Principal elements for visualizing event sequence data applied to 3D cascading diagrams

Abstract

As we live in the knowledge information society where various kinds of data are explosively produced, importance of data type-specific visualization technique is being emphasized. The subject of this study is about the event sequence data which is to explain certain phenomena by occurrence of events and a sequence of events. While event sequence data are collected in many domains, there are no clear guidelines for which aspects should be taken as visual. We address that one of the critical elements for visualizing the event sequence data is to choosing a factor for aggregating the data. Then the frequency becomes an important information as well as the event paths to understand causality, and the final outcome of the events. In this paper, we describe main visual elements for better understanding of such data. Among seven factors identified, we highlight 'outer factor' for aggregating data, and arrange the data accordingly in 3D space. The visualization model is constructed as multi-layered cascading form. Taking advantage of 3D space, users can choose the viewing direction to reveal different aspects of information. We also present the results applied to real-world datasets to examine the effectiveness of the proposed visualization model.