Name
Abstract | ||
|---|---|---|
Service registries are overwhelmed by the ever-increasing number of Web services. The scale of Web services pose challenges to Web service discovery and composition. To address the above challenges, we propose a method called Clustered Service Rank (CSR) that combines spectral clustering and popularity analysis of networks of Web services in this paper. Specifically, CSR applies Fidler vector to cluster the network of Web services and uses Page Rank to identify the service importance in each cluster. Using user ratings as ground truth, we evaluated the performance of CSR in service ranking by comparing it against that of basic PageRank method. Our preliminary results show that CSR provides a better match to users' functional requirements and less the network traversal than basic PageRank does. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2132176.2132267 | iConference 2011 |
Keywords | Field | DocType |
clustered service rank,service registry,basic pagerank,page rank,service importance,web service discovery,network traversal,service rank,service ranking,web service,basic pagerank method,functional requirement,spectral clustering,k means,ground truth | Corporate social responsibility,Spectral clustering,PageRank,Functional requirement,World Wide Web,Tree traversal,Ranking,Computer science,Popularity,Web service | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Azari | 1 | 12 | 2.29 |
| Lina Zhou | 2 | 664 | 51.70 |
| Aryya Gangopadhyay | 3 | 391 | 112.49 |