Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Viewpoint: From TeraGrid to knowledge grid
Communications of the ACM
Communications of the ACM
The Globus Striped GridFTP Framework and Server
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Distributed data mining services leveraging WSRF
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Globus toolkit version 4: software for service-oriented systems
NPC'05 Proceedings of the 2005 IFIP international conference on Network and Parallel Computing
Weka4WS: a WSRF-enabled weka toolkit for distributed data mining on grids
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Distributed data mining on grids: services, tools, and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Review: Service-oriented middleware: A survey
Journal of Network and Computer Applications
An empirical study on mining sequential patterns in a grid computing environment
Expert Systems with Applications: An International Journal
Heterogeneous database integration of EPR system based on OGSA-DAI
HPCA'09 Proceedings of the Second international conference on High Performance Computing and Applications
A multi-agent data mining system for cartel detection in Brazilian government procurement
Expert Systems with Applications: An International Journal
A decentralized approach for mining event correlations in distributed system monitoring
Journal of Parallel and Distributed Computing
Efficient algorithms for frequent pattern mining in many-task computing environments
Knowledge-Based Systems
A virtual mart for knowledge discovery in databases
Information Systems Frontiers
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Distribution of data and computation allows for solving larger problems and executing applications that are distributed in nature. The grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals, institutions, and resources. The grid extends the distributed and parallel computing paradigms allowing for resource negotiation and dynamical allocation, heterogeneity, open protocols, and services. Grid environments can be used both for compute-intensive tasks and data intensive applications by exploiting their resources, services, and data access mechanisms. Data mining algorithms and knowledge discovery processes are both compute and data intensive, therefore the grid can offer a computing and data management infrastructure for supporting decentralized and parallel data analysis. This paper discusses how grid computing can be used to support distributed data mining. Research activities in grid-based data mining and some challenges in this area are presented along with some promising future directions for developing grid-based distributed data mining.