Temporal Treemaps for Visualizing Time Series Data

Treemap is an interactive graphical technique for visualizing large hierarchical information spaces using nested rectangles in a space filling manner. The size and color of the rectangles show data attributes and enable users to spot trends, patterns or exceptions. Current implementations of treemaps help explore time-invariant data. However, many real-world applications require monitoring hierarchical, time-variant data. This thesis extends treemaps to interactively explore time series data by mapping temporal changes to color attribute of treemaps. Specific contributions of this thesis include: · Temporal treemaps for exploring time series data through visualizing absolute or relative changes, animating them over time, filtering data items, and discovering trends using time series graphs. · The design and implementation of extensible software modules based on systems engineering methodologies and object-oriented approach. · Validation through five case studies: health statistics, web logs, production data, birth statistics, and help-desk tickets; future improvements identified from the user feedback

Extending the utility of treemaps with flexible hierarchy

Treemaps are a visualization technique for presenting hierarchical information on two-dimensional displays. Prior implementations limit the visualization to pre-defined static hierarchies. Flexible hierarchy, a new capability of Treemap 4.0, enables users to define various hierarchies through dynamically selecting a series of data attributes so that they can discover patterns, clusters and outliers. This paper describes the design and implementation issues of flexible hierarchy. It then reports on a usability study, which led to enhancements to the interface

Vortex analysis of intra-aneurismal flow in cerebral aneurysms

© 2016 Kevin Sunderland et al. This study aims to develop an alternative vortex analysis method by measuring structure ofIntracranial aneurysm (IA) flow vortexes across the cardiac cycle, to quantify temporal stability of aneurismal flow. Hemodynamics were modeled in patient-specific geometries, using computational fluid dynamics (CFD) simulations. Modified versions of known 2 and Q-criterion methods identified vortex regions; then regions were segmented out using the classical marching cube algorithm. Temporal stability was measured by the degree of vortex overlap (DVO) at each step of a cardiac cycle against a cycle-averaged vortex and by the change in number of cores over the cycle. No statistical differences exist in DVO or number of vortex cores between 5 terminal IAs and 5 sidewall IAs. No strong correlation exists between vortex core characteristics and geometric or hemodynamic characteristics of IAs. Statistical independence suggests this proposed method may provide novel IA information. However, threshold values used to determine the vortex core regions and resolution of velocity data influenced analysis outcomes and have to be addressed in future studies. In conclusions, preliminary results show that the proposed methodology may help give novel insight toward aneurismal flow characteristic and help in future risk assessment given more developments