Abstract: In order to improve the performance of Data Mining applications, an effective method is task parallelization. The scheduler on Grid plays an important role to management subtasks so as to achieve high performance. We introduce an additional component that we call serializer, whose purpose is to decompose the tasks into a series of independent tasks according the directed acyclic graph (DAG), and send them to the scheduler queue as soon as they become executable with respect to the DAG dependencies. The experimental result demonstrates that the architecture has good performance
International audienceVery large data volumes and high computation costs in data mining applications...
Abstract: Grid computing is nothing but the computing environment in which the resources are shared ...
Abstract. Task Scheduling is a critical design issue of distributed computing. The emerging Grid com...
Abstract:- Distributed data mining plays a crucial role in knowledge discovery in very large databas...
Increasingly the datasets used for data mining are huge and physically distributed
Abstract—In this paper, we discuss a Grid data mining system based on the MapReduce paradigm of comp...
The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Di...
Abstract. Increasingly the datasets used for data mining are becoming huge and physically distribute...
Grid Computing systems aim to enable the sharing, selection, and aggregation of a wide variety of re...
Navigation or dynamic scheduling of applications on computational grids can be improved through the ...
Abstract In large Grids, like the National Grid Service (NGS), or large distributed architecture dif...
GRID environments are privileged targets for computation-intensive problem solving in areas from wea...
Abstract—The growing computerization in modern academic and industrial sectors is generating huge vo...
A grid consists of high-end computational, storage, and network resources that, while known a priori...
Data Grid technology promises geographically distributed scientists to access and share physically d...
International audienceVery large data volumes and high computation costs in data mining applications...
Abstract: Grid computing is nothing but the computing environment in which the resources are shared ...
Abstract. Task Scheduling is a critical design issue of distributed computing. The emerging Grid com...
Abstract:- Distributed data mining plays a crucial role in knowledge discovery in very large databas...
Increasingly the datasets used for data mining are huge and physically distributed
Abstract—In this paper, we discuss a Grid data mining system based on the MapReduce paradigm of comp...
The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Di...
Abstract. Increasingly the datasets used for data mining are becoming huge and physically distribute...
Grid Computing systems aim to enable the sharing, selection, and aggregation of a wide variety of re...
Navigation or dynamic scheduling of applications on computational grids can be improved through the ...
Abstract In large Grids, like the National Grid Service (NGS), or large distributed architecture dif...
GRID environments are privileged targets for computation-intensive problem solving in areas from wea...
Abstract—The growing computerization in modern academic and industrial sectors is generating huge vo...
A grid consists of high-end computational, storage, and network resources that, while known a priori...
Data Grid technology promises geographically distributed scientists to access and share physically d...
International audienceVery large data volumes and high computation costs in data mining applications...
Abstract: Grid computing is nothing but the computing environment in which the resources are shared ...
Abstract. Task Scheduling is a critical design issue of distributed computing. The emerging Grid com...