Analysis and Experimentation of Grid-Based Data Mining with Dynamic Load Balancing

Autor:	Tae Young Kim, Yong Beom Ma, Jong Sik Lee, Seung H. Song
Rok vydání:	2009
Předmět:	Waiting time Grid computing Computer science Dynamic load balancing Range (statistics) Data mining Grid computer.software_genre Key issues Execution time Grid based computer
Zdroj:	Advanced Data Mining and Applications ISBN: 9783642033476 ADMA
DOI:	10.1007/978-3-642-03348-3_57
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::af396dc2e81c07a0e098a286ba20ed97 https://doi.org/10.1007/978-3-642-03348-3_57 Zobrazit plný text záznamu