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Sparse Functional Representation for Large-Scale Service Clustering

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7636)

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.

Keywords

Service Discovery Sparse Code Service Description Cluster Quality Service Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Qi Yu
    • 1
  1. 1.College of Computing and Information ScienceRochester Institute of TechnologyUSA

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