Contextualized visual analytics for multivariate events

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.

Informationen zur Zitierung

Peng, Lei; Lin, Ziyue; Andrienko, Natalia; Andrienko, Gennady; Chen, Siming: Contextualized visual analytics for multivariate events, Visual Informatics, 2025, 9, 2, 100234, June, https://www.sciencedirect.com/science/article/pii/S2468502X25000099, Peng.etal.2025a,

Assoziierte Lamarr-ForscherInnen

lamarr institute person Andriyenko Gennadiy pi - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Gennady Andrienko

Principal Investigator Menschenzentrierte KI-Systeme zum Profil
lamarr institute person Andriyenko Nathaliya pi - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Natalia Andrienko

Area Chair Menschenzentrierte KI-Systeme zum Profil