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Cluster Computing

, Volume 22, Supplement 2, pp 2921–2930 | Cite as

Service ranking in service networks using parameters in complex networks: a comparative study

  • Shiyuan Zhou
  • Yinglin WangEmail author
Article

Abstract

In recent years, the number of services, together with their types, is expanding rapidly. Confronted with such a large number of services, people are hard to discover the desired services. Service ranking is regarded as a significant step to solve the problem. But, how to evaluate the importance of services correctly is still a significant issue. Though there are some literatures to rank services, it is not enough. In this paper, we proposed to apply parameters in complex networks to measure the importance of services. First, our approach models services and the relations between every pair of services as a service network. Second, we apply a set of parameters in complex networks to measure the corresponding parameter values as their importance. Finally, services will be ranked according to their importance in a descending order. Empirical experiments are performed on a real-world data set crawled from ProgrammableWeb, and the results show the similarity and difference between the results of different parameters.

Keywords

Service-oriented computing Service ranking Service importance Complex network Mashup Web API 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61375053).

References

  1. 1.
    Papazoglou, M.P.: Service-oriented computing: concepts, characteristics and directions. Inf. Syst. J. 1, 3–12 (2003)Google Scholar
  2. 2.
    Mcilraith, S.A., Son, T.C., Zeng, H.: Semantic web services. IEEE Intell. Syst. 16, 46–53 (2012)Google Scholar
  3. 3.
    Pan, W.F., Li, B., Shao, B., He, P.: Service classification and recommendation based on software networks. Chin. J. Comput. 34, 2355–2369 (2011)Google Scholar
  4. 4.
    Pan, W.F., Li, B., Jiang, B., Ju, C.H.: Service composition recommendation based on service network. Syst. Eng. Theory Pract. 34, 131–142 (2014)Google Scholar
  5. 5.
    Costa, L.F., Rodrigues, F.A., Travieso, G., Boas, P.R.V.: Characterization of complex networks: a survey of measurements. Adv. Phys. 56, 167–242 (2007)Google Scholar
  6. 6.
    Pan, W.F., Li, B., Ma, Y.T., Liu, J.: Multi-granularity evolution analysis of software using complex network theory. J. Syst. Sci. Complex. 24, 1068–1082 (2011)Google Scholar
  7. 7.
    Pan, W.F., Li, B., Liu, J., Ma, Y.T., Hu, B.: Analyzing the structure of Java software systems by weighted k-core decomposition. Future Gener. Comput. Syst. (2017).  https://doi.org/10.1016/j.future.2017.09.039
  8. 8.
    Gekas, J.: Web service ranking in service networks. In: Proceedings of the European Semantic Web Conference (ESWC 2006), pp. 1–3 (2006)Google Scholar
  9. 9.
    Skoutas, D., Sacharidis, D., Simitsis, A., Sellis, T.: Ranking and clustering web services using multi-criteria dominance relationships. IEEE Trans. Serv. Comput. 3, 163C177 (2010)Google Scholar
  10. 10.
    Zhou, Y., Liu, L., Perng, C.S., Sailer, A.: Ranking services by service network structure and service attributes. In: Proceedings of the IEEE International Conference on Web Services, pp. 26–33 (2013)Google Scholar
  11. 11.
    Wang, M.C., Luo, S.: SRPASN: service ranking using PageRank algorithm in service networks. Tech. Bull. 55, 233–238 (2017)Google Scholar
  12. 12.
    Zaidman, A., Demeyer, S.: Automatic identification of key classes in a software system using web mining techniques. J. Softw. Maint. Evol. Res. Pract. 20, 387–417 (2008)Google Scholar
  13. 13.
    Perin, F., Renggli, L., Ressia, J.: Ranking software artifacts. In: Proceedings of 4th Workshop on FAMIX and Moose in Software Reengineering (FAMOOSr’10), pp. 1–4 (2010)Google Scholar
  14. 14.
    Steidl, D., Hummel, B., Juergens, E.: Using network analysis for recommendation of central software classes. In: Proceedings of the 19th Working Conference on Reverse Engineering (WCRE’12), pp. 93–102 (2012)Google Scholar
  15. 15.
    Pan, W.F., Song, B.B., Li, K.S., Zhang, K.J.: Identifying key classes in object-oriented software using generalized k-core decomposition. Future Gener. Comput. Syst. (2017).  https://doi.org/10.1016/j.future.2017.10.006
  16. 16.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)Google Scholar
  17. 17.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393, 440–442 (1998)Google Scholar
  18. 18.
    Borgatti, S.P.: Centrality and network flow. Soc. Netw. 27, 55–71 (2005)Google Scholar
  19. 19.
    Benzi, M., Klymko, C.: A matrix analysis of different centrality measures. SIAM J. Matrix Anal. Appl. 36, 686–706 (2013)Google Scholar
  20. 20.
    Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25, 163–177 (2001)Google Scholar
  21. 21.
    Sabidussi, G.: The centrality index of a graph. Psychometrikam 31, 581–603 (1966)Google Scholar
  22. 22.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30, 107–117 (1998)Google Scholar
  23. 23.
    Alvarez-Hamelin, J.I., Dall’Asta, L., Barrat, A., Vespignani, A.: \(k\)-core decomposition of Internet graphs: hierarchies, self-similarity and measurement biases. Netw. Heterog. Media 3, 371–394 (2008)Google Scholar
  24. 24.
    Kendall, M.: A new measure of rank correlation. Biometrika 30, 81–93 (1938)Google Scholar
  25. 25.
    Newman, M.E.J.: Spread of epidemic disease on networks. Phys. Rev. E 66, 016128 (2002)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Management and EngineeringShanghai University of Finance and EconomicsShanghaiChina
  2. 2.Jiaxing UniversityJiaxingChina

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