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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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Related Experiment Video

Updated: Jun 28, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Visualizing temporal patterns in large multivariate data using textual pattern matching.

Markus Glatter1, Jian Huang, Sean Ahern

  • 1The University of Tennessee at Knoxville, TN, USA. glatter@cs.utk.edu

IEEE Transactions on Visualization and Computer Graphics
|November 8, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new textual pattern matching method for visualizing temporal patterns in large scientific datasets. This approach enables efficient, scalable, and concept-driven exploration of complex time-varying data.

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Area of Science:

  • Data Visualization
  • Scientific Computing
  • Information Visualization

Background:

  • Extracting and visualizing temporal patterns in large scientific datasets remains a significant challenge.
  • Existing methods lack flexibility in defining general temporal patterns and scalability for large, time-dependent data.
  • Current solutions are often not widely available or generalizable for complex simulations.

Purpose of the Study:

  • To develop a novel approach for specifying and identifying general temporal patterns in large scientific data.
  • To address the limitations in current methods for defining and visualizing temporal patterns.
  • To enable concept-driven exploration of large-scale time-varying multivariate data.

Main Methods:

  • Developed a textual pattern matching approach for defining and identifying temporal patterns.
  • Created a formal language for specifying these patterns.
  • Implemented an efficient and scalable solution for handling large datasets.

Main Results:

  • Demonstrated a flexible and concise method for defining general temporal patterns.
  • Provided a working implementation capable of handling large-scale simulation data.
  • Enabled concept-driven exploration of time-varying multivariate data across multiple application domains.

Conclusions:

  • The developed textual pattern matching approach offers a scalable and efficient solution for visualizing temporal patterns in large scientific data.
  • This method empowers researchers to explore complex, time-varying datasets more effectively.
  • It represents a significant advancement in the field of scientific data visualization.