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Related Concept Videos

Bar Graph01:07

Bar Graph

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Pie Chart01:04

Pie Chart

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A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
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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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Modified Boxplots00:57

Modified Boxplots

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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.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics.

Manasi Vartak1, Sajjadur Rahman2, Samuel Madden1

  • 1MIT.

Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases
|January 19, 2016
PubMed
Summary
This summary is machine-generated.

SeeDB is a new visualization recommendation engine that helps data analysts quickly find interesting trends in complex datasets. It uses novel optimizations to efficiently evaluate and recommend useful visualizations at interactive speeds.

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Area of Science:

  • Data analysis and visualization
  • Database systems
  • Human-computer interaction

Background:

  • Data analysts face challenges in identifying relevant visualizations for high-dimensional datasets.
  • Manual exploration of visualizations is time-consuming and laborious.
  • Existing systems lack efficient methods for recommending "interesting" visualizations.

Purpose of the Study:

  • To propose SeeDB, a visualization recommendation engine for fast visual analysis.
  • To address the challenges of scale and utility in visualization recommendation.
  • To facilitate the identification of useful or interesting visualizations in large datasets.

Main Methods:

  • SeeDB explores visualization spaces, evaluates trends, and recommends visualizations.
  • Introduces pruning and sharing optimizations to address scalability.
  • Adopts a deviation-based metric for assessing visualization utility.
  • Implements SeeDB as a middleware layer compatible with any DBMS.

Main Results:

  • SeeDB accurately identifies interesting visualizations.
  • Optimizations achieve multiple orders of magnitude speedup.
  • Recommendations are provided at interactive time scales.
  • User studies validate the utility metric and recommendation effectiveness.

Conclusions:

  • SeeDB significantly accelerates visual analysis for high-dimensional data.
  • The proposed optimizations effectively handle scale and utility challenges.
  • SeeDB enhances the capabilities of visual analytics tools through intelligent recommendations.