Performance-based data distribution for data mining applications on grid computing environments

Authors:
Wen-Chung Shih;Chao-Tung Yang;Shian-Shyong Tseng
Affiliations:
Department of Information Science and Applications, Asia University, Taichung, Taiwan, ROC 41354;High-Performance Computing Laboratory, Department of Computer Science, Tunghai University, Taichung, Taiwan, ROC 40704;Department of Information Science and Applications, Asia University, Taichung, Taiwan, ROC 41354 and Department of Computer and Information Science, National Chiao Tung University, Hsinchu, Taiwan ...
Venue:
The Journal of Supercomputing
Year:
2010

Citing 22
Cited 0

Partitioning Techniques for Large-Grained Parallelism

IEEE Transactions on Computers
PVM: a framework for parallel distributed computing

Concurrency: Practice and Experience
The grid: blueprint for a new computing infrastructure

The grid: blueprint for a new computing infrastructure
Data mining: concepts and techniques

Data mining: concepts and techniques
A Novel Data Distribution Technique for Host-Client Type Parallel Applications

IEEE Transactions on Parallel and Distributed Systems
A grid-enabled MPI: message passing in heterogeneous distributed computing systems

SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
Scheduling Divisible Loads in Parallel and Distributed Systems

Scheduling Divisible Loads in Parallel and Distributed Systems
Parallel and Distributed Association Mining: A Survey

IEEE Concurrency
Parallel Mining of Association Rules

IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining

VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Scheduling High Performance Data Mining Tasks on a Data Grid Environment

Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Ten Reasons to Use Divisible Load Theory

Computer
Data Mining on NASA's Information Power Grid

HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Commercial Applications on the AP3000 Parallel Computer

MPPM '97 Proceedings of the Conference on Massively Parallel Programming Models
Parallel Classification for Data Mining on Shared-Memory Multiprocessors

ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Divisible Load Theory: A New Paradigm for Load Scheduling in Distributed Systems

Cluster Computing
Scheduling Divisible Loads on Star and Tree Networks: Results and Open Problems

IEEE Transactions on Parallel and Distributed Systems
The Anatomy of the Grid: Enabling Scalable Virtual Organizations

International Journal of High Performance Computing Applications
Dynamic Load Balancing for the Distributed Mining of Molecular Structures

IEEE Transactions on Parallel and Distributed Systems
Introduction to grid computing with globus

Introduction to grid computing with globus
Distributed data mining on grids: services, tools, and applications

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Effective data distribution techniques can significantly reduce the total execution time of a program on grid computing environments, especially for data mining applications. In this paper, we describe a linear programming formulation for the data distribution problem on grids. Furthermore, a heuristic method, named Heuristic Data Distribution Scheme (HDDS), is proposed to solve this problem. We implement two types of data mining applications, Association Rule Mining and Decision Tree Construction, and conduct experiments on grid testbeds. Experimental results show that data mining programs using the proposed HDDS to distribute data could execute more efficiently than traditional schemes could.