Lei Peng , Ziyue Lin , Natalia Andrienko , Gennady Andrienko , Siming Chen
{"title":"Contextualized visual analytics for multivariate events","authors":"Lei Peng , Ziyue Lin , Natalia Andrienko , Gennady Andrienko , Siming Chen","doi":"10.1016/j.visinf.2025.100234","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"9 2","pages":"Article 100234"},"PeriodicalIF":3.8000,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Visual Informatics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468502X25000099","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
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.