Sara L. Su

Understanding Visualization by Understanding Individual Users

Visualization is often seen as a tool to support complex thinking. Although different people can have very different ways of approaching the kind of complex task that visualizations support, as researchers and designers we still rarely consider individual differences in creating and evaluating visualizations. This article reviews recent research on individual differences in visualization and human-computer interaction, showing that both cognitive abilities and personality profiles might significantly affect performance with these tools. The study of individual differences has led to the conclusion that advances in this important area in visualization will require more focused research. Specifically, we must isolate the cognitive factors that are relevant to visualization and the design factors that make one visualization more suited to a user than another. In doing so, we could increase our understanding of the visualization user and reshape how we approach design and evaluation.

Heapviz: Interactive heap visualization for program understanding and debugging

Understanding the data structures in a program is crucial to understanding how the program works, or why it does not work. Inspecting the code that implements the data structures, however, is an arduous task and often fails to yield insights into the global organization of a program’s data. Inspecting the actual contents of the heap solves these problems but presents a significant challenge of its own: finding an effective way to present the enormous number of objects it contains. In this paper we present Heapviz, a tool for visualizing and exploring snapshots of the heap obtained from a running Java program. Unlike existing tools, such as traditional debuggers, Heapviz presents a global view of the program state as a graph, together with powerful interactive capabilities for navigating it. Our tool employs several key techniques that help manage the scale of the data. First, we reduce the size and complexity of the graph by using algorithms inspired by static shape analysis to aggregate the nodes that make up a data structure. Second, we implement a powerful visualization component whose interactive interface provides extensive support for exploring the graph. The user can search for objects based on type, connectivity, and field values; group objects; and color or hide and show each group. The user may also inspect individual objects to see their field values and neighbors in the graph. These interactive abilities help the user manage the complexity of these huge graphs. By applying Heapviz to both constructed and real-world examples, we show that it provides programmers with a powerful and intuitive tool for exploring program behavior.

Dead-End Elimination as a Heuristic for Min-Cut Image Segmentation

We apply the dead-end elimination (DEE) strategy from protein design as a heuristic for the max-flow/min-cut formulation of the image segmentation problem. DEE combines aspects of constraint propagation and branch-and-bound to eliminate solutions incompatible with global optimization of the objective function. Though DEE can be used for segmentation into an arbitrary number of regions, in this paper we evaluate only the case of binary segmentation. We provide a runtime analysis and evaluation of DEE applied to two min-cut algorithms. Preliminary results show that DEE consistently reduces the search space for the Edmonds-Karp algorithm; tuning DEE as a heuristic for Boykov-Kolmogorov and other algorithms is future work.