资源共享下的计算机大数据挖掘服务模式探索

IF 1.1 Q3 INFORMATION SCIENCE & LIBRARY SCIENCE
WeiWei Hu, Lina Sun, Lijie Li
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引用次数: 0

摘要

为满足用户对数据挖掘服务的多样化需求,提高资源利用率和企业竞争力,本文旨在构建基于资源共享机制的大数据分析(BDA)数据挖掘服务模型。本文根据大数据分析(BDA)的特点,为其设计了一种定制化的数据挖掘服务模型。在该模型中,作者应用改进的 Apriori 算法确定优化方案,并改进蚁群优化算法,提高了数据挖掘的效率和准确性。通过分析实验结果,证明了所提出的 BDA 数据挖掘服务模型的科学性和合理性,并改进了数据挖掘模型的实施策略。这些研究成果为BDA基于响应面建模的数据挖掘服务模型提供了重要参考,也为企业在面对大数据时如何更好地利用资源、提高竞争力提供了指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Exploration of the Computer Big Data Mining Service Model Under Resource Sharing
In order to meet the diverse needs of users for data mining services and improve resource utilization and enterprise competitiveness, this article aims to construct a Big Data Analytics (BDA) data mining service model based on resource sharing mechanisms. This article designs a customized data mining service model for BDA based on its characteristics. In this model, the authors apply the improved Apriori algorithm to determine the optimization plan and improve the ant colony optimization algorithm to improve the efficiency and accuracy of data mining. By analyzing the experimental results, the scientificity and rationality of the proposed data mining service model for BDA were demonstrated, and the implementation strategy of the data mining model was improved. These research findings provide important references for BDA's data mining service model based on response surface modeling and also provide guidance for enterprises on how to better utilize resources and improve competitiveness when facing big data.
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来源期刊
Information Resources Management Journal
Information Resources Management Journal INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
3.60
自引率
7.10%
发文量
44
期刊介绍: Topics should be drawn from, but not limited to, the following areas, with major emphasis on the managerial and organizational aspects of information resource and technology management: •Application of IT to operation •Artificial intelligence and expert systems technologies and issues •Business process management and modeling •Data warehousing and mining •Database management technologies and issues •Decision support and group decision support systems •Distance learning technologies and issues •Distributed software development •E-collaboration •Electronic commerce technologies and issues •Electronic government •Emerging technologies management
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