• DocumentCode
    1957680
  • Title

    The Anatomy of Weka4WS: A WSRF-enabled Toolkit for Distributed Data Mining on Grid

  • Author

    Zheng, Zhao ; Shu, Gao

  • Author_Institution
    Sch. of Comput. Sci., Wuhan Univ. of Technol., Wuhan
  • Volume
    3
  • fYear
    2008
  • fDate
    12-14 Dec. 2008
  • Firstpage
    53
  • Lastpage
    57
  • Abstract
    Data mining technology is widely used for the analysis of large datasets stored in databases. However, conventional data mining is not satisfied with the requirement due to the heterogeneous and distributed of the datasets. Grid computing emerged as an important new field of distributed computing, which could support for distributed knowledge discovery applications. Weka4WS is an open-source framework extended from the Weka toolkit for distributed data mining on Grid, which deploys many of machine learning algorithms provided by Weka Toolkit as WSRF-compliant services. This paper presents the architecture, implementation and execution of Weka4WS. At last, an example about distributed Classification is given to illustrate the effective of Weka4WS framework further.
  • Keywords
    data mining; grid computing; WSRF-compliant services; Weka toolkit; Weka4WS; datasets; distributed data mining; distributed knowledge discovery applications; grid computing; Anatomy; Computer architecture; Computer science; Data mining; Delta modulation; Distributed computing; Grid computing; Machine learning algorithms; Resource management; Web services; GT4; Mammography; Weka; Weka4WS;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Software Engineering, 2008 International Conference on
  • Conference_Location
    Wuhan, Hubei
  • Print_ISBN
    978-0-7695-3336-0
  • Type

    conf

  • DOI
    10.1109/CSSE.2008.629
  • Filename
    4722288