Earticle

초록

영어

Extracting useful knowledge from data sets measuring in gigabytes and even terabytes is a challenging research area for the data mining community. Sequential approaches suffer from a performance problem due to the fact that they have to mine voluminous databases. Parallelism is introduced as an important solution that could improve the response time and the scalability of these approaches. However, parallelization process is not trivial and still facing many challenges including the workload balancing problem.
In this paper, we propose a hierarchical dynamic load balancing strategy for parallel association rule mining algorithms in the context of a Grid computing environment. The French research grid “Grid’5000” is used as our experimental test-bed. Through a detailed experimental study, we show that our strategy improves the performance and helps the parallel algorithm to scale very well with the number of computational nodes available.

목차

Abstract
 1. Introduction
 2. Mining Association Rules
 3. Parallel and Distributed Mining of Association Rules
 4. Load Balancing and Data Mining
 5. The Hierarchical Grid Model
  5.1. The 1/N Model
  5.2. The C/N Model
 6. Characteristics of the Proposed Model
 7. The Hierarchical Workload Balancing Strategy
  7.1. The Workload Balancing Algorithms
 8. Performance Evaluation
  8.1. Parallelization Approach
  8.2. Experimental Platform
  8.3. Experiments
 9. Discussion
 10. Conclusion
 Acknowledgements
 References

키워드

저자

• Raja Tlili [ MOSIC, Computer Science Department, Faculty of Sciences of Tunisia Tunis El Manar University, Tunisia ]
• Yahya Slimani [ MOSIC, Computer Science Department, Faculty of Sciences of Tunisia Tunis El Manar University, Tunisia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605