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Related Concept Videos

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
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If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
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A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Handling Data Skew in MapReduce Cluster by Using Partition Tuning.

Yufei Gao1, Yanjie Zhou2, Bing Zhou3

  • 1College of Information Science and Technology, Beijing Normal University, Beijing, China.

Journal of Healthcare Engineering
|October 26, 2017
PubMed
Summary
This summary is machine-generated.

The Partition Tuning-based Skew Handling (PTSH) algorithm efficiently addresses data skew in big data analytics. This method improves MapReduce job performance, particularly for healthcare data mining.

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Area of Science:

  • Computer Science
  • Data Science
  • Bioinformatics

Background:

  • Healthcare generates vast datasets requiring efficient analysis.
  • The MapReduce model is crucial for big data analytics but suffers from data skew.
  • Data skew significantly degrades the performance of big data processing.

Purpose of the Study:

  • To introduce and evaluate the Partition Tuning-based Skew Handling (PTSH) algorithm.
  • To address and mitigate the issue of data skew in MapReduce frameworks.
  • To enhance the efficiency of big data analytics in the healthcare sector.

Main Methods:

  • PTSH employs a two-stage partitioning strategy and partition tuning.
  • It disperses key-value pairs across virtual partitions.
  • Recombination of partitions is performed to handle data skew.

Main Results:

  • PTSH effectively handles data skew in MapReduce.
  • The algorithm demonstrates improved performance over native Hadoop, Closer, and LEEN.
  • Significant reduction in time for association rule mining (ARM) on healthcare data was observed.

Conclusions:

  • PTSH is a robust and efficient solution for data skew in MapReduce.
  • The algorithm offers substantial performance gains for big data analytics.
  • PTSH is particularly beneficial for association rule mining in healthcare datasets.