Analyzing Web Services Networks: Theory and Practice

  • Peep Küngas
  • Marlon Dumas
  • Shahab Mokarizadeh
  • Mihhail Matskin
Chapter

Abstract

This paper addresses the problem of applying the general network theory for analyzing qualitatively Web services networks. The paper reviews current approaches to analyzing Web services networks, generalizes the published approaches into a formal framework for analyzing Web services networks and demonstrates its applicability in practice. More specifically, two case studies are described where the presented framework has been applied. The first one considers identification of redundant data in large-scale service-oriented information systems, while the second one measures information diffusion between individual information systems.

Keywords

Shipping Income 

References

  1. 1.
    M. Cha, A. Mislove, and K. P. Gummadi. A measurement-driven analysis of information propagation in the flickr social network. In Proc. of the 18th International Conference on World Wide Web, WWW ’09, pages 721–730, Madrid, Spain, 2009. ACM.Google Scholar
  2. 2.
    A. K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios. Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering, 19(1):1–16, 2007.Google Scholar
  3. 3.
    J. Gekas and M. Fasli. Employing graph network analysis for web service composition. International Journal of Information Technology and Web Engineering, 2:21–40, 2007.Google Scholar
  4. 4.
    A. Heß and N. Kushmerick. Learning to attach semantic metadata to web services. In Proc. of 2nd International Semantic Web Conference (ISWC2003), pages 258–273. Springer, 2003.Google Scholar
  5. 5.
    G. Jiang, G. Cybenko, and J. A. Hendler. Semantic interoperability and information fluidity. Int. J. Cooperative Inf. Syst., 15(1):1–22, 2006.Google Scholar
  6. 6.
    A. Kalja, A. Reitsakas, and N. Saard. eGovernment in Estonia: Best practices. In Technology Management: A Unifying Discipline for Melting the Boundaries, pages 500–506. IEEE Press, 2005.Google Scholar
  7. 7.
    H. Kil, S.-C. Oh, E. Elmacioglu, W. Nam, and D. Lee. Graph theoretic topological analysis of web service networks. World Wide Web, 12:321–343, 2009.Google Scholar
  8. 8.
    P. Küngas and M. Dumas. Cost-effective semantic annotation of XML schemas and web service interfaces. In Proc. of IEEE 2009 International Conference on Services Computing, pages 372–379. IEEE Computer Society Press, 2009.Google Scholar
  9. 9.
    P. Küngas and M. Matskin. Interaction and potential synergy between commercial and governmental web services - a case study. In Procedings of 2007 IEEE International Conference on Services Computing - Workshops (SCW 2007), 9–13 July 2007, Salt Lake City, Utah, USA, pages 1–8. IEEE Computer Society, 2007.Google Scholar
  10. 10.
    F. Liu, Y. Shi, X. Luo, G. Liang, and Z. Xu. Discovery of semantic web service flow based on computation. In Proceedings of the 2009 IEEE International Conference on Web Services, ICWS ’09, pages 319–326, Washington, DC, USA, 2009. IEEE Computer Society.Google Scholar
  11. 11.
    S. Mokarizadeh, P. Küngas, and M. Matskin. Ontology learning for cost-effective large-scale semantic annotation of web service interfaces. In Proceedings of EKAW 2010, pages 401–410. Springer, 2010.Google Scholar
  12. 12.
    S. Mokarizadeh, P. Küngas, and M. Matskin. Evaluation of a semi-automated semantic annotation approach for bootstrapping the analysis of large-scale web service networks. In Proceedings of the 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011, Lyon, France, August 22–27, 2011, pages 388–395. IEEE Computer Society, 2011.Google Scholar
  13. 13.
    D. L. Moody and G. G. Shanks. Improving the quality of data models: empirical validation of a quality management framework. Information Systems, 28(6):619–650, 2003.Google Scholar
  14. 14.
    S.-C. Oh, D. Lee, and S. Kumara. Effective web service composition in diverse and large-scale service networks. IEEE Transactions on Services Computing, 1(1):15–32, 2008.Google Scholar
  15. 15.
    D. J. Rosenkrantz, S. Goel, S. S. Ravi, and J. Gangolly. Resilience metrics for service-oriented networks: A service allocation approach. IEEE Trans. Serv. Comput., 2:183–196, July 2009.Google Scholar
  16. 16.
    W. She, I.-L. Yen, B. Thuraisingham, and E. Bertino. Policy-driven service composition with information flow control. In The 8th International Conference on Web Services, ICWS 2010, Miami, Florida, USA, July 5–10, 2010, pages 50–57. IEEE Computer Society, 2010.Google Scholar
  17. 17.
    X. Shi, B. L. Tseng, and L. A. Adamic. Information diffusion in computer science citation networks. CoRR, abs/0905.2636, 2009.Google Scholar
  18. 18.
    V. Ventrone and S. Heiler. Some practical advice for dealing with semantic heterogeneity in federated database systems. In Proceedings of the Database Colloquium, San Diego, August 1994, Armed Forces Communications and Electronics Assc. (AFCEA), 1994.Google Scholar
  19. 19.
    G. C. Witt and G. C. Simsion. Data Modeling Essentials: Analysis, Design, and Innovation. The Coriolis, Group, 2000.Google Scholar
  20. 20.
    J. Yang and S. Counts. Comparing information diffusion structure in weblogs and microblogs. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, ICWSM 2010, Washington, DC, USA, May 23–26, 2010, pages 351–354. The AAAI Press, 2010.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Peep Küngas
    • 1
  • Marlon Dumas
    • 1
  • Shahab Mokarizadeh
    • 2
  • Mihhail Matskin
    • 2
  1. 1.University of TartuTartuEstonia
  2. 2.Royal Institute of TechnologyStockholmSweden

Personalised recommendations