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Actor-Oriented Design of Scientific Workflows

  • Shawn Bowers
  • Bertram Ludäscher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3716)

Abstract

Scientific workflows are becoming increasingly important as a unifying mechanism for interlinking scientific data management, analysis, simulation, and visualization tasks. Scientific workflow systems are problem-solving environments, supporting scientists in the creation and execution of scientific workflows. While current systems permit the creation of executable workflows, conceptual modeling and design of scientific workflows has largely been neglected. Unlike business workflows, scientific workflows are typically highly data-centric naturally leading to dataflow-oriented modeling approaches. We first develop a formal model for scientific workflows based on an actor-oriented modeling and design approach, originally developed for studying models of complex concurrent systems. Actor-oriented modeling separates two modeling concerns: component communication (dataflow) and overall workflow coordination (orchestration). We then extend our framework by introducing a novel hybrid type system, separating further the concerns of conventional data modeling (structural data type) and conceptual modeling (semantic type). In our approach, semantic and structural mismatches can be handled independently or simultaneously, and via different types of adapters, giving rise to new methods of scientific workflow design.

Keywords

Output Port Input Port Semantic Type Abstract Actor Composite Actor 
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.
    Ailamaki, A., Ioannidis, Y.E., Livny, M.: Scientific Workflow Management by Database Management. In: Proc. of SSDBM, pp. 190–199 (1998)Google Scholar
  2. 2.
    Alonso, G., Mohan, C.: Workflow Management Systems: The Next Generation of Distributed Processing Tools. In: Advanced Transaction Models and Architectures (1997)Google Scholar
  3. 3.
    Batini, C., Ceri, S., Navathe, S.: Conceptual Database Design: An Entity-Relationship Approach. Benjamin/Cummings (1992)Google Scholar
  4. 4.
    Bowers, S., Ludäscher, B.: An Ontology-Driven Framework for Data Transformation in ScientificWorkflows. In: Rahm, E. (ed.) DILS 2004. LNCS (LNBI), vol. 2994, pp. 1–16. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Casati, F., Ceri, S., Pernici, B., Pozzi, G.: Conceptual Modelling ofWorkFlows. In: Papazoglou, M.P. (ed.) ER 1995 and OOER 1995. LNCS, vol. 1021, pp. 341–354. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  6. 6.
    Castagna, G.: Covariance and contravariance: conflict without a cause. ACM Transactions on Programming Languages and Systems (TOPLAS) 17(3) (1995)Google Scholar
  7. 7.
    Wright, K.W.B.H., Brown, M.J.: The Dataflow Visualization Pipeline as a Problem Solving Environment. In: Virtual Environments and Scientific Visualization. Springer, Heidelberg (1996)Google Scholar
  8. 8.
    Kiepuszewski, B.: Expressiveness and Suitability of Languages for Control Flow Modelling in Workflows. Ph.D. Thesis, Queensland University of Technology (2002)Google Scholar
  9. 9.
    Lee, E.A., Neuendorffer, S.: Actor-oriented Models for Codesign: Balancing Re-Use and Performance. In: Formal Methods and Models for Systems. Kluwer, Dordrecht (2004)Google Scholar
  10. 10.
    Lee, E.A., Parks, T.M.: Dataflow process networks. Proc. of the IEEE 83(5), 773–801 (1995)CrossRefGoogle Scholar
  11. 11.
    Ludäscher, B., Altintas, I., Chad Berkley, D.H., Jaeger-Frank, E., Jones, M., Lee, E., Tao, J., Zhao, Y.: Scientific Workflow Management and the Kepler System. In: Concurrency and Computation: Practice and Experience, Special Issue on Scientific Workflows (2005) (to appear)Google Scholar
  12. 12.
    Majithia, S., Shields, M.S., Taylor, I.J., Wang, I.: Triana: A Graphical Web Service Composition and Execution Toolkit. In: Proc. of the IEEE Intl. Conf. on Web Services (ICWS). IEEE Computer Society, Los Alamitos (2004)Google Scholar
  13. 13.
    Meidanis, J., Vossen, G., Weske, M.: Using Workflow Management in DNA Sequencing. In: Proc. of CoopIS, pp. 114–123 (1996)Google Scholar
  14. 14.
    Morrison, J.P.: Flow-Based Programming: A New Approach to Application Development. Van Nostrand Reinhold, New York (1994)zbMATHGoogle Scholar
  15. 15.
    Oinn, T.M., Addis, M., Ferris, J., Marvin, D., Senger, M., Greenwood, R.M., Carver, T., Glover, K., Pocock, M.R., Wipat, A., Li, P.: Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics 20(17), 3045–3054 (2004)CrossRefGoogle Scholar
  16. 16.
    van der Aalst, W., van Hee, K.: Workflow Management: Models, Methods, and Systems (Cooperative Information Systems). MIT Press, Cambridge (2002)Google Scholar
  17. 17.
    van der Aalst, W.M.P., ter Hofstede, A.H.M., Kiepuszewski, B., Barros, A.P.: Workflow Patterns. Distributed and Parallel Databases 14(1), 5–51 (2003)CrossRefGoogle Scholar
  18. 18.
    zur Muehlen, M.: Workflow-based Process Controlling: Foundation, Design, and Application of workflow-driven Process Information Systems. Logos Verlag, Berlin (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Shawn Bowers
    • 1
  • Bertram Ludäscher
    • 2
  1. 1.UC Davis Genome Center 
  2. 2.Department of Computer ScienceUniversity of CaliforniaDavis

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