Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called KNOWLEDGE GRID. This paper describes the KNOWLEDGE GRID framework and presents the toolset provided by the KNOWLEDGE GRID for implementing distributed knowledge discovery. The paper discusses how to design and implement data mining applications by using the KNOWLEDGE GRID tools starting from searching grid resources, composing software and data components, and executing the resulting data mining process on a grid. Some performance results are also discussed.

Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called KNOWLEDGE GRID. This paper describes the KNOWLEDGE GRID framework and presents the toolset provided by the KNOWLEDGE GRID for implementing distributed knowledge discovery. The paper discusses how to design and implement data mining applications by using the KNOWLEDGE GRID tools starting from searching grid resources, composing software and data components, and executing the resulting data mining process on a grid. Some performance results are also discussed.

Distributed data mining on grids: services, tools, and applications

PUGLIESE, Andrea;TALIA, Domenico;TRUNFIO, Paolo
2004

Abstract

Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called KNOWLEDGE GRID. This paper describes the KNOWLEDGE GRID framework and presents the toolset provided by the KNOWLEDGE GRID for implementing distributed knowledge discovery. The paper discusses how to design and implement data mining applications by using the KNOWLEDGE GRID tools starting from searching grid resources, composing software and data components, and executing the resulting data mining process on a grid. Some performance results are also discussed.
Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called KNOWLEDGE GRID. This paper describes the KNOWLEDGE GRID framework and presents the toolset provided by the KNOWLEDGE GRID for implementing distributed knowledge discovery. The paper discusses how to design and implement data mining applications by using the KNOWLEDGE GRID tools starting from searching grid resources, composing software and data components, and executing the resulting data mining process on a grid. Some performance results are also discussed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/129124
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