Characteristics of a parallel data mining application implemented on an ATM connected PC cluster

1997 ◽  
pp. 303-317 ◽  
Author(s):  
Masato Oguchi ◽  
Takahiko Shintani ◽  
Takayuki Tamura ◽  
Masaru Kitsuregawa
Keyword(s):  
Data Mining ◽  
Pc Cluster ◽  
Parallel Data ◽  
Parallel Data Mining ◽  
Data Mining Application

scholarly journals IMPLEMENTATION AND EVALUATION OF RUNTIME DATA DECLUSTERING METHOD OVER SAN-CONNECTED PC CLUSTER

2014 ◽  
pp. 108-112
Author(s):  
Masato Oguchi ◽  
Masaru Kitsuregawa
Keyword(s):  
Data Mining ◽  
Performance Degradation ◽  
Area Network ◽  
Storage Area Network ◽  
Application Performance ◽  
Pc Cluster ◽  
Parallel Data ◽  
Parallel Data Mining ◽  
Storage Area ◽  
Disk Access

In this paper, a PC cluster connected with Storage Area Network (SAN) is built and evaluated. In the case of SAN­connected cluster, each node can access all shared disks directly without LAN; thus, SAN­connected clusters achieve better performance than LAN­connected clusters for disk access operations. However, if a lot of nodes access the same­shared disk simultaneously, application performance degrades due to I/O­bottleneck. A runtime data declustering method, in which data is declustered to several other disks dynamically during the execution of application, is proposed to resolve this problem. Parallel data mining is implemented and evaluated on the SAN­connected PC cluster. This application requires iterative scans of a shared disk, which degrade execution performance severely due to I/O­bottleneck. The runtime data declustering method is applied to this case. According to the results of experiments, the proposed method prevents performance degradation caused by shared disk bottleneck in SAN­connected clusters.

Parallel Data Mining on Large Scale PC Cluster

2000 ◽  
pp. 15-26
Author(s):  
Masaru Kitsuregawa ◽  
Takahiko Shintani ◽  
Masahisa Tamura ◽  
Iko Pramudiono
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Pc Cluster ◽  
Parallel Data ◽  
Parallel Data Mining
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