Data Mining Techniques in Grid Computing Environments

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Data mining meets gridcomputing: Time to dance?

Alberto Sánchez, Jesús Montes, Werner Dubitzky, Julio J. Valdés, María S. Pérez and Pedro de Miguel

ABSTRACT

A grand challenge problem (Wah, 1993) refers to a computing problem that cannot be solved in a reasonable amount of time with conventional computers. While grand challenge problems can be found in many domains, science applications are typically at the forefront of these largescale computing problems. Fundamental scientific problems currently being explored generate increasingly complex data, require more realistic simulations of the processes under study and demand greater and more intricate visualizations of the results. These problems often require numerous complex calculations and collaboration among people with multiple disciplines and geographic locations. Examples of scientific grand challenge problems include multi-scale environmental modelling and ecosystem simulations, biomedical imaging and biomechanics, nuclear power and weapons simulations, fluid dynamics and fundamental computational science (use of computation to attain scientific knowledge) (Butler, 1999; Gomes and Selman, 2005).

Many grand challenge problems involve the analysis of very large volumes of data. Data mining (also known as knowledge discovery in databases) (Frawley, Piatetsky-Shapiro and Matheus, 1992) is a well stablished field of computer science concerned with the automated search of large volumes of data for patterns that can be considered ...