CBR Based Workflow Composition Assistant

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Abstract—Composing a scientific workflow from scratch may be time-consuming, even if the scientist is fully aware of the semantics, the inputs, and the outputs of the expected workflow.
Reusing existing services and parts from already composed workflows can aid in reducing the total workflow composition time. However, matching the semantics and the inputs and outputs of these reusable components manually is not an easy task, especially when there are hundreds of such components available. Even components are annotated with information on the semantics of their inputs and outputs, the complex nature of the semantic languages may make manual component selection even harder. In this paper, we propose a Case-Based Reasoning (CBR) approach to assist composition of workflows based on the characteristics of the inputs and the outputs of the reusable workflow components, facilitating user exploitation of existing services and workflows during workflow composition. The architecture can also be extended to utilize the semantics of the various components improving the precision of the identified reusable components.

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CBR Based Workflow Composition Assistant

  1. 1. CBR Based Workflow Composition Assistant Eran Chinthaka, Jaliya Ekanayake, David Leake, Beth Plale School of Informatics, Indiana University Bloomington, Indiana, USA. {echintha, jekanaya, leake, plale}@cs.indiana.edu
  2. 2. Problem  Composing workflows from scratch is hard  Common case :  User has inputs and expected outputs  Need a way to get to output from Inputs  What can help  Use of existing workflows  From user’s space  From known places like myexperiments.org  Enabling input and output descriptions using multiple methods  semantic annotations  keywords
  3. 3. Suggested Solution  Assumption  “All workflows that are acyclic can be represented as a graph”  User describes the inputs and outputs of a workflow/part of a workflow using keywords or semantic annotations  Use NLP techniques to match inputs and outputs  Simplest solution uses Keyword matching with Lesk Algorithm  Use CBR based approach for retrieving similar workflows  Adapt existing workflow to new ones  By down sizing existing ones  Extending existing ones
  4. 4. System Flow
  5. 5. Matching Workflows • Getting part of a workflow • Adopt a part of a workflow
  6. 6. Key Features • Better performance compared to heavy knowledge based approach or manual composition • Independent of the workflow description languages (WSDL, SCUFL, etc.,) • Ability to extend similarity measures to use semantic annotations or other NLP techniques • Ability to provide threshold for similarity check and to control number of cases retrieved • NLP based keyword checking
  7. 7. Future Work  Reducing common workflow languages in to graph representation  Integration of standard semantic language like OWL, RDF or WSDL-S  Integration with a workflow composition tool (eg: Xbaya, WF)  Performance Evaluation with an existing knowledge heavy system  Evaluation of accuracy and/or usability of suggested cases
  8. 8. Related Work • D. Leake and J. Kendall-Morwick, “Towards case-based support for e-science workflow generation by mining provenance information,” in Proceedings of the Nineth European Conference on Case-Based Reasoning. Springer, 2008, in press. • J. Kim, Y. Gil, and M. Spraragen, “A knowledge-based approach to interactive workflow composition,” 2004. • J. L. Ambite and D. Kapoor, “Automatically composing data workflows with relational descriptions and shim services,” International Semantic Web Conference, 2007. • M. Carman, L. Serafini, and P. Traverso, “Web service composition as planning,” in ICAPS03 International Conference on Automated Planning and Scheduling, 2003. • E. Sirin, J. Hendler, and B. Parsia, “Semi-automatic composition of web services using semantic descriptions,” in ICEIS-2003 Workshop on Web Services.
  9. 9. Thank You !!

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