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Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

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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Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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Ogive Graph01:07

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Velocity and Position by Graphical Method

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Related Experiment Video

Updated: Jun 19, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

ActiviTree: interactive visual exploration of sequences in event-based data using graph similarity.

Katerina Vrotsou1, Jimmy Johansson, Matthew Cooper

  • 1Linköping University. katerina.vrotsou@itn.liu.se

IEEE Transactions on Visualization and Computer Graphics
|October 17, 2009
PubMed
Summary

This study introduces an interactive visual data mining approach for identifying significant sequences in large temporal datasets. The method enables efficient, user-centered exploration without data pre-processing, suitable for social science and other fields.

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Area of Science:

  • Data Mining
  • Information Visualization
  • Computational Social Science

Background:

  • Identifying significant sequences in large, complex event-based temporal data is challenging.
  • Traditional visual methods are limited to small datasets, while algorithmic approaches are computationally expensive and may miss infrequent patterns.
  • Existing methods often overlook important infrequent sequences and outliers due to computational constraints.

Purpose of the Study:

  • To introduce an interactive visual data mining approach for user-centered exploration of temporal data.
  • To facilitate the identification of significant sequences, including infrequent ones, within large datasets.
  • To provide an efficient and interactive experience for data analysis without requiring pre-processing.

Main Methods:

  • Adaptation of web searching techniques combined with an intuitive visual interface.
  • Development of a search algorithm with negligible execution time for large datasets.
  • Implementation of a user-centered interactive exploration process.

Main Results:

  • The developed approach allows for negligible-time search execution, even on large datasets.
  • No data pre-processing is required, enabling a fully interactive user experience.
  • The technique effectively supports the identification of significant sequences tailored to user needs.

Conclusions:

  • The interactive visual data mining approach offers an efficient solution for analyzing large temporal event data.
  • The method is applicable across various disciplines, with a demonstrated use in social science diary data.
  • This technique enhances the discovery of meaningful patterns, including infrequent sequences, in complex datasets.