处理大数据中的不确定性、噪音和假新闻问题

Yuvraj Singh, Pawan Singh
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引用次数: 0

摘要

每天从当今的数据框架和计算机化创新中产生巨大的tb级信息存储。检查这些庞大的信息需要在不同层次上进行大量的努力,以便为动态分离信息。在计算机化的世界中,信息来源于不同的来源,先进技术的快速进步促进了大数据的发展。它提供了一个变革性的飞跃,在许多领域的巨大数据集的分类。通常,它指的是使用传统的数据集管理设备或信息处理应用程序难以处理的庞大而复杂的数据集的分类。信息挖掘中引入的大多数方法通常都没有准备好有效地处理庞大的数据集。大数据审查的关键问题是信息库框架之间缺乏协调,就像信息挖掘和事实审查等调查工具一样。当我们希望为其可行的应用进行信息披露和描述时,这些困难通常会出现。一个关键问题是如何定量地描绘大数据的基本品质。在描述信息剧变时需要认识论分支。对大数据复杂性假设的回顾有助于理解大数据的基本属性和复杂样例的排列,完善大数据的刻画,提高信息反映,指导大数据模型和计算的注册方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Handling Uncertainty, Noise and Fake News Problem in Big Data
A tremendous store of terabytes of information is produced every day from present-day data frameworks and computerized innovations. Examination of this monstrous information requires plenty of endeavors at different levels to separate information for dynamic. In a computerized world, information is created from different sources and the quick progress from advanced advances has prompted the development of Big Data. It furnishes a transformative leap forward in many fields with an assortment of enormous datasets. As a rule, it alludes to the assortment of enormous and complex datasets which are hard to handle utilizing conventional data set administration apparatuses or information handling applications. The majority of the introduced approaches in information mining are not normally ready to deal with the enormous datasets effectively. The critical issue in the examination of Big Data is the absence of coordination between information base frameworks just as with investigation instruments like information mining and factual examination. These difficulties by and large emerge when we wish to perform information disclosure and portrayal for its viable applications. A crucial issue is how to quantitatively portray the fundamental qualities of Big Data. There is a requirement for epistemological ramifications in portraying information upheaval. Also, the review on the intricacy hypothesis of Big Data will assist with understanding fundamental attributes and arrangement of complicated examples in Big Data, improve its portrayal, improve information reflection, and guide the plan of registering models and calculations on Big Data.
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