ABSTRACT
Streamit lets users explore visualizations of text streams without prior knowledge of the data. It incorporates incoming documents from a continuous source into an existing visualization context with automatic grouping and separation based on document similarities. A powerful user interface allows in-depth data analysis.
Subject(s)
Computer Graphics , Cell Phone , Cluster Analysis , Computer Simulation , Internet , User-Computer InterfaceABSTRACT
Many text collections with temporal references, such as news corpora and weblogs, are generated to report and discuss real life events. Thus, event-related tasks, such as detecting real life events that drive the generation of the text documents, tracking event evolutions, and investigating reports and commentaries about events of interest, are important when exploring such text collections. To incorporate and leverage human efforts in conducting such tasks, we propose a novel visual analytics approach named EventRiver. EventRiver integrates event-based automated text analysis and visualization to reveal the events motivating the text generation and the long term stories they construct. On the visualization, users can interactively conduct tasks such as event browsing, tracking, association, and investigation. A working prototype of EventRiver has been implemented for exploring news corpora. A set of case studies, experiments, and a preliminary user test have been conducted to evaluate its effectiveness and efficiency.
