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Learning from Uncertainty for Big Data: Future Analytical Challenges and Strategies

机译:从不确定性中学习大数据:未来的分析挑战和策略

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摘要

This article will focus on the fourth V, the veracity, to demonstrate the essential impact of modeling uncertainty on learning performance improvement. Low veracity corresponds to the changed uncertainty and the large-scale missing values of big data. Sometimes, along with the growing size of datasets, the uncertainty of data itself often changes sharply, which definitely makes the traditional processing tools unavailable. Except for the changed uncertainty of data itself, the uncertainty in data modeling and data processing are also changing very notably.
机译:本文将重点讨论第四个V的准确性,以说明不确定性建模对学习成绩改善的本质影响。低准确性对应于变化的不确定性和大数据的大规模缺失值。有时,随着数据集规模的增长,数据本身的不确定性通常会急剧变化,这无疑使传统的处理工具不可用。除了数据本身的不确定性发生变化之外,数据建模和数据处理中的不确定性也发生了显着变化。

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