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Updated: Dec 24, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Temporal event searches based on event maps and relationships.

Yi Cai1, Haoran Xie2, Raymond Y K Lau3

  • 1School of Software Engineering, South China University of Technology, China.

Applied Soft Computing
|April 15, 2020
PubMed
Summary

This study introduces a new framework for temporal event searches, organizing results into a temporal event map (TEM) to show event relationships. This method enhances understanding of event evolution and identifies key component events more effectively.

Keywords:
Event relationEvent searchTemporal event mapWeb mining

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Area of Science:

  • Information Retrieval
  • Computer Science
  • Data Science

Background:

  • Effective event understanding requires comprehensive analysis of temporal event data.
  • Existing methods for temporal event search often lack a holistic view of event evolution and interdependencies.

Purpose of the Study:

  • To formalize temporal event searches and develop a framework for analyzing event relationships.
  • To organize search results into a Temporal Event Map (TEM) for a clear overview of event evolution.
  • To propose a method for measuring event importance and discovering key component events.

Main Methods:

  • Defined three event relationships: temporal, content dependence, and event reference.
  • Developed a Temporal Event Map (TEM) to visualize event evolution and dependencies.
  • Proposed an importance measurement method and algebraic operators for TEM analysis.

Main Results:

  • The proposed framework outperforms the baseline Event Evolution Graph (EEG) method.
  • The TEM effectively visualizes event dependencies and evolution.
  • Identified new event relationships missed by prior methods and human annotators.

Conclusions:

  • The framework provides a superior method for temporal event searches and understanding event evolution.
  • The TEM offers a comprehensive visualization of event dependencies, aiding in the discovery of critical information.
  • This approach enhances the ability to effectively and efficiently satisfy user needs for understanding complex events.