Parallel, Distributed, and Grid-Based Data Mining

Abstract

Knowledge discovery has become a necessary task in scientific, life sciences, and business fields, both for the growing amount of data being collected and for the complexity of the analysis that need to be performed on it. Classic data mining techniques, developed for centralized sites, often reveal themselves inadequate, due to some unique characteristics of today’s data sources. In such cases, sequential approaches to data mining cannot provide for scalability, in terms of the data dimensionality, size, and runtime performance. Moreover, the increasing trend towards decentralized business organizations, distribution of users, software, and hardware systems magnifies the need for more advanced and flexible approaches and solutions. Life science is one of the application areas that best resemble such scenario. This chapter presents the state of the art about the major data mining techniques, systems and approaches. A detailed taxonomy is drawn by analyzing and comparing parallel, distributed and Grid-based data mining methods, with a particular focus on the exploitation of large and remotely dispersed datasets and/or high-performance computers.