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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
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In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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FUn: a framework for interactive visualizations of large, high-dimensional datasets on the web.

Daniel Probst1, Jean-Louis Reymond1

  • 1Department of Chemistry and Biochemistry, National Center for Competence in Research NCCR TransCure, University of Berne, 3012 Berne, Switzerland.

Bioinformatics (Oxford, England)
|November 30, 2017
PubMed
Summary

This study introduces FUn, a framework for interactive 3D web-based visualization of big data, enabling detailed inspection of millions of data points. It addresses limitations in current software for handling large scientific datasets.

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Area of Science:

  • Scientific Visualization
  • Computational Chemistry
  • Bioinformatics

Background:

  • Big data analytics are crucial in science, but current tools struggle with interactive visualization of large datasets (>100,000 points) on the web.
  • Existing solutions often rely on data reduction or statistical summaries, preventing detailed, record-level visual inspection.
  • Advancements in GPU hardware now enable rendering millions of data points on consumer devices, creating an opportunity for enhanced data visualization.

Purpose of the Study:

  • To present FUn, a novel framework for creating interactive, web-based 3D visualizations of large datasets.
  • To enable detailed, record-level visual inspection of big data, overcoming limitations of current scientific software.
  • To leverage recent hardware advancements for rendering millions of data points interactively.

Main Methods:

  • Development of FUn, a framework comprising a client module (Faerun) and a server module (Underdark).
  • Implementation of a reference system providing access to the SureChEMBL database, containing over 17 million chemical compounds.
  • Utilizing advancements in graphical processing units (GPUs) for rendering large-scale datasets.

Main Results:

  • FUn facilitates the creation of interactive 3D web-based visualizations for large datasets.
  • The framework enables visual inspection at the record level, a capability lacking in many current tools.
  • A reference implementation demonstrates FUn's utility with a large chemical compound database.

Conclusions:

  • FUn offers a powerful solution for visualizing and interacting with big data on the web.
  • The framework overcomes previous limitations in handling large datasets, enabling detailed visual analysis.
  • FUn has the potential to significantly impact various scientific fields by improving data exploration capabilities.