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Scientific workflow systems are increasingly used to automate complex data analyses, largely due to their benefits over traditional approaches for workflow design, optimization, and provenance recording. Many workflow systems employ a simple dependency model to represent the provenance of data produced by workflow runs. Although commonly adopted, this model does not capture explicit data dependencies introduced by "provenance-aware" processes, and it can lead to inefficient storage when workflow data is complex or structured. We present a provenance model, extending the conventional approach, that supports (i) explicit data dependencies and (ii) nested data collections. Our model adopts techniques from reference-based XML versioning, adding annotations for process and data dependencies. We present strategies and reduction techniques to store immediate and transitive provenance information within our model, and examine trade-offs among update time, storage size, and query response time. We evaluate our approach on real-world and synthetic workflow execution traces, demonstrating significant reductions in storage size, while also reducing the time required to store and query provenance information.

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Abstract. The complexity of scientific workflows for analyzing biological data creates a number of challenges for current workflow and provenance systems. This complexity is due in part to the nature of scientific data (e.g., heterogeneous, nested data collections) and the programming constructs required for automation (e.g., nested workflows, looping, pipeline parallelism). We present an extended version of the Kepler scientific workflow system to address these challenges, tailored for the systematics community. Our system combines novel approaches for representing scientific data, modeling and automating complex analyses, and recording and browsing associated provenance information.