Chapter 14: Building Search Computing Applications

  • Alessandro Bozzon
  • Marco Brambilla
  • Stefano Ceri
  • Francesco Corcoglioniti
  • Nicola Gatti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5950)

Abstract

Search Computing aims at opening the Web to a new class of search applications, by offering enhanced expressive and computational power. The success of Search Computing, as of any technical advance, will be measured by its impact upon the search industry and market, and this in turn will be highly influenced by reactions of Web users and developers. It is too early to anticipate such reactions – as the technology is still “under construction” – but this chapter attempts a first identification of the possible future players in the development of Search Computing applications, by grossly identifying the roles of “data source publishers” and of “application developers”, and by discussing how classical advertising-based models may support the new applications. This chapter also describes the high-level design of the prototyping environment that is currently under development and how the design will support the deployment upon high performance architectures. Finally, we describe advertising as the prevalent business model of the search engines industry, and briefly discuss the options for the evolution of such model in the context of Search Computing.

Keywords

Search Computing software engineering development process advertising models cloud computing software architectures 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Amazon. Elastic Compute Cloud, EC2 (2009), http://aws.amazon.com/ec2/
  2. 2.
    Hayes, B.: Cloud computing. Communications of the ACM 51(7), 9–11 (2008)CrossRefGoogle Scholar
  3. 3.
    Farrell, J., Nezlek, G.S.: Rich Internet Applications The Next Stage of Application Development. In: 29th International Conference on Information Technology Interfaces, ITI 2007, June 25-28, pp. 413–418 (2007)Google Scholar
  4. 4.
    Blumofe, R.D., Leiserson, C.E.: Scheduling multithreaded computations by work stealing. J. ACM 46(5), 720–748 (1999)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Bondi, A.B.: Characteristics of scalability and their impact on performance. In: WOSP 2000: Proceedings of the 2nd international workshop on Software and performance, pp. 195–203. ACM, New York (2000)Google Scholar
  6. 6.
    Buyya, R., Abramson, D., Giddy, J., Stockinger, H.: Economic models for resource management and scheduling in grid computing. Concurrency and Computation: Practice and Experience 14(13-15), 1507–1542 (2002)CrossRefMATHGoogle Scholar
  7. 7.
    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009)CrossRefGoogle Scholar
  8. 8.
    Cardellini, V., Casalicchio, E., Colajanni, M., Yu, P.S.: The state of the art in locally distributed web-server systems. ACM Comput. Surv. 34(2), 263–311 (2002)CrossRefGoogle Scholar
  9. 9.
    Craswell, N., Crimmins, F., Hawking, D., Moffat, A.: Performance and cost trade-offs in web search. In: ADC 2004: Proceedings of the 15th Australasian database conference, pp. 161–169. Australian Computer Society, Inc., Darlinghurst (2004)Google Scholar
  10. 10.
    Daniel, F., Yu, J., Benatallah, B., Casati, F., Matera, M., Saint-Paul, R.: Understanding UI Integration: A Survey of Problems, Technologies, and Opportunities. IEEE Internet Computing 11(3), 59–66 (2007)CrossRefGoogle Scholar
  11. 11.
    Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. In: OSDI 2004: Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation, pp. 10–10. USENIX Association, Berkeley (2004)Google Scholar
  12. 12.
    Even-Dar, E., Kearns, M., Wortman, J.: Sponsored Search with Contexts. In: Deng, X., Graham, F.C. (eds.) WINE 2007. LNCS, vol. 4858, pp. 312–317. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Feng, J., Bhargava, H.K., Pennock, D.: Implementing Sponsored Search in Web Search Engines: Computational Evaluation of Alternative Mechanisms. Informs Journal on Computing (forthcoming), http://ssrn.com/abstract=721262
  14. 14.
    Fielding, R., Gettys, J., Mogul, J.C., Frystyk, H., Masinter, L., Leach, P., Berners-Lee, T.: Hypertext transfer protocol (1998), http:/1.1.Tech.rep.Google Scholar
  15. 15.
    Google. AdSense (2009), https://www.google.com/adsense/
  16. 16.
    Google. AdWords (2009), https://www.google.com/adwords/
  17. 17.
    Kossmann, D.: The state of the art in distributed query processing. ACM Comput. Surv. 32(4), 422–469 (2000)CrossRefGoogle Scholar
  18. 18.
    Mas-Colell, A., Whinston, M.D., Green, J.R.: Microeconomic Theory. Oxford University Press, Oxford (1995)MATHGoogle Scholar
  19. 19.
    Microsoft. Microsoft Advertising (2009), http://advertising.microsoft.com/
  20. 20.
    Muth, P., Wodtke, D., Weissenfels, J., Dittrich, A.K., Weikum, G.: From centralized workflow specification to distributed workflow execution. J. Intell. Inf. Syst. 10(2), 159–184 (1998)CrossRefGoogle Scholar
  21. 21.
    Narahari, Y., Garg, D., Narayanam, R., Prakash, H.: Game theoretic problems in network economics and mechanism design solutions. Springer, Berlin (2009)MATHGoogle Scholar
  22. 22.
    Pfister, G.F.: In search of clusters, 2nd edn. Prentice-Hall, Inc., Upper Saddle River (1998)Google Scholar
  23. 23.
    Shafer, J.C., Agrawal, R., Lauw, H.W.: Symphony: Enabling Search-Driven Applications. In: USETIM (Using Search Engine Technology for Information Management) Workshop, VLDB Lyon (2009)Google Scholar
  24. 24.
    Weber, T.A., Zheng, Z.E.: A model of search intermediaries and paid referrals. Tech. rep., 02-12-01, The Wharton School, University of Pennsylvania (2003), http://papers.ssrn.com/sol3/papers.cfm?abstract_id=601903
  25. 25.
    Yahoo! APT from Yahoo! (2009), http://apt.yahoo.com/
  26. 26.
    Yahoo! SearchMarketing (2009), http://searchmarketing.yahoo.com/
  27. 27.
    Yang, H.C., Dasdan, A., Hsiao, R.L., Parker, D.S.: Map-reduce-merge: simplified relational data processing on large clusters. In: SIGMOD 2007, pp. 1029–1040. ACM, New York (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alessandro Bozzon
    • 1
  • Marco Brambilla
    • 1
  • Stefano Ceri
    • 1
  • Francesco Corcoglioniti
    • 1
  • Nicola Gatti
    • 1
  1. 1.Dipartimento di Elettronica ed InformazionePolitecnico di MilanoMilanoItaly

Personalised recommendations