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
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;
Conference_Titel :
Computer Science and Software Engineering, 2008 International Conference on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-0-7695-3336-0
DOI :
10.1109/CSSE.2008.629