采用网格分层划分(HGP)方法实现大数据测量模型的最大精度

Verdi Yasin, O. S. Sitompul, M. Zarlis, P. Sihombing
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引用次数: 2

摘要

云计算系统中通过在线存储系统存储的数据量发展非常大,因此在处理如此庞大的数据时有可能出现问题。因此,在会议上,本研究的部分结果当然提出了一种新的方法模型,研究人员开发的是使用集成分析模型来分析数据,当然在这种情况下使用网格划分层次(HGP)来测量大数据的准确性,并使用统一建模语言(UML)作为设计工具。该框架下的大数据测量模型最大限度地满足了测量对象实体的需求或将对数据的准确性赞不绝口。因此,为了得到准确的测试结果,还需要进一步的过程来支持该方法模型的结果。当然,这种大数据测量方法模型的工作模式是同意通过云服务器或数据库管理系统服务器存储数据的集成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Big data measurement model in achieving maximum accuracy using the model Hierarchy of Grid Partition (HGP) method
The development of the amount of data stored through online-based storage systems in cloud computing systems is very large, so there is a possibility that there will be problems in processing such huge data. Therefore in the conference part of the results of this study of course presents an opinion about the new method model that researchers developed is to analyze the data using an integrated analysis model and in measuring the accuracy of large data of course on this occasion using the hierarchy of grid partition (HGP) and as a tool for design that uses the Unified Modeling Language (UML). The big data measurement model in the framework reaches the maximum of the needs of the measuring object entity or which will praise the accuracy of the data. Therefore, there needs to be a further process to support the results of this method model in order to get accurate test results. The working pattern of this big data measurement method model is, of course, agreed on the integration of data stored through the Cloud Server, or Database Management System Server.
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