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- W63061330 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." @default.
- W63061330 created "2016-06-24" @default.
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- W63061330 date "2012-01-01" @default.
- W63061330 modified "2023-09-26" @default.
- W63061330 title "Sparse Functional Representation for Large-Scale Service Clustering" @default.
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- W63061330 doi "https://doi.org/10.1007/978-3-642-34321-6_31" @default.
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