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An Algorithm for Treating Uncertainties in the Visualization of Pipeline Sensors' Datasets

机译:管道传感器数据集可视化中的不确定性处理算法

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Researchers have seen visualization as a tool in presenting data based on available datasets. Its usage is however undermined by its inability to acknowledge the associated uncertainties in real world measurements. Visualization results are said to be "too generous", providing us with visual assumptions that though, may not be too far from reality, but the associated inaccuracies could become significant when dealing with life dependant datasets. Uncertainty reality is now becoming a significant research interest. In most cases accuracy is a neglected issue. Two wrong assumptions are believed; the first is that the data visualized is accurate, and the second is that the visualization process is exempt from errors. The objectives of this paper are to present the implications of inaccuracies and propose a treatment algorithm for the visualizations of pipeline sensors' datasets. The paper also features attributes that gives a user an idea of sensors' datasets inaccuracies.
机译:研究人员已经将可视化视为一种基于可用数据集呈现数据的工具。但是,由于无法确认现实世界中的测量结果中的不确定性,因此破坏了它的使用。可视化结果被称为“过于宽泛”,为我们提供了视觉上的假设,尽管可能与现实相距不算太远,但是在处理依赖于生命的数据集时,相关的不准确度可能变得很明显。不确定性现实现在正成为重要的研究兴趣。在大多数情况下,准确性是一个被忽略的问题。相信两个错误的假设;首先是可视化的数据是准确的,其次是可视化过程没有错误。本文的目的是提出不准确性的含义,并提出一种用于可视化管道传感器数据集的处理算法。本文还介绍了一些属性,这些属性使用户可以了解传感器的数据集的不准确性。

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