Work

Contextualized visual analytics for multivariate events

Year: 2025

Type: article

Abstract: For event analysis, the information from both before and after the event can be crucial in certain scenarios. By incorporating a contextualized perspective in event analysis, analysts can gain deeper insights from the events. We propose a contextualized visual analysis framework which enables the identification and interpretation of temporal patterns within and across multivariate events. The framework consists of a design of visual representation for multivariate event contexts, a data processing workflow to support the visualization, and a context-centered visual analysis system to facilitate the interactive exploration of temporal patterns. To demonstrate the applicability and effectiveness of our framework, we present case studies using real-world datasets from two different domains and an expert study conducted with experienced data analysts.

Source: Visual Informatics

DOI: 10.1016/j.visinf.2025.100234

Authors: Lei Peng, Ziyue Lin, Natalia Andrienko, Gennady Andrienko, Siming Chen

Institutions: Fudan University, City, University of London, Fraunhofer Institute for Intelligent Analysis and Information Systems

Language: English

Topic: Data Visualization and Analytics

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: Xi'an Eurasia University, National Natural Science Foundation of China, Bundesministerium für Bildung und Forschung

Awards: G1227141104, G3928477400, G7334803100, G7718724753