Treffer: Clustering Mashups by Integrating Structural and Semantic Similarities Using Fuzzy AHP

Title:
Clustering Mashups by Integrating Structural and Semantic Similarities Using Fuzzy AHP
Authors:
Weifeng Pan, Xinxin Xu, Hua Ming, Carl K. Chang
Contributors:
Department of Computer Science
Source:
International Journal of Web Services Research. 18:34-57
Publisher Information:
IGI Global, 2021.
Publication Year:
2021
Subject Terms:
DegreeDisciplines::Physical Sciences and Mathematics::Computer Sciences::Theory and Algorithms, Mashup Clustering, Service Computing, Genetic Algorithm, 0202 electrical engineering, electronic engineering, information engineering, DegreeDisciplines::Physical Sciences and Mathematics::Computer Sciences, Fuzzy AHP, 02 engineering and technology, Similarity
Document Type:
Fachzeitschrift Article
File Description:
application/pdf
Language:
English
ISSN:
1546-5004
1545-7362
DOI:
10.4018/ijwsr.2021010103
Access URL:
https://www.igi-global.com/article/clustering-mashups-by-integrating-structural-and-semantic-similarities-using-fuzzy-ahp/271727
Accession Number:
edsair.doi.dedup.....ea20983f1c2dba80ff76c68ee582b91a
Database:
OpenAIRE

Weitere Informationen

Mashup technology has become a promising way to develop and deliver applications on the web. Automatically organizing Mashups into functionally similar clusters helps improve the performance of Mashup discovery. Although there are many approaches aiming to cluster Mashups, they solely focus on utilizing semantic similarities to guide the Mashup clustering process and are unable to utilize both the structural and semantic information in Mashup profiles. In this paper, a novel approach to cluster Mashups into groups is proposed, which integrates structural similarity and semantic similarity using fuzzy AHP (fuzzy analytic hierarchy process). The structural similarity is computed from usage histories between Mashups and Web APIs using SimRank algorithm. The semantic similarity is computed from the descriptions and tags of Mashups using LDA (latent dirichlet allocation). A clustering algorithm based on the genetic algorithm is employed to cluster Mashups. Comprehensive experiments are performed on a real data set collected from ProgrammableWeb. The results show the effectiveness of the approach when compared with two kinds of conventional approaches.