By Topic

WS-SCAN: A effective approach for web services clustering

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Zhiliang Zhu ; Coll. of Software, Northeastern Univ., Shenyang, China ; Haitao Yuan ; Jie Song ; Jing Bi
more authors

With the rapid growth of available web services developed by different organizations, clustering of web services is required for conveniently managing services such as web services selection, discovery, composition and QoS prediction. However, the traditional clustering approaches have some drawbacks in similarity measuring and information preprocessing. In this paper, a similarity model is presented to measure the similarity between web services. Based on this model, a special preprocessing approach is proposed, which considers the programming style and naming rules. The proposed approach is combined with the SCAN algorithm and evaluated through the planned experiments. The experimental results show that the proposed model and approach can effectively improve clustering of web services and further improve the web service-based applications such as service discovery, composition and QoS prediction.

Published in:

Computer Application and System Modeling (ICCASM), 2010 International Conference on  (Volume:5 )

Date of Conference:

22-24 Oct. 2010