Digital data volumes are growing exponentially in all sciences. To handle this abundance in data availability, scientists must use data analysis techniques in their scientific practices and solving environments to get the benefits coming from knowledge that can be extracted from large data sources. When data is maintained over geographically remote sites the computational power of distributed and parallel systems can be exploited for knowledge discovery in scientific data. In this scenario the Grid can provide an effective computational support for distributed knowledge discovery on large datasets. In particular, Grid services for data integration and analysis can represent a primary component for e-science applications involving distributed massive and complex data sets. This paper describes some research activities in data-intensive Grid computing. In particular we discuss the use of data mining models and services on Grid systems for the analysis of large data repositories.

Using grids for exploiting the abundance of data in science

Eugenio Cesario
;
Domenico Talia
2010

Abstract

Digital data volumes are growing exponentially in all sciences. To handle this abundance in data availability, scientists must use data analysis techniques in their scientific practices and solving environments to get the benefits coming from knowledge that can be extracted from large data sources. When data is maintained over geographically remote sites the computational power of distributed and parallel systems can be exploited for knowledge discovery in scientific data. In this scenario the Grid can provide an effective computational support for distributed knowledge discovery on large datasets. In particular, Grid services for data integration and analysis can represent a primary component for e-science applications involving distributed massive and complex data sets. This paper describes some research activities in data-intensive Grid computing. In particular we discuss the use of data mining models and services on Grid systems for the analysis of large data repositories.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/304811
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