Towards Better Pattern Enhancement in Temporal Evolving Set Visualization

Abstract

Temporal evolving set data are time-varying and growing ubiquitous in person re-identification, parameter choice, and streaming data analysis. We construct a workflow to analyze and explore the inconspicuous pattern between multiple nonadjacent sets in temporal evolving set data. We propose a progressive timeline layout algorithm based on a mathematical optimization model to place the set element after data update, our layout algorithm can calculate the coordinates of elements in a short time and preserve the distance ratio between elements. To relax the visual clutter when visualizing sets’ relationships, we design two types of pattern enhancement strategies and their combinations: optimization-based pattern enhancement strategy and design-based pattern enhancement strategy. We conduct a comprehensive evaluation to verify and compare our pattern enhancement strategies including a quantitative experiment, two case studies, and an informal user study. The results show that our pattern enhancement strategies can effectively help users identify inconspicuous patterns. Our workflow and strategies show broad application prospects and we hope it could be a fundamental component in data projection pattern mining and streaming data analysis.