The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
The AppLeS parameter sweep template: user-level middleware for the grid
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
NILE: wide-area computing for high energy physics
EW 7 Proceedings of the 7th workshop on ACM SIGOPS European workshop: Systems support for worldwide applications
Parallel data intensive computing in scientific and commercial applications
Parallel Computing - Parallel data-intensive algorithms and applications
Parallelism in Knowledge Discovery Techniques
PARA '02 Proceedings of the 6th International Conference on Applied Parallel Computing Advanced Scientific Computing
Giggle: a framework for constructing scalable replica location services
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
The SDSC storage resource broker
CASCON '98 Proceedings of the 1998 conference of the Centre for Advanced Studies on Collaborative research
The Cactus Code: A Problem Solving Environment for the Grid
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
High Performance Parametric Modeling with Nimrod/G: Killer Application for the Global Grid?
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Rough set based data mining tasks scheduling on knowledge grid
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
Rough set based computation times estimation on knowledge grid
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
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Grid is a new solution to computationally and data intensive computing problems. Since the distributed knowledge discovery process is both data and computational intensive, the Grid is a natural platform for deploying a high performance data mining service. In order to improve the performance of data mining applications, an effective method is task parallelization. Existing mechanisms of data mining parallelization are based on NOW or SMP, it is necessary to develop new parallel mechanism for grid feature. In this paper, we present a framework for high performance DDM applications in Computational Grid environments called Data Mining Grid, with the function for decomposing data mining application into subtasks and then combine those subtasks to form directed acyclic graph. This kind of parallel mechanism decomposes application according to the actual computation power of each node in dynamic Grid environment.