Scaling up inductive learning with massive parallelism
Machine Learning
Viewpoint: From TeraGrid to knowledge grid
Communications of the ACM
Communications of the ACM
Strategies for Parallel Data Mining
IEEE Concurrency
KNOWLEDGE GRID: High Performance Knowledge Discovery on the Grid
GRID '01 Proceedings of the Second International Workshop on Grid Computing
Parallelism in Knowledge Discovery Techniques
PARA '02 Proceedings of the 6th International Conference on Applied Parallel Computing Advanced Scientific Computing
Computational and data Grids in large-scale science and engineering
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
MSS '01 Proceedings of the Eighteenth IEEE Symposium on Mass Storage Systems and Technologies
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Data mining often is a compute intensive and time requiring process. For this reason, several data mining systems have been implemented on parallel computing platforms to achieve high performance in the analysis of large data sets. Moreover, when large data repositories are coupled with geographical distribution of data, users and systems, more sophisticated technologies are needed to implement high-performance distributed KDD systems. Recently computational Grids emerged as privileged platforms for distributed computing and a growing number of Grid-based KDD systems have been designed. In this paper we first outline different ways to exploit parallelism in the main data mining techniques and algorithms, then we discuss Grid-based KDD systems.