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Managing Variability in Workflow with Feature Model Composition Operators

  • Mathieu Acher
  • Philippe Collet
  • Philippe Lahire
  • Robert France
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6144)

Abstract

In grid-based scientific applications, building a workflow essentially involves composing parameterized services describing families of services and then configuring the resulting workflow product line. In domains (e.g., medical imaging) in which many different kinds of highly parameterized services exist, there is a strong need to manage variabilities so that scientists can more easily configure and compose services with consistency guarantees. In this paper, we propose an approach in which variable points in services are described with several separate feature models, so that families of workflow can be defined as compositions of feature models. A compositional technique then allows reasoning about the compatibility between connected services to ensure consistency of an entire workflow, while supporting automatic propagation of variability choices when configuring services.

Keywords

Medical Image Composition Operator Service Composition Connected Service Medical Imaging Service 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mathieu Acher
    • 1
  • Philippe Collet
    • 1
  • Philippe Lahire
    • 1
  • Robert France
    • 2
  1. 1.I3S Laboratory (CNRS UMR 6070)University of Nice Sophia Antipolis, FranceSophia Antipolis CedexFrance
  2. 2.Computer Science DepartmentColorado State UniversityUSA

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