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相关概念视频

Quality Assurance01:19

Quality Assurance

124
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
124
Quality Control01:05

Quality Control

162
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
162
Data Validation01:15

Data Validation

161
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
161
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

107
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
107
Statgraphics01:10

Statgraphics

126
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
126
Run Charts01:12

Run Charts

59
Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For...
59

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相关实验视频

Updated: Jun 28, 2025

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

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在大数据分析管道中平衡保护和质量

Antongiacomo Polimeno1, Paolo Mignone2, Chiara Braghin1

  • 1Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy.

Big data
|April 11, 2024
PubMed
概括

这项研究引入了一个新的数据引擎,为大数据应用程序平衡数据保护和共享. 它通过集成的访问控制来确保数据质量和安全性,保持模型的有效性.

关键词:
访问控制 访问控制 访问控制检测异常检测异常检测大数据就是大数据.数据治理数据治理数据保护数据的保护.

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

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科学领域:

  • 计算机科学 计算机科学
  • 数据工程数据工程
  • 信息安全 信息安全

背景情况:

  • 现有的数据引擎难以平衡数据保护和共享,阻碍了大数据的采用.
  • 对数据治理和访问控制的独立方法会造成概念和技术上的差距.

研究的目的:

  • 介绍一个新的数据引擎架构,整合大数据分析的治理和访问控制.
  • 解决数据管道内数据保护和共享要求之间的冲突.

主要方法:

  • 开发了一个数据引擎,配备了一个集成的访问控制系统,基于转换来强制执行数据访问.
  • 实施数据消毒,在使用前保护敏感属性,平衡保护和质量.
  • 在智能城市场景中使用奥斯陆的交通数据测试了该解决方案.

主要成果:

  • 通过安全转换处理的数据进行训练的预测模型仍然有效.
  • 数据引擎成功地将数据保护与数据质量和可用性相平衡.
  • 在大数据分析中展示了基于角色的访问控制的可行性.

结论:

  • 拟议的数据引擎架构有效地解决了数据保护和共享之间的冲突.
  • 综合访问控制和数据转换可以提高大数据治理,而不会影响分析性能.
  • 该解决方案适用于现实世界的应用,例如智能城市数据分析.