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Effective Mashup Service Clustering Method by Exploiting LDA Topic Model from Multiple Data Sources

  • Buqing CaoEmail author
  • Xiaoqing (Frank) Liu
  • Jianxun Liu
  • Mingdong Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9464)

Abstract

Mashup is emerging as a promising software development method for allowing software developers to compose existing Web APIs to create new or value-added composite Web services. However, the rapid growth in the number of available Mashup services makes it difficult for software developers to select a suitable Mashup service to satisfy their requirements. Even though clustering based Mashup discovery technique shows a promise of improving the quality of Mashup service discovery, Mashup service clustering with high accuracy for discovery of Mashup services is still a challenge problem. In this paper, we propose a novel Mashup service clustering method for Mashup service discovery with high accuracy by exploiting LDA topic model built from multiple data sources. It enables to infer topic probability distribution of Mashup services, which serves as a basis of computation of similarity of Mashup services. K-means and Agnes algorithm are used to perform Mashup service clustering in terms of their similarities. Compared with other service clustering approaches, experimental results show that our approach achieves significant improvement in terms of precision, recall and F-measure rate, which will improve Mashup service discovery.

Keywords

Mashup service LDA topic model Multiple data source Service clustering 

Notes

Acknowledgements

The work was supported by National Natural Science Foundation of China under grant No. 61402168, 61402167 and 61272063, State Key Laboratory of Software Engineering (SKLSE) of China (Wuhan University) under grant No. SKLSE2014-10-10, and Scientific Research Fund of Hunan Provincial Education Department under grant 11C0689 and 11C0535.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Buqing Cao
    • 1
    • 2
    • 3
    Email author
  • Xiaoqing (Frank) Liu
    • 2
  • Jianxun Liu
    • 1
  • Mingdong Tang
    • 1
  1. 1.School of Computer Science and EngineeringHunan University of Science and TechnologyXiangtanChina
  2. 2.Computer Science and Computer Engineering DepartmentUniversity of Arkansas in FayettevilleFayettevilleUSA
  3. 3.State Key Laboratory of Software EngineeringWuhan UniversityWuhanChina

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