WSBen
- 1 January 2009
- journal article
- Published by IGI Global in International Journal of Web Services Research
- Vol. 6 (1), 1-19
- https://doi.org/10.4018/jwsr.2009092301
Abstract
In this article, a novel benchmark toolkit, WSBen, for testing web services discovery and composition algorithms is presented. The WSBen includes: (1) a collection of synthetically generated web services files in WSDL format with diverse data and model characteristics; (2) queries for testing discovery and composition algorithms; (3) auxiliary files to do statistical analysis on the WSDL test sets; (4) converted WSDL test sets that conventional AI planners can read; and (5) a graphical interface to control all these behaviors. Users can fine-tune the generated WSDL test files by varying underlying network models. To illustrate the application of the WSBen, in addition, we present case studies from three domains: (1) web service composition; (2) AI planning; and (3) the laws of networks in Physics community. It is our hope that WSBen will provide useful insights in evaluating the performance of web services discovery and composition algorithms. The WSBen toolkit is available at: http://pike.psu.edu/sw/wsben/.Keywords
This publication has 12 references indexed in Scilit:
- Random graphs with arbitrary degree distributions and their applicationsPublished by Walter de Gruyter GmbH ,2011
- WSBen: A Web Services Discovery and Composition BenchmarkPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- A snapshot of public web servicesACM SIGMOD Record, 2005
- Network lawsCommunications of the ACM, 2004
- Emergence of social conventions in complex networksArtificial Intelligence, 2002
- Statistical mechanics of complex networksReviews of Modern Physics, 2002
- Topology of Evolving Networks: Local Events and UniversalityPhysical Review Letters, 2000
- Diameter of the World-Wide WebNature, 1999
- Fast planning through planning graph analysisArtificial Intelligence, 1997
- The computational complexity of propositional STRIPS planningArtificial Intelligence, 1994