Abstract:- Distributed data mining plays a crucial role in knowledge discovery in very large database. 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. The key issue for distributed data mining Grid system is how to scheduling data mining tasks in a high efficient way. In this paper, we propose a novel and efficient mechanism which is based on decomposing and mapping data mining tasks to DAG, and ordering them according the respective execution cost. The results show that this mechanism is scalable and feasibility
Abstract In large Grids, like the National Grid Service (NGS), or large distributed architecture dif...
Fog computing (FC) is an emerging paradigm that extends computation, communication, and storage faci...
In many industrial, scientific and commercial applications, it is often necessary to analyze large d...
Increasingly the datasets used for data mining are huge and physically distributed
Abstract. Increasingly the datasets used for data mining are becoming huge and physically distribute...
The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Di...
Abstract: In order to improve the performance of Data Mining applications, an effective method is ta...
Abstract:- In this paper, we propose a new solution for dynamic task scheduling in distributed envir...
International audienceScheduling in computational grids addresses the allocation of computing jobs t...
Abstract—In this paper, we discuss a Grid data mining system based on the MapReduce paradigm of comp...
Abstract—The growing computerization in modern academic and industrial sectors is generating huge vo...
International audienceVery large data volumes and high computation costs in data mining applications...
This is an open access article that can be obtained from the links below - Copyright @ 2006 Springer...
This is an open access article, that can be obtained from the link below- Copyright @ 2006 Wiley-Bla...
The use of information technology (IT) in scientific investigations is now commonplace, due largely ...
Abstract In large Grids, like the National Grid Service (NGS), or large distributed architecture dif...
Fog computing (FC) is an emerging paradigm that extends computation, communication, and storage faci...
In many industrial, scientific and commercial applications, it is often necessary to analyze large d...
Increasingly the datasets used for data mining are huge and physically distributed
Abstract. Increasingly the datasets used for data mining are becoming huge and physically distribute...
The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Di...
Abstract: In order to improve the performance of Data Mining applications, an effective method is ta...
Abstract:- In this paper, we propose a new solution for dynamic task scheduling in distributed envir...
International audienceScheduling in computational grids addresses the allocation of computing jobs t...
Abstract—In this paper, we discuss a Grid data mining system based on the MapReduce paradigm of comp...
Abstract—The growing computerization in modern academic and industrial sectors is generating huge vo...
International audienceVery large data volumes and high computation costs in data mining applications...
This is an open access article that can be obtained from the links below - Copyright @ 2006 Springer...
This is an open access article, that can be obtained from the link below- Copyright @ 2006 Wiley-Bla...
The use of information technology (IT) in scientific investigations is now commonplace, due largely ...
Abstract In large Grids, like the National Grid Service (NGS), or large distributed architecture dif...
Fog computing (FC) is an emerging paradigm that extends computation, communication, and storage faci...
In many industrial, scientific and commercial applications, it is often necessary to analyze large d...