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Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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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.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Scatter Plot01:15

Scatter Plot

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The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
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Interpreting R Charts01:22

Interpreting R Charts

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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.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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Fast Fourier Transform01:10

Fast Fourier Transform

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The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
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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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Related Experiment Video

Updated: May 1, 2026

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FTSPlot: fast time series visualization for large datasets.

Michael Riss1

  • 1Department of ETSEIB, Technical University of Catalonia (UPC), Barcelona, Spain.

Plos One
|April 16, 2014
PubMed
Summary

FTSPlot visualizes large electrophysiology datasets rapidly using advanced computer graphics. This tool enables lag-free, interactive browsing of massive time series data, significantly improving analysis efficiency.

Area of Science:

  • Neuroscience
  • Computer Science
  • Data Visualization

Background:

  • Electrophysiological data analysis requires visual inspection of time series.
  • Large datasets from long-term experiments challenge conventional plotting software.
  • Efficient browsing of massive electrophysiological data is crucial for accurate analysis.

Purpose of the Study:

  • To introduce FTSPlot, a novel visualization concept for large-scale time series data.
  • To enable fast, interactive visualization of electrophysiological recordings.
  • To overcome limitations of conventional plotting software for massive datasets.

Main Methods:

  • Utilizes high-performance computer graphics techniques: hierarchic level of detail and out-of-core data handling.
  • Implements a preprocessing step to convert data into an optimized format for fast visualization.

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  • Achieves O(1) visualization complexity, independent of data size, after O(n x log(N)) preprocessing.

    • Main Results:

    • FTSPlot demonstrates lag-free visualization (< 20 ms) of large time series, event, and interval annotations.
    • The software supports massive datasets, with benchmarks conducted on 1 TiB (1.3 x 10^11 samples).
    • A demonstration prototype confirms the technology's capability for real-time data handling.

    Conclusions:

    • FTSPlot provides an efficient and scalable solution for visualizing large electrophysiological datasets.
    • The software's O(1) visualization complexity offers significant advantages over conventional methods.
    • FTSPlot is freely available as a Qt GUI component for integration into future research software.