understanding workflows for distributed computing: nitty-gritty details
WORKS '13 Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science
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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.