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Related Concept Videos

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

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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...
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Data Validation01:15

Data Validation

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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:
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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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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...
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Statgraphics01:10

Statgraphics

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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,...
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Run Charts01:12

Run Charts

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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...
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Balancing Protection and Quality in Big Data Analytics Pipelines.

Antongiacomo Polimeno1, Paolo Mignone2, Chiara Braghin1

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

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Summary
This summary is machine-generated.

This study introduces a new data engine that balances data protection and sharing for big data applications. It ensures data quality and security through integrated access control, maintaining model effectiveness.

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Area of Science:

  • Computer Science
  • Data Engineering
  • Information Security

Background:

  • Existing data engines struggle to balance data protection and sharing, hindering big data adoption.
  • Independent approaches to data governance and access control create conceptual and technological gaps.

Purpose of the Study:

  • To present a novel data engine architecture integrating governance and access control for big data analytics.
  • To address the conflict between data protection and sharing requirements within data pipelines.

Main Methods:

  • Developed a data engine with an integrated access control system enforcing data access based on transformations.
  • Implemented data sanitization to protect sensitive attributes before usage, balancing protection and quality.
  • Tested the solution in a smart city scenario using Oslo's transportation data.

Main Results:

  • Predictive models trained on data processed via secure transformations remained effective.
  • The data engine successfully balanced data protection with data quality and usability.
  • Demonstrated the feasibility of role-based access control in big data analytics.

Conclusions:

  • The proposed data engine architecture effectively resolves the conflict between data protection and sharing.
  • Integrated access control and data transformation enhance big data governance without compromising analytical performance.
  • The solution is viable for real-world applications, such as smart city data analysis.