A Grid-Based Infrastructure for Distributed Retrieval

  • Fabio Simeoni
  • Leonardo Candela
  • George Kakaletris
  • Mads Sibeko
  • Pasquale Pagano
  • Giorgos Papanikos
  • Paul Polydoras
  • Yannis Ioannidis
  • Dagfinn Aarvaag
  • Fabio Crestani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4675)

Abstract

In large-scale distributed retrieval, challenges of latency, heterogeneity, and dynamicity emphasise the importance of infrastructural support in reducing the development costs of state-of-the-art solutions. We present a service-based infrastructure for distributed retrieval which blends middleware facilities and a design framework to ‘lift’ the resource sharing approach and the computational services of a European Grid platform into the domain of e-Science applications. In this paper, we give an overview of the DiligentSearch Framework and illustrate its exploitation in the field of Earth Science.

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References

  1. 1.
    Blair, D.C.: The data-document distinction revisited. SIGMIS Database 37, 77–96 (2006)CrossRefGoogle Scholar
  2. 2.
    Sanderson, R.: Srw: Search/retrieve webservice. Public Draft (2003)Google Scholar
  3. 3.
    Callan, J.: 5 Distributed Information Retrieval. In: Advances in Information Retrieval, pp. 127–150. Kluwer Academic Publishers, Hingham, MA (2000)Google Scholar
  4. 4.
    Kobayashi, M., Takeda, K.: Information retrieval on the web. ACM Comput. Surv. 32, 144–173 (2000)CrossRefGoogle Scholar
  5. 5.
    Risson, J., Moors, T.: Survey of research towards robust peer-to-peer networks: search methods. Comput. Networks 50, 3485–3521 (2006)MATHCrossRefGoogle Scholar
  6. 6.
    Atkinson, M., Crowcroft, J., Goble, C., Gurd, J., Rodden, T., Shadbolt, N., Sloman, M., Sommerville, I., Storey, T.: Computer Challenges to emerge from eScience (e-Science vision document)Google Scholar
  7. 7.
    Foster, I., Kesselman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organization. The International Journal of High Performance Computing Applications 15, 200–222 (2001)CrossRefGoogle Scholar
  8. 8.
    Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration. Open Grid Service Infrastructure WG, Global Grid Forum (2002)Google Scholar
  9. 9.
    Globus Alliance: The Globus Alliance Website, http://www.globus.org/
  10. 10.
    EGEE: Enabling Grids for E-sciencE. INFSO 508833, http://public.eu-egee.org/
  11. 11.
    Atkins, D.E., Droegemeier, K.K., Feldman, S.I., Garcia-Molina, H., Klein, M.L., Messerschmitt, D.G., Messina, P., Ostriker, J.P., Wright, M.H.: Revolutionizing science and engineering through cyberinfrastructure. Report of the National Science Foundation Blue-Ribbon Advisory Panel on Cyberinfrastructure (2003)Google Scholar
  12. 12.
    Larson, R.R., Sanderson, R.: Grid-based digital libraries: Cheshire3 and distributed retrieval. In: JCDL 2005. Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 112–113. ACM Press, New York (2005)CrossRefGoogle Scholar
  13. 13.
    GRACE: GRid seArch & Categorization Engine (2005), http://www.grace-ist.org
  14. 14.
    Banks, T.: Web Services Resource Framework (WSRF) - Primer. Committee draft 01, OASIS (2005), http://docs.oasis-open.org/wsrf/wsrf-primer-1.2-primer-cd-01.pdf
  15. 15.
    Niblett, P., Graham, S.: Events and service-oriented architecture: The oasis web services notification specification. IBM Systems Journal 44, 869–886 (2005)CrossRefGoogle Scholar
  16. 16.
    Kossmann, D.: The state of the art in distributed query processing. ACM Computing Surveys 32, 422–469 (2000)CrossRefGoogle Scholar
  17. 17.
    Ioannidis, Y.E.: Query optimization. ACM Computing Surveys 28, 121–123 (1996)CrossRefGoogle Scholar
  18. 18.
    Stonebraker, M., Aoki, P., Litwin, W., Pfeffer, A., Sah, A., Sidell, J., Staelin, C., Yu, A.: Mariposa: A Wide-Area Distributed Database System. The VLDB Journal 5, 48–63 (1996)CrossRefGoogle Scholar
  19. 19.
    Chen, C., Roussopoulos, N.: Adaptive selectivity estimation using query feedback. In: 1994 ACM SIGMOD International Conference on Management of data, pp. 161–172 (1994)Google Scholar
  20. 20.
    Simeoni, F., Azzopardi, L., Crestani, F.: An application framework for distributed information retrieval. In: Sugimoto, S., Hunter, J., Rauber, A., Morishima, A. (eds.) ICADL 2006. LNCS, vol. 4312, pp. 192–201. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Callan, J.P., Connell, M.E.: Query-based sampling of text databases. Information Systems 19, 97–130 (2001)Google Scholar
  22. 22.
    Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: SIGMOD 1984. Proceedings of the 1984 ACM SIGMOD international conference on Management of data, pp. 47–57. ACM Press, New York (1984)CrossRefGoogle Scholar
  23. 23.
    Manolopoulos, Y., Nanopoulos, A., Papadopoulos, A., Theodoridis, Y.: R-Trees: Theory and Applications. In: Advanced Information and Knowledge Processing, Springer, Heidelberg (2006)Google Scholar
  24. 24.
    Martínez, C.: Partial Quicksort. In: ANALCO 2004. The First Workshop on Analytic Algorithmics and Combinatorics, New Orleans (2004)Google Scholar
  25. 25.
    Callan, J.P., Lu, Z., Croft, W.B.: Searching distributed collections with inference networks. In: SIGIR 1995. Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 21–28. ACM Press, New York (1995)CrossRefGoogle Scholar
  26. 26.
    Sun, W., Ling, Y., Rishe, N., Deng, Y.: An instant and accurate size estimation method for joins and selections in a retrieval-intensive environment. In: SIGMOD 1993. Proceedings of the 1993 ACM SIGMOD international conference on Management of data, pp. 79–88. ACM Press, New York (1993)CrossRefGoogle Scholar
  27. 27.
    Si, L., Callan, J.: A semisupervised learning method to merge search engine results. ACM Trans. Inf. Syst. 21, 457–491 (2003)CrossRefGoogle Scholar
  28. 28.
    Si, L., Callan, J.P.: Unified utility maximization framework for resource selection. In: Grossman, D., Gravano, L., Zhai, C., Herzog, O., Evans, D.A. (eds.) CIKM, pp. 32–41. ACM, New York (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Fabio Simeoni
    • 1
  • Leonardo Candela
    • 2
  • George Kakaletris
    • 3
  • Mads Sibeko
    • 4
  • Pasquale Pagano
    • 2
  • Giorgos Papanikos
    • 3
  • Paul Polydoras
    • 3
  • Yannis Ioannidis
    • 3
  • Dagfinn Aarvaag
    • 4
  • Fabio Crestani
    • 1
  1. 1.Department of Computer and Information Sciences, University of Strathclyde, GlasgowUK
  2. 2.Istituto di Scienza e Tecnologie dell’Informazione “Alessandro Faedo” – CNR, PisaItaly
  3. 3.Department of Informatics and Telecommunications, University of Athens – AthensGreece
  4. 4.Fast Search & Transfer ASA, OsloNorway

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