流大数据分析的框架、应用和挑战综述

Syed Muhammad Nabeel Mustafa, Muhammad Umer Farooque, Muhammad Tahir, Shariq Mahmood Khan, Rohail Qamar
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引用次数: 1

摘要

数据输出在21世纪急剧增加。由于几乎所有行业的数字化,数据导入流的速度都有所提高。大数据就是指这种海量的数据。一个复杂的数据网络,具有数量、速度、多样性、真实性和价值,构成了这些信息。为了从数据中获得有用的见解,有必要对数据进行检查。“流式大数据”指的是变化非常快的数据。实时或流媒体的大数据分析大大提高了分析能力。流式大数据分析提供实时数据的瞬时分析,协助决策者。实时大数据分析已经占据了商业的中心舞台。在这篇综述性研究中,我们对流化大数据分析现象进行了深入的研究。我们还将查看正在使用的各种流分析框架。此外,我们还研究了流式大数据分析的应用领域以及遇到的困难。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Frameworks, Applications and Challenges in Streaming Big Data Analytics: A Review
Data output has increased dramatically in the twenty-first century. The speed at which data is introduced into the stream has increased as a result of the digitization of practically all industries. Big data refers to this enormous amount of data. A complex network of data with volume, velocity, diversity, authenticity, and value makes up this information. To derive useful insights from the data, it became necessary to examine the data. The term "streaming Big data" refers to data that changes very quickly. Big data analytics that are live or streaming have significantly improved analytics. Instantaneous analytics of the real-time data are provided by streaming big data analytics, assisting the decision-makers. Big data analytics in real-time has taken center stage in business. We thoroughly examine the phenomenon of streaming Big data analytics in this review study. We also look at the various streaming analytics frameworks in use. Additionally, we look into the fields in which streaming Big Data analytics are applied as well as the difficulties encountered.
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