Abstract
Algorithms and methods for analyzing large amounts of data are studied and developed. This paper presents a Data Mining (DM) method operated in grid computing environment. Because DM technology uses large amounts of data and requires costs to compute, utilizing and sharing computing data and resources are key issues in DM. Therefore, a Dynamic Load Balancing (DLB) algorithm and a decision range readjustment algorithm are proposed and applied to the Grid-based Data Mining (GDM) method. And we analyzed the average waiting time for learning and computing time. For a performance evaluation, the system execution time, computing time, and average waiting time for learning are measured. Experimental results show that GDM with the DLB method provides many advantages in terms of processing time and cost.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kumar, V., Grama, A., Rao, V.N.: Scalable Load Balancing Techniques for Parallel Computers. Journal of Distributed Computing 7 (1994)
Braspenning, P.J., Thuijsman, F., Weijters, A.J.M.M.: Artificial Neural Networks: An Introduction to ANN Theory and Practice. In: Neural Network School 1999. LNCS, vol. 931, pp. 1–66. Springer, Heidelberg (1995)
Berman, F., Fox, G., Hey, T.: Grid Computing: Making the Global Infrastructure a Reality. J. Wiley, Chichester (2003)
Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1998)
Zurada, J.M.: Introduction to Artificial Neural Systems. Jaico Publishing House (1992)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, pp. 279–310. Morgan Kaufmann, San Francisco (2000)
Nadler, M., Smith, E.P.: Pattern Recognition Engineering, pp. 75–80. John Wiley & Sons Inc., Chichester (1992)
Kumar, V., Grama, A., Gupta, A., Karypis, G.: Introduction to Parallel Computing: Design and Analysis of Algorithms. The Benjamin/Cummings Publishing Company (1994)
Kapolka, A.: The Extensible Run-Time Infrastructure (XRTI): An Experimental Implementation of Proposed Improvements to the High Level Architecture, Master’s Thesis, Naval Postgraduate School (2003)
Zaki, M.J., Li, W., Parthasarathy, S.: Customized Dynamic Load Balancing for a Network of Workstations. In: 5th IEEE International Symposium on High Performance Distributed Computing (1996)
Sanders, P.: A Detailed Analysis of Random Polling Dynamic Load Balancing. In: International Symposium on Parallel Architectures, Algorithms, and Networks (1994)
Ma, Y.B., Cho, K.C., Jang, S.H., Lee, J.S.: Grid-based ANN Data Mining for Bioinformatics Applications. In: International Conference on Hybrid Information Technology, Jeju (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, Y.B., Kim, T.Y., Song, S.H., Lee, J.S. (2009). Analysis and Experimentation of Grid-Based Data Mining with Dynamic Load Balancing. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-03348-3_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03347-6
Online ISBN: 978-3-642-03348-3
eBook Packages: Computer ScienceComputer Science (R0)