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相关概念视频

Interpreting Run Charts01:25

Interpreting Run Charts

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Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
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Detection of Gross Error: The Q Test01:00

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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What Are Outliers?01:12

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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
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Run Charts01:12

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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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High-Performance Liquid Chromatography: Types of Detectors01:15

High-Performance Liquid Chromatography: Types of Detectors

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The role of the detectors in High-Performance Liquid Chromatography (HPLC) is to analyze the solutes as they exit from the chromatographic column. The detector recognizes the solute's property and generates corresponding electrical signals, which are converted into a readable graph of the detector's response versus elution time called a chromatogram at the computer. There are several types of HPLC detectors, each with its own advantages and limitations, depending on the analyte...
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相关实验视频

Updated: Jul 10, 2025

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
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在云平台应用程序中检测性能异常.

Hiranya Jayathilaka1, Chandra Krintz1, Rich Wolski1

  • 1Computer Science Department, Univ. of California, Santa Barbara.

IEEE transactions on cloud computing
|November 20, 2023
PubMed
概括
此摘要是机器生成的。

根是一种新的云平台即服务 (PaaS) 系统,可以检测性能异常并识别瓶. 它可以在不需要更改代码的情况下监控应用程序,确定工作负载变化或PaaS服务问题的问题.

关键词:
云计算是一种云计算.性能异常检测检测性能异常检测根源原因分析分析.

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科学领域:

  • 云计算 云计算 云计算 云计算
  • 系统监控 系统监控
  • 绩效分析 绩效分析

背景情况:

  • 云平台即服务 (PaaS) 系统需要对性能进行强有力的监控.
  • 开发人员经常面临的负担是用于性能监控的仪器化应用程序代码.
  • 在复杂的PaaS环境中识别性能异常的根本原因是具有挑战性的.

研究的目的:

  • 在PaaS中引入Roots,这是一套用于性能异常检测和瓶识别的全系统.
  • 提供应用程序性能监控作为PaaS核心功能,消除了手动代码仪表的需要.
  • 在PaaS层之间对性能数据进行相关联,以追踪异常到特定的云平台组件.

主要方法:

  • 开发了Roots,这是一个跟踪HTTP/S请求和PaaS服务使用情况的系统.
  • 使用PaaS服务接口的轻量级监控.
  • 利用多种统计技术进行异常检测和根本原因分析 (工作量与瓶).

主要成果:

  • 根有效地检测性能异常,并识别PaaS系统中的瓶.
  • 该系统区分由工作负载变化引起的异常和由PaaS服务瓶引起的异常.
  • 跨 PaaS 层的数据相关性成功地将高层异常追溯到特定组件.

结论:

  • 根提供了一个全面的解决方案,用于性能监测和异常检测在PaaS云.
  • 该系统通过自动化代码仪表来显著减少开发人员的努力.
  • 根允许精确识别云平台基础架构中的性能瓶.