Declarative Rules for Inferring Fine-Grained Data Provenance from Scientific Workflow Execution Traces

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• 作者：Shawn Bowers (18)
  Timothy McPhillips (19)
  Bertram Lud?scher (20)
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7525
• 期：1
• 页码：97-110
• 全文大小：262KB
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  3. Anand, M.K., Bowers, S., McPhillips, T.M., Lud?scher, B.: Efficient provenance storage over nested data collections. In: EDBT (2009)
  4. Bowers, S., McPhillips, T., Riddle, S., Anand, M.K., Lud?scher, B.: Kepler/pPOD: Scientific Workflow and Provenance Support for Assembling the Tree of Life. In: Freire, J., Koop, D., Moreau, L. (eds.) IPAW 2008. LNCS, vol.?5272, pp. 70-7. Springer, Heidelberg (2008) CrossRef
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  18. The W3C Provenance Working Group, http://www.w3.org/2011/prov
  
• 作者单位：Shawn Bowers (18)
  Timothy McPhillips (19)
  Bertram Lud?scher (20)
  
  18. Dept. of Computer Science, Gonzaga University, USA
  19. Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Stanford University, USA
  20. Dept. of Computer Science, University of California Davis, USA
  

文摘

Fine-grained dependencies within scientific workflow provenance specify lineage relationships between a workflow result and the input data, intermediate data, and computation steps used in the result’s derivation. This information is often needed to determine the quality and validity of scientific data, and as such, plays a key role in both provenance standardization efforts and provenance query frameworks. While most scientific workflow systems can record basic information concerning the execution of a workflow, they typically fall into one of three categories with respect to recording dependencies: (1) they rely on workflow computation steps to declare dependency relationships at runtime; (2) they impose implicit assumptions concerning dependency patterns from which dependencies are automatically inferred; or (3) they do not assert any dependency information at all. We present an alternative approach that decouples dependency inference from workflow systems and underlying execution traces. In particular, we present a high-level declarative language for expressing explicit dependency rules that can be applied (at any time) to workflow trace events to generate fine-grained dependency information. This approach not only makes provenance dependency rules explicit, but allows rules to be specified and refined by different users as needed. We present our dependency rule language and implementation that rewrites dependency rules into relational queries over underlying workflow traces. We also demonstrate the language using common types of dependency patterns found within scientific workflows.