Scientific measurements are often depicted as line graphs. Stateof-the-art high throughput systems in life sciences, telemetry and electronics measurement rapidly generate hundreds to thousands of such graphs. Despite the increasing volume and ubiquity of such data, few software systems provide efficient interactive management, navigation and exploratory analysis of large line graph collections. To address these issues, we have developed Line Graph Explorer (LGE). LGE is a novel and visually scalable line graph management system that supports facile navigation and interactive visual analysis. LGE provides a compact overview of the entire collection by encoding the y-dimension of individual line graphs with color instead of space, thus enabling the analyst to see major common features and alignments of the data. Using Focus+Context techniques, LGE provides interactions for viewing selected compressed graphs in detail as standard line graphs without losing a sense of the general pattern and major features of the collection. To further enhance visualization and pattern discovery, LGE provides sorting and clustering of line graphs based on similarity of selected graph features. Sequential sorting by associated line graph metadata is also supported. We illustrate the features and use of LGE with examples from meteorology and biology.

Large-scale session log analysis typically includes statistical methods and detailed log examinations. While both methods have merits, statistical methods can miss previously unknown subpopulations in the data and detailed analyses may have selection biases. We therefore built Session Viewer, a visualization tool to facilitate and bridge between statistical and detailed analyses. Taking a multiple-coordinated view approach, Session Viewer shows multiple session populations at the Aggregate, Multiple, and Detail data levels to support different analysis styles. To bridge between the statistical and the detailed analysis levels, Session Viewer provides fluid traversal between data levels and side-by-side comparison at all data levels. We describe an analysis of a large-scale web usage study to demonstrate the use of Session Viewer, where we quantified the importance of grouping sessions based on task type.