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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Xia, B., Fan, Y., Tan, W., Huang, K., Zhang, J., Wu, C.: Category-aware API clustering and distributed recommendation for automatic Mashup creation. IEEE Trans. Serv. Comput. doi:10.1109/TSC.2014.2379251 (preprinted)
Cao, B., Liu, J., Tang, M., Zheng, Z., Wang, G.: Mashup service recommendation based on usage history and service network. Int. J. Web Serv. Res. 10(4), 82–101 (2013)
Liu, W., Liu, W.: Web service clustering using text mining techniques. Int. J. Agent-Oriented Softw. Eng. 3(1), 6–26 (2009)
Liu, W., Liu, W.: Discovering homogeneous service communities through web service clustering. Serv.-Oriented Comput. Agents Semant. Eng. 5006, 69–82 (2008)
Sun, P., Jiang, C.: Using service clustering to facilitate process-oriented semantic web service discovery. J. Comput. 31(8), 1340–1353 (2008). (In Chinese)
Platzer, C., Rosenberg, F., Dustdar, S.: Web service clustering using multidimensional angles as proximity measures. ACM Trans. Internet Technol. 9(3), 1–26 (2009)
Chen, L., Hu, L., Wu, J., Zheng, Z., Ying, J., Li, Y., Deng, S.: Wtcluster: utilizing tags for web service clustering. In: Proceedings of International Conference on Service-oriented Computing, pp. 204–218, Paphos, Cyprus (2011)
Wu, J., Chen, L., Zheng, Z., Lyu, R., Wu, Z.: Clustering Web services to facilitate service discovery. Knowl. Inf. Syst. 38(1), 207–229 (2014)
Wu, J., Chen, L., Xie, Y., Zheng, Z.: Titan: a system for effective web service discovery. In: Proceedings of the 21st International Conference on World Wide Web, pp. 441–444. ACM, New York, USA (2012)
Chen, L., Wang, Y., Yu, Q., Zheng, Z., Wu, J.: WT-LDA: user tagging augmented LDA for web service clustering. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 162–176. Springer, Heidelberg (2013)
Li, Z., Wang, J., Zhang, N., Li, Z., He, C., He, K.: A topic-oriented clustering approach for domain services. J. Comput. Res. Dev. 51(2), 408–419 (2014). (In Chinese)
Yang, H., Chen, J., Meng, X., Qiu, B.: Dynamically traveling web service clustering based on spatial and temporal aspects. In: Hainaut, J.-L., Rundensteiner, E.A., Kirchberg, M., Bertolotto, M., Brochhausen, M., Chen, Y.-P.P., Cherfi, S.S.-S., Doerr, M., Han, H., Hartmann, S., Parsons, J., Poels, G., Rolland, C., Trujillo, J., Yu, E., Zimányie, E. (eds.) ER Workshops 2007. LNCS, vol. 4802, pp. 348–357. Springer, Heidelberg (2007)
Zhou, Z., Sellami, M., Gaaloul, W., Barhamgi, M., Defude, B.: Data providing services clustering and management for facilitating service discovery and replacement. IEEE Trans. Autom. Sci. Eng. 10(4), 1131–1146 (2013)
Zhang, L., Cheng, S., Chang, C., Zhou, Q.: A pattern-recognition-based algorithm and case study for clustering and selecting business services. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 42(1), 102–114 (2012)
Cassar, G., Barnaghi, P., Moessner, K.: Probabilistic methods for service clustering. In: Proceedings of the 4th International Workshop on SMR2 Conjunction with the International Semantic Web Conference, pp. 4–20, Shanghai, China (2010)
Mustapha, A., Mohamed, Q., Zahi, J.: Leveraging formal concept analysis with topic correlation for service clustering and discovery. In: 2014 IEEE International Conference on Web Services, pp. 153–160, Alaska, USA, 27 June–2 July 2014
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Cao, B., Liu, X., Liu, J., Tang, M. (2015). Effective Mashup Service Clustering Method by Exploiting LDA Topic Model from Multiple Data Sources. In: Yao, L., Xie, X., Zhang, Q., Yang, L., Zomaya, A., Jin, H. (eds) Advances in Services Computing. APSCC 2015. Lecture Notes in Computer Science(), vol 9464. Springer, Cham. https://doi.org/10.1007/978-3-319-26979-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-26979-5_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26978-8
Online ISBN: 978-3-319-26979-5
eBook Packages: Computer ScienceComputer Science (R0)