While the assessment of using motion in visualizations has been polarized, we conjecture that motion may be more effective in comparative visualizations if applied properly, especially when dealing with large amounts of multi-dimensional data. We have designed visualizations to represent driver behaviors. A series of user studies have been conducted to verify if adding motion to the static visualization can help users make comparisons and separate drastically different behaviors. Results show that adding motion indeed leads to shorter completion time and less cognitive workload.

We share our challenges and lessons learned in designing our exhibit prototype that encourages museum visitors to learn about marine animal behaviors through interactive visualization and data exploration. Our intent is to have visitors draw comparisons between animal behaviors, similarly to how scientists would, to make insights and discoveries. In our efforts, we have designed a set of visual encodings around the Tagging of Pelagic Predator (TOPP) data set to create the appropriate abstractions of this rich and complex field data. We have incorporated Multiple External Representations (MERs) and tangible user interfaces (TUIs) to provide a complementary representation of the data and promote self-learning. Through the formative evaluation, we can identify a few strengths and weaknesses of our prototype design. Our evaluation results suggest that we are progressing in the right direction - we observed the public making some comparisons and inferences - but still require further design iterations to improve our visualization exhibit.

This paper presents a novel framework for applying storyline visualizations to streaming data. The framework includes three components: a new data management scheme for processing and storing the incoming data, a layout construction algorithm specifically designed for incrementally generating storylines from streaming data, and a layout refinement algorithm for improving the legibility of the visualization. By dividing the layout computation to two separate components, one for constructing and another for refining, our framework effectively provides the users with the ability to follow and reason dynamic data. The evaluation studies of our storyline visualization framework demonstrate its efficacy to present streaming data as well as its superior performance over existing methods in terms of both computational efficiency and visual clarity.

Regression testing is an indispensable process in software development, which ensures stable features have not been adversely broken by new changes. When GUI plays an important role in an application, a popular choice to automate the tests is applying GUI Capture/Replay tools. Unfortunately, in many applications which render images as output, the correctness of a replay run can no longer be straightforwardly verified. In this paper, we propose a test automation solution, called Intrusive Test Automation, which uses program instrumentation technique to collect the runtime internal information of a program. As a result, the correctness of a test run can be verified by the runtime traces. In addition, when large number of failed test cases are reported by the test automation system, recommending some representative test cases as a start for debugging can be helpful to programmers. This paper proposes a clustering technique based on the information collected from the instrumented code. In principle, fixing bugs in one representative test case can fix its related failed test cases as well. A case study is presented to demonstrate the effectiveness of the approach.