大数据分析背景下的数据智能综合研究

Web Intell. Pub Date : 2022-04-07 DOI:10.3233/web-210480
C. Banchhor, N. Srinivasu
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引用次数: 1

摘要

物联网、云计算和传感器网络等现代系统产生了庞大的数据档案。从这些庞大的存档数据中提取知识需要改进算法设计技术。对如此庞大的数据进行分析的研究领域被称为大数据分析,它有助于以更低的成本优化性能,并有效地检索信息。传统数据分析的增强需要修改以适应大数据分析,因为它可能无法管理大量数据。真正的思想是如何设计适合处理大数据分析的数据挖掘算法。本文从数据分析的初始层面进行讨论,首先是关于大数据分析过程的见解。大数据分析在知识抽取领域具有当前的研究优势。本文强调了与大数据分析相关的挑战和问题,并提供了对所使用的几种技术和方法的内部见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comprehensive study of data intelligence in the context of big data analytics
Modern systems like the Internet of Things, cloud computing, and sensor networks generate a huge data archive. The knowledge extraction from these huge archived data requires modified approaches in algorithm design techniques. The field of study in which analysis of such huge data is carried out is called big data analytics, which helps to optimize the performance with reduced cost and retrieves the information efficiently. The enhancement of traditional data analytics needs to modify to suit big data analytics because it may not manage huge amounts of data. The real thought is how to design the data mining algorithms suitable to handle big data analysis. This paper discusses data analytics at the initial level, to begin with, the insights about the analysis process for big data. Big data analytics have a current research edge in the knowledge extraction field. This paper highlights the challenges and problems associated with big data analysis and provide inner insights into several techniques and methods used.
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