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An empirical study on mining sequential patterns in a grid computing environment
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DOI：10.1016/j.eswa.2011.11.095.png)
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Abstract
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Mining sequential patterns (MSP) is an important task for knowledge discovery and data mining (KDD). Like in most KDD tasks, MSP also invokes a number of iterations for generating, adjusting, and comparing data. This paper presents an empirical study on deploying MSP in a grid computing environment and demonstrates the effectiveness and performance improvements gained in this deployment. GSP, which is a typical MSP method, is used as the mining algorithm to be investigated. A grid computing environment is designed and implemented, where all GSP functions are organized as loosely coupled web-services. MSP is achieved through the cooperation of these web-services using the divide-and-conquer strategy. Several monitoring mechanisms are developed to help manage the MSP process. The experimental results show that the proposed grid computing environment provides a flexible and efficient platform for MSP. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Data mining
Mining sequential patterns
GSP
Grid computing
Distributed processing
Loosely coupled parallelism
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