大数据分析项目的评估和项目预测分析方法

G. Kabanda
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引用次数: 3

摘要

大数据是管理从内部、外部、结构化和非结构化等多种异构数据类型中获取的大量数据的过程,这些数据可用于收集和分析企业数据。本文的目的是对大数据分析项目进行评估,讨论项目失败的原因,并解释为什么以及如何项目预测分析(PPA)方法可能会对基于数据挖掘,机器学习和人工智能的未来方法产生影响。采用定性研究方法。本研究采用语篇分析为主,文献分析为主的研究设计。拉克劳和墨菲的话语理论是最彻底的后结构主义方法。联系Gabriel Kabanda gabrielkabanda@gmail.com大西洋国际大学900 Fort Street Mall 40檀香山,
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Evaluation of Big Data Analytics Projects and the Project Predictive Analytics Approach
Big Data is the process of managing large volumes of data obtained from several heterogeneous data types e.g. internal, external, structured and unstructured that can be used for collecting and analyzing enterprise data. The purpose of the paper is to conduct an evaluation of Big Data Analytics Projects which discusses why the projects fail and explain why and how the Project Predictive Analytics (PPA) approach may make a difference with respect to the future methods based on data mining, machine learning, and artificial intelligence. A qualitative research methodology was used. The research design was discourse analysis supported by document analysis. Laclau and Mouffe’s discourse theory was the most thoroughly poststructuralist approach. CONTACT Gabriel Kabanda gabrielkabanda@gmail.com Atlantic International University 900 Fort Street Mall 40 Honolulu,
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