Abstract
Service clustering provides an effective means to discover hidden service communities that group services with relevant functionalities. However, the ever increasing number of Web services poses key challenges for building large-scale service communities. In this paper, we address the scalability issue in service clustering, aiming to discover service communities over very large-scale services. A key observation is that service descriptions are usually represented by long but very sparse term vectors as each service is only described by a limited number of terms. This inspires us to seek a new service representation that is economical to store, efficient to process, and intuitive to interpret. This new representation enables service clustering to scale to massive number of services. More specifically, a set of anchor services are identified that allow to represent each service as a linear combination of a small number of anchor services. In this way, the large number of services are encoded with a much more compact anchor service space. We conduct extensive experiments on real-world service data to assess both the effectiveness and efficiency of the proposed approach. Results on a dataset with over 3,700 Web services clearly demonstrate the good scalability of sparse functional representation.
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Bose, A., Nayak, R., Bruza, P.: Improving Web Service Discovery by Using Semantic Models. In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 366–380. Springer, Heidelberg (2008)
Cai, D., He, X., Han, J.: Document clustering using locality preserving indexing. IEEE Trans. Knowl. Data Eng. 17(12), 1624–1637 (2005)
Chen, L., Hu, L., Zheng, Z., Wu, J., Yin, J., Li, Y., Deng, S.: WTCluster: Utilizing Tags for Web Services Clustering. In: Kappel, G., Maamar, Z., Motahari-Nezhad, H.R. (eds.) ICSOC 2011. LNCS, vol. 7084, pp. 204–218. Springer, Heidelberg (2011)
Ding, C.H.Q., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix t-factorizations for clustering. In: KDD, pp. 126–135 (2006)
Dong, X., Halevy, A., Madhavan, J., Nemes, E., Zhang, J.: Similarity search for web services. In: VLDB 2004: Proceedings of the Thirtieth International Conference on Very Large Data Bases, pp. 372–383. VLDB Endowment (2004)
Elgazzar, K., Hassan, A.E., Martin, P.: Clustering wsdl documents to bootstrap the discovery of web services. In: ICWS, pp. 147–154 (2010)
Klusch, M., Fries, B., Sycara, K.: Automated semantic web service discovery with owls-mx. In: AAMAS, pp. 915–922. ACM, New York (2006)
Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: NIPS, pp. 801–808 (2006)
Liu, F., Shi, Y., Yu, J., Wang, T., Wu, J.: Measuring similarity of web services based on WSDL. In: ICWS, pp. 155–162 (2010)
Liu, X., Huang, G., Mei, H.: Discovering homogeneous web service community in the user-centric web environment. IEEE T. Services Computing 2(2), 167–181 (2009)
Lovasz, L.: Matching Theory (North-Holland mathematics studies). Elsevier Science Ltd. (1986)
Ma, J., Zhang, Y., He, J.: Efficiently finding web services using a clustering semantic approach. In: CSSSIA 2008: Proceedings of the 2008 International Workshop on Context Enabled Source and Service Selection, Integration and Adaptation, pp. 1–8. ACM, New York (2008)
Segev, A., Sheng, Q.Z.: Bootstrapping ontologies for web services. IEEE Transactions on Services Computing 5, 33–44 (2012)
Tong, H., Papadimitriou, S., Sun, J., Yu, P.S., Faloutsos, C.: Colibri: fast mining of large static and dynamic graphs. In: KDD, pp. 686–694 (2008)
Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, SIGIR 2003, pp. 267–273. ACM, New York (2003)
Yu, Q.: Place Semantics into Context: Service Community Discovery from the WSDL Corpus. In: Kappel, G., Maamar, Z., Motahari-Nezhad, H.R. (eds.) ICSOC 2011. LNCS, vol. 7084, pp. 188–203. Springer, Heidelberg (2011)
Yu, Q., Rege, M.: On service community learning: A co-clustering approach. In: ICWS, pp. 283–290 (2010)
Zhang, Y., Zheng, Z., Lyu, M.R.: Wsexpress: A qos-aware search engine for web services. In: ICWS, pp. 91–98 (2010)
Zheng, M., Bu, J., Chen, C., Wang, C., Zhang, L., Qiu, G., Cai, D.: Graph regularized sparse coding for image representation. IEEE Transactions on Image Processing 20(5), 1327–1336 (2011)
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Yu, Q. (2012). Sparse Functional Representation for Large-Scale Service Clustering. In: Liu, C., Ludwig, H., Toumani, F., Yu, Q. (eds) Service-Oriented Computing. ICSOC 2012. Lecture Notes in Computer Science, vol 7636. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34321-6_31
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DOI: https://doi.org/10.1007/978-3-642-34321-6_31
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