Data Mining Techniques in Grid Computing Environments

Preface

Modern organizations across many sectors rely increasingly on computerized information processes and infrastructures. This is particularly true for high-tech and knowledge sectors such as finance, communication, engineering, manufacturing, government, education, medicine, science and technology. As the underlying information systems evolve and become progressively more sophisticated, their users and managers are facing an exponentially growing volume of increasingly complex data, information, and knowledge. Exploring, analyzing and interpreting this information is a challenging task. Besides traditional statistics-based methods, data mining is quickly becoming a key technology in addressing the data analysis and interpretation tasks.

Data mining can be viewed as the formulation, analysis, and implementation of an induction process (proceeding from specific data to general patterns) that facilitates the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. Data mining ranges from highly theoretical mathematical work in areas such as statistics, machine learning, knowledge representation and algorithms to systems solutions for problems like fraud detection, modeling of cancer and other complex diseases, network intrusion, information retrieval on the Web and monitoring of grid systems. Data mining techniques are increasingly employed in traditional scientific discovery disciplines, such as biological, medical, biomedical, chemical, ...