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.

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. 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. 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. System Flow

5. Matching Workflows
• Getting part of a workflow
• Adopt a part of a workflow

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. 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. 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. Thank You !!