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Flexible Scientific Workflow Modeling Using Frames, Templates, and Dynamic Embedding

  • Anne H. H. Ngu
  • Shawn Bowers
  • Nicholas Haasch
  • Timothy McPhillips
  • Terence Critchlow
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5069)

Abstract

While most scientific workflows systems are based on dataflow, some amount of control-flow modeling is often necessary for engineering fault-tolerant, robust, and adaptive workflows. However, control-flow modeling within dataflow often results in workflow specifications that are hard to comprehend, reuse, and maintain. We describe new modeling constructs to address these issues that provide a structured approach for modeling control-flow within scientific workflows, and discuss their implementation within the Kepler scientific workflow system.

Keywords

Composite Actor Frame Actor Sink Actor Embed Component Runtime Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ludäscher, B., et al.: Scientific workflow management and the Kepler system. Concurrency and Computation: Practice & Experience (2006)Google Scholar
  2. 2.
    Oinn, T., et al.: Taverna: A tool for the composition and enactment of bioinformatics workflows. Bioinformatics 20 (2004)Google Scholar
  3. 3.
    Parker, S.G., Miller, M., Hansen, C.D., Johnson, C.R.: An integrated problem solving environment: The SCIRun computational steering system. In: HICSS (1998)Google Scholar
  4. 4.
    Lee, E.A., Parks, T.M.: Dataflow process networks. Proc. of the IEEE 83, 773–801 (1995)CrossRefGoogle Scholar
  5. 5.
    Bowers, S., Ludäscher, B.: Actor-oriented design of scientific workflows. In: ER (2005)Google Scholar
  6. 6.
    Bowers, S., Ludäscher, B., Ngu, A.H.H., Critchlow, T.: Enabling scientific workflow reuse through structured composition of dataflow and control flow. In: IEEE SciFlow (2006)Google Scholar
  7. 7.
    Eker, J., et al.: Taming heterogeneity—The Ptolemy approach. Proc. of the IEEE 91 (2003)Google Scholar
  8. 8.
    Goderis, A., Goble, C., Sattler, U., Lord, P.: Seven bottlenecks to workflow reuse and repurposing. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 323–337. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Xin, X.: Case study: Terascale supernova initiative workflow (TSI-Swesty). LLNL Technical Note (2004)Google Scholar
  10. 10.
    Alonso, G., Mohan, C.: Workflow management systems: The next generation of distributed processing tools. In: Advanced Transaction Models and Architectures (1997)Google Scholar
  11. 11.
    van der Aalst, W.M.P., ter Hofstede, A.H.M., Kiepuszewski, B., Barros, A.P.: Workflow patterns. Distributed and Parallel Databases 14 (2003)Google Scholar
  12. 12.
    Curbera, F., et al.: Business Process Execution Language for Web Services (BPEL4WS), Version 1.0 (2002)Google Scholar
  13. 13.
    Martin, D.L., et al.: Bringing semantics to web services: The OWL-S approach. In: Intl. Workshop on Semantic Web Services and Web Process Composition (2004)Google Scholar
  14. 14.
    Girault, A., Lee, B., Lee, E.A.: Hierarchical finite state machines with multiple concurrency models. IEEE Transactions on CAD 18 (1999)Google Scholar
  15. 15.
    Lee, E.A., Neuendorffer, S.: Actor-oriented models for codesign: Balancing re-use and performance. In: Formal Methods and Models for System Design. Kluwer, Dordrecht (2004)Google Scholar
  16. 16.
    Ngu, A.H.H., et al.: Advanced process-based component integration in Telcordia’s cable OSS. In: ICDE (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Anne H. H. Ngu
    • 1
  • Shawn Bowers
    • 2
  • Nicholas Haasch
    • 1
  • Timothy McPhillips
    • 2
  • Terence Critchlow
    • 3
  1. 1.Texas State UniversitySan Marcos 
  2. 2.Genome Center, University of CaliforniaDavis 
  3. 3.Pacific Northwest National Laboratory 

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