A Dynamic Bridge for Data Sharing on e-Science Grid Implementing Web 2.0 Service

  • Im Y. Jung
  • Heon Y. Yeom
Conference paper


This paper proposes a dynamic bridge for e-Science Grid, implementing Web 2.0 service in order to share experimental data effectively.An e-Science Grid has been established as a cyber laboratory for the users with a special research purpose on science. As an open space, e-Science Grid is expected to stimulate the collaborative researches and the cross domain ones. These research trends need a more efficient and convenient data service satisfying the science researchers. A dynamic bridge designed based on HVEM DataGrid, satisfies the users’ requirements for the data sharing on e-Science Grid effectively. It supports a data tagging service in order for HVEM DataGrid to be utilized more extensively without any modification of the existing Grid architecture or services. Moreover, it can be adopted and deleted easily without any effect to the legacy Grid. With the legacyinterface to access data in e-Science Grid, the data tags endow the Grid with the flexibility for data access. This paper evaluates the usefulness of the dynamic bridge by analyzing its overhead and performance.


User Cluster Deterministic Annealing Cross Domain Knowledge Background Dynamic Bridge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    A. M. Akito MONDEN and C. THOMBORSON. Tamper-resistant software system based on a finite state machine. IEICE transactions on fundamentals of electronics, communications and computer science, 88(1):112–122, 2005.Google Scholar
  2. 2.
    Im Young Jung, In Soon Cho, Heon Y. Yeom, Hee S. Kweon and Jysoo Lee. HVEM DataGrid: Implementation of a Biologic Data Management System for Experiments with High Voltage Electron Microscope. Distributed, High-Performance and Grid Computing in Computational Biology (GCCB 2006), Jan. 2007.Google Scholar
  3. 3.
  4. 4.
    Seung-Jin Kwak and Jeong-Taek Kim. A Study on the Development of High Voltage Electron Microscope Metadata Model for Efficient Management and Sharing. Journal of Korean library and information science society, 38(3):117–138, 2007.Google Scholar
  5. 5.
    Geoffrey C. Fox et al., Web 2.0 for Grids and e-Science. Instrumenting the Grid 2nd International Workshop on Distributed Cooperative Laboratories(INGRID 2007), ITALY, April, 2007.Google Scholar
  6. 6.
    Geoffrey C. Fox et al., Web 2.0 for E-Science Environments. 3rd International Conference on Semantics, Knowledge and Grid(SKG2007), China, October, 2007.Google Scholar
  7. 7.
    Ahmet Fatih Mustacoglu, Ahmet E. Topcu Aurel Cami, Geoffrey Fox. A Novel Event-Based Consistency Model for Supporting Collaborative Cyberinfrastructure Based Scientific Research. Proceedings of The 2007 International Symposium on Collaborative Technologies and Systems (CTS 2007), 2007.Google Scholar
  8. 8.
    Marlon E. Pierce, Geoffrey C. Fox, Joshua Rosen, Siddharth Maini, and Jong Y. Choi. Social networking for scientists using tagging and shared bookmarks: a Web 2.0 application. International Symposium on Collaborative Technologies and Systems (CTS 2008), 2008.Google Scholar
  9. 9.
    H. Halpin, V. Robu, and H. Shepherd. The Complex Dynamics of Collaborative Tagging. In Proceedings of the 16th international Conference on World Wide Web(WWW'07), ACM, New York, 211–220, 2007.Google Scholar
  10. 10.
    Y. Zhao, G. Karypis and U. Fayyad. Hierarchical Clustering Algorithms for Document Datasets. Data Mining and Knowledge Discovery, 10:141–168, 2005.CrossRefMathSciNetGoogle Scholar
  11. 11.
    C. Ding and X. He. K-means clustering via principal component analysis. ACM International Conference Proceeding Series, 2004.Google Scholar
  12. 12.
    K. Rose. Deterministic Annealing for Clustering, Compression, Classification, Regression and Related Optimization Problems. Proc. IEEE, 86:2210–2239, 1998.CrossRefGoogle Scholar
  13. 13.
    A. Hotho, R. Jaschke, C. Schmitz and G. Stumme. Information retrieval in folksonomies: Search and ranking. The Semantic Web: Research and Applications, 4011:411–426, 2006.CrossRefGoogle Scholar
  14. 14.
    G. Karypis, E.H. Han and V. Kumar. Chameleon: hierarchical clustering using dynamic modeling. Computer, 32:68–75, 1999.CrossRefGoogle Scholar
  15. 15.
    E. Hartuv, and R. Shamir. A clustering algorithm based on graph connectivity. Information Processing Letters, 76:175–181, 2000.MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    G. Salton, A. Wong, C.S. Yang. A vector space model for automatic indexing. Commun. ACM, 18(11):613–620, 1975.MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of CSE, Seoul Nat’l UnivSeoulSouth Korea

Personalised recommendations