QoS Browsing for Web Service Selection

  • Chen Ding
  • Preethy Sambamoorthy
  • Yue Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5900)


In most of current research works on Quality of Service (QoS) based web service selection, searching is usually the dominant way to find the desired services. However, sometimes, requestors may not have the knowledge of the available QoS attributes and their value ranges in the registry, or they may only have vague QoS requirements. Under this situation, we believe that browsing is a more appropriate way to help the QoS-based service selection process. In this paper, we propose an interactive QoS browsing mechanism to first show an overview of the QoS value distribution to requestors and then gradually present more and more detailed views on some requestor interested value ranges. We find that interval data (or more generally symbolic data) is a more proper type to represent the QoS value, compared with the single valued numerical data. So we use interval clustering algorithms to implement our browsing system. The experiment compares the performance of using different distance measures and shows the effectiveness of the interval clustering algorithm we use. We also use a sample data set to illustrate the interactive QoS browsing process.


Interval Data Service Selection Cluster Prototype Symbolic Data Analysis Dynamic Cluster Algorithm 
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.


  1. 1.
    Al-Masri, E., Mahmoud, Q.H.: QoS-based Discovery and Ranking of Web Services. In: 6th International Conference on Computer Communications and Networks, pp. 529–534 (2007)Google Scholar
  2. 2.
  3. 3.
    Bianchini, D., De Antonellis, V., Melchiori, M.: QoS in Ontology-based Service Classification and Discovery. In: 15th International Workshop on Database and Expert Systems Applications, pp. 145–150 (2004)Google Scholar
  4. 4.
    Chavent, M., De Carvalho, F.A.T., Lechevallier, Y., Verde, R.: New Clustering Methods for Interval Data. Computational Statistics 21(2), 211–229 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Choo, C.W., Detlor, B., Turnbull, D.: Information Seeking on the Web – an Integrated Model of Browsing and Searching. In: 62nd Annual Meeting of the American Society for Information Science, pp. 3–16 (1999)Google Scholar
  6. 6.
    Cutting, D., Karger, D.R., Pederson, J., Turkey, J.: Scatter/Gather: A Cluster-based Approach to Browsing Large Documents. In: 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 318–329 (1992)Google Scholar
  7. 7.
    De Souza, R.M.C.R., De Carvalho, F.A.T.: Clustering of Interval Data Based on City-Block Distances. Pattern Recognition Letters 25(3), 353–365 (2004)CrossRefGoogle Scholar
  8. 8.
    Diday, E., Noirhomme-Fraiture, M.: Symbolic Data Analysis and the SODAS Software. Wiley-Interscience, Hoboken (2008)zbMATHGoogle Scholar
  9. 9.
    Dong, X., Halevy, A., Madhavan, J., Nemes, E., Zhang, J.: Similarity Search for Web Services. In: 30th International Conference on Very Large Data Bases, pp. 372–383 (2004)Google Scholar
  10. 10.
    Gowda, K.C., Ravi, T.R.: Agglomerative Clustering of Symbolic Objects Using the Concepts of Both Similarity and Dissimilarity. Pattern Recognition Letters 16(6), 647–652 (1995)CrossRefGoogle Scholar
  11. 11.
    Ke, W., Sugimoto, C.R., Mostafa, J.: Dynamicity vs. Effectiveness: A User Study of a Clustering Algorithm for Scatter/Gather. In: 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 19–26 (2009)Google Scholar
  12. 12.
    Lamparter, S., Ankolekar, A., Studer, R., Grimm, S.: Preference-based Selection of Highly Configurable Web Services. In: 16th International Conference on World Wide Web, pp. 1013–1022 (2007)Google Scholar
  13. 13.
    Li, S.M., Ding, C., Chi, C.H., Deng, J.: Adaptive Quality Recommendation Mechanism for Software Service Provisioning. In: IEEE International Conference on Web Services, pp. 169–176 (2008)Google Scholar
  14. 14.
    Liu, Y.T., Ngu, A.H., Zeng, L.Z.: QoS Computation and Policing in Dynamic Web Service. In: 13th International Conference on World Wide Web, pp. 66–73 (2004)Google Scholar
  15. 15.
    Ran, S.: A Model for Web Services Discovery with QoS. ACM SIGecom Exchanges 4(1), 1–10 (2003)CrossRefGoogle Scholar
  16. 16.
  17. 17.
  18. 18.
    Stroulia, E., Wang, Y.: Structural and Semantic Matching for Assessing Web-Service Similarity. International Journal of Cooperative Information Systems, Special Issue: Service-Oriented Computing 14(4), 407–437 (2005)Google Scholar
  19. 19.
    Vu, L.H., Hauswirth, M., Aberer, K.: QoS-based Service Selection and Ranking with Trust and Reputation Management. In: International Conference on Cooperative Information Systems, pp. 446–483 (2005)Google Scholar
  20. 20.
    Wang, Y., Vassileva, J.: Toward Trust and Reputation Based Web Service Selection: A Survey. International Transactions on Systems Science and Applications 3(2), 118–132 (2007)Google Scholar
  21. 21.
    Xu, Z., Martin, P., Powley, W., Zulkernine, F.: Reputation-Enhanced QoS-based Web Services Discovery. In: IEEE International Conference on Web Services, pp. 249–256 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chen Ding
    • 1
  • Preethy Sambamoorthy
    • 1
  • Yue Tan
    • 2
  1. 1.Department of Computer ScienceRyerson UniversityCanada
  2. 2.School of SoftwareTsinghua UniversityChina

Personalised recommendations