Innovation in the Programmable Web: Characterizing the Mashup Ecosystem

  • Shuli Yu
  • C. Jason Woodard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5472)


This paper investigates the structure and dynamics of the Web 2.0 software ecosystem by analyzing empirical data on web service APIs and mashups. Using network analysis tools to visualize the growth of the ecosystem from December 2005 to 2007, we find that the APIs are organized into three tiers, and that mashups are often formed by combining APIs across tiers. Plotting the cumulative distribution of mashups to APIs reveals a power-law relationship, although the tail is short compared to previously reported distributions of book and movie sales. While this finding highlights the dominant role played by the most popular APIs in the mashup ecosystem, additional evidence reveals the importance of less popular APIs in weaving the ecosystem’s rich network structure.


API mashup social network power law long tail small world 


  1. 1.
    O’Reilly, T.: Web 2.0: Compact Definition? (2005),
  2. 2.
  3. 3.
    Cho, A.: An Introduction to Mashups for Health Librarians. JCHLA  28, 19–22 (2007)Google Scholar
  4. 4.
    Kulathuramaiyer, N.: Mashups: Emerging Application Development Paradigm for a Digital Journal. JUCS 13, 531–542 (2007)Google Scholar
  5. 5.
    O’Brien, D.S., Fitzgerald, B.F.: Mashups, Remixes and Copyright Law. INTLB 9(2), 17–19 (2006)Google Scholar
  6. 6.
    Goodman, E., Moed, A.: Community in Mashups: The Case of Personal Geodata (2006),
  7. 7.
    Jackson, C., Wang, H.J.: Subspace: Secure Cross-domain Communication for Web Mashups. In: 16th International World Wide Web Conference, pp. 611–620 (2007)Google Scholar
  8. 8.
    Liu, X., Hui, Y., Sun, W., Liang, H.: Towards Service Composition Based on Mashup. In: 2007 IEEE Congress on Services, pp. 332–339 (2007)Google Scholar
  9. 9.
    Hinchcliffe, D.: Is IBM Making Enterprise Mashups Respectable? (2006),
  10. 10.
    Barabási, A.-L., Albert, R.: Emergence of Scaling in Random Networks. Science 286, 509–512 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Anderson, C.: The Long Tail (2004),
  12. 12.
    Watts, D.J., Strogatz, S.H.: Collective Dynamics of “Small-World” Networks. Nature 393, 409–410 (1998)CrossRefGoogle Scholar
  13. 13.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge Univ. Press, Cambridge (1994)CrossRefzbMATHGoogle Scholar
  14. 14.
    Borgatti, S.P.: NetDraw: Graph Visualization Software. Analytic Technologies (2002)Google Scholar
  15. 15.
    Adamic, L.A.: Zipf, Power-Laws, and Pareto: A Ranking Tutorial,
  16. 16.
    Brynjolfsson, E., Hu, Y.J., Smith, M.D.: From Niches to Riches: Anatomy of the Long Tail. Sloan Mgmt. Rev. 47(4), 67–71 (2006)Google Scholar
  17. 17.
    Kilkki, K.: A Practical Model for Analyzing Long Tails. First Monday 12(5) (2007)Google Scholar
  18. 18.
    Berlind, D.: Mashup Ecosystem Poised to Explode (2006),
  19. 19.
    Hinchcliffe, D.: The Web 2.0 Mashup Ecosystem Ramps Up (2006),

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Shuli Yu
    • 1
  • C. Jason Woodard
    • 1
  1. 1.School of Information SystemsSingapore Management UniversitySingapore

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