Get More Out of Your Data - Advanced Methods for Automated Data Integration, Cleaning and Augmentation

机译：充分利用数据-自动化数据集成，清理和扩充的高级方法

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Routine laboratory and on-line sensor data, operational settings, and data from targeted measurement campaigns are stored in different databases or files and are corrupted by missing values, and contain outliers. The application of Principal Components Analysis (PCA) and other methods to automated data processing is best supported with software tools that provide an ability to merge several data sources and to remove/replace poor and missing data. Automated data processing methods provide a means to make data processing more systematic and consistent (i.e. among many users or practitioners).

机译：常规实验室和在线传感器数据，操作设置以及来自目标测量活动的数据存储在不同的数据库或文件中，并因缺少值而损坏，并包含异常值。软件工具最好地支持将主成分分析（PCA）和其他方法应用于自动化数据处理，该软件工具具有合并多个数据源以及删除/替换不良和丢失的数据的功能。自动化的数据处理方法提供了一种使数据处理更加系统和一致（即在许多用户或从业人员中）的手段。

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《Annual Water Environment Federation technical exhibition and conference》|2019年|3266-3271|共6页
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Ivan Miletic; Leiv Rieger; Alex Rosenthal; Oliver Schraa;
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Data quality; data analysis; data to information; advanced process control;

机译：数据质量;数据分析;数据到信息;先进的过程控制;

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Get More Out of Your Data - Advanced Methods for Automated Data Integration, Cleaning and Augmentation

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