Abstract
Increasingly, application developers seek the ability to search for existing Web services within large Internet-based repositories. The goal is to retrieve services that match the user's requirements. With the growing number of services in the repositories and the challenges of quickly finding the right ones, the need for clustering related services becomes evident to enhance search engine results with a list of similar services for each hit. In this article, a statistical clustering approach is presented that enhances an existing distributed vector space search engine for Web services with the possibility of dynamically calculating clusters of similar services for each hit in the list found by the search engine. The focus is laid on a very efficient and scalable clustering implementation that can handle very large service repositories. The evaluation with a large service repository demonstrates the feasibility and performance of the approach.
- Abramowicz, W., Haniewicz, K., Kaczmarek, M., and Zyskowski, D. 2007. Architecture for Web services filtering and clustering. In Proceedings of the International Conference on Internet and Web Applications and Services (ICIW). 18. Google Scholar
Digital Library
- Andritsos, P. and Tzerpos, V. 2003. Software clustering based on information loss minimization. In Proceedings of the 10th Working Conference on Reverse Engineering (WCRE'03). IEEE Computer Society, Washington, DC. Google Scholar
Digital Library
- Benatallah, B., Hacid, M.-S., Leger, A., Rey, C., and Toumani, F. 2005. On automating Web services discovery. Int. J. VLDB 14, 1 (3), 84--96. Google Scholar
Digital Library
- Caverlee, J., Liu, L., and Rocco, D. 2004. Discovering and ranking Web services with BASIL: A personalized approach with biased focus. In Proceedings of the 2nd International Conference on Service-Oriented Computing (ICSOC'04). ACM Press, 153--162. Google Scholar
Digital Library
- Christensen, E., Curbera, F., Meredith, G., and Weerawarana, S. 2001. Web Services Description Language (WSDL) 1.1. W3C. http://www.w3.org/TR/wsdl.Google Scholar
- Dong, X., Halevy, A. Y., Madhavan, J., Nemes, E., and Zhang, J. 2004. Simlarity search for Web services. In Proceedings of the 30th International Conference on Very Large Databases (VLDB'04). 372--383. Google Scholar
Digital Library
- Eckey, H.-F., Kosfeld, R., and Rengers, M. 2002. Multivariate Statistics. Gabler.Google Scholar
- Friedman, R. 2002. Caching Web services in mobile ad hoc networks: Opportunities and challenges. In Proceedings of the 2nd ACM International Workshop on Principles of Mobile Computing (POMC'02). ACM Press, 90--96. Google Scholar
Digital Library
- Hess, A., Johnston, E., and Kushmerick, N. 2004. ASSAM: A tool for semi-automatically annotating semantic Web services. In Proceedings of the 3rd International Semantic Web Conference (ISWC'04). 320--334.Google Scholar
- IBM. 2005. IBM business registry. https://uddi.ibm.com/ubr/registry.html.Google Scholar
- la Torre, F. D. and Kanade, T. 2006. Discriminative cluster analysis. In Proceedings of the 23rd International Conference on Machine Learning (ICML'06). ACM Press, 241--248. Google Scholar
Digital Library
- MacQueen, J. B. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. vol. 1. University of California Press, Berkeley, 281--297.Google Scholar
- Magdalenic, I., Vrdoljakand, B., and Skocir, Z. 2006. Towards dynamic Web service generation on demand. In Proceedings of the International Conference on Software in Telecommunications and Computer Networks, (SoftCOM'06).Google Scholar
- Microsoft. 2005. Microsoft public uddi registry. http://uddi.microsoft.com/inquire.Google Scholar
- OASIS 2005. Universal Description, Discovery and Integration (vol.) 3.0 (UDDI) Specification. OASIS. http://www.oasis-open.org/committees/uddi-spec.Google Scholar
- Papazoglou, M. P. 2003. Service-oriented computing: Concepts, characteristics and directions. In Proceedings of the 4th International Conference on Web Information Systems Engineering. 3--12. Google Scholar
Digital Library
- Papazoglou, M. P., Traverso, P., Dustdar, S., and Leymann, F. 2006. Service-Oriented Computing Research Roadmap. http://infolab.uvt.nl/pub/papazogloump-2006-96.pdfGoogle Scholar
- Platzer, C. 2007. V.U.S.E. - The Vector Space Web Service Search Engine. http://vuse.de.vu/.Google Scholar
- Platzer, C. and Dustdar, S. 2005. A vector space search engine for Web services. In Proceedings of the 3rd European IEEE Conference on Web Services (ECOWS'05). Google Scholar
Digital Library
- Ran, S. 2003. A model for Web services discovery with QoS. SIGecom Exch. 4, 1, 1--10. Google Scholar
Digital Library
- Rosenberg, F., Platzer, C., and Dustdar, S. 2006. Boot-strapping performance and dependability attributes of Web services. In Proceedings of the IEEE Conference on Web Services (ICWS'06), 205--212. Google Scholar
Digital Library
- Sivashanmugam, K., Verma, K., and Sheth, A. 2004. Discovery of Web services in a federated registry environment. In Proceedings of the IEEE International Conference on Web Services (ICWS), 270--278. Google Scholar
Digital Library
- W3C. 2000. Resource Description Framework (RDF). http://www.w3.org/RDF.Google Scholar
- Yu, T., Zhang, Y., and Lin, K.-J. 2007. Efficient algorithms for Web services selection with end-to-end qos constraints. ACM Trans. Web 1, 1, 6. Google Scholar
Digital Library
Index Terms
Web service clustering using multidimensional angles as proximity measures
Recommendations
A Web service search engine for large-scale Web service discovery based on the probabilistic topic modeling and clustering
With the ever increasing number of Web services, discovering an appropriate Web service requested by users has become a vital yet challenging task. We need a scalable and efficient search engine to deal with the large volume of Web services. The aim of ...
Identifying Client Goals for Web Service Discovery
SCC '09: Proceedings of the 2009 IEEE International Conference on Services ComputingWeb service discovery has become a daunting task primarily due to its inability for allowing clients to articulate proper service search queries. Improving the quality of service search results could not be achieved unless we determine ways for ...
Flexible matching and ranking of web service advertisements
MW4SOC '07: Proceedings of the 2nd workshop on Middleware for service oriented computing: held at the ACM/IFIP/USENIX International Middleware ConferenceWith the growing number of service advertisements in service marketplaces, there is a need for matchmakers which select and rank functionally similar services based on non-functional properties, such as QoS and reputation parameters. Current matchmakers ...






Comments