Visual Analysis of Cyclic Time Series with Semantic Zoom

Visual analysis (VA) tasks often involve exploring large and complex multi-dimensional datasets to identify trends and anomalies. However, the challenge lies in displaying all the data and maintaining the desired level of detail within the limited screen space. In this paper, we propose a solution that incorporates multiple visualizations and semantic zooming to address this compromise. Our visualization tool focuses on cycle-dependent data, showcasing time series with repetitive behavior. Through semantic zooming, cyclic time series data can be displayed in large quantities and high levels of detail without the need for multiple views. Our proposed tool includes three independent visualizations: line plots, horizon graphs, and adaptive heatmaps. By offering different visualization options, we aim to provide a rich and flexible analytical experience that response to the different user needs and encourages comprehensive data exploration. The tool accommodates both novice and expert users, allowing for intuitive analysis as well as advanced techniques for detailed examination. Our approach follows the mantra of 'overview first, zoom and filter, then details-on-demand' facilitating rapid detection and exploration of patterns and trends. In this paper, we present the detailed design, interaction capabilities with semantic zoom, and the results of a user study that demonstrate the effectiveness and usefulness of our proposed tool.