Vis Reading Group
The goal of our visualization reading group is to supplement each member's own research with group discussion and brainstorming. In the past, we have published group projects that formed out of these discussions.
In 2012-13, the reading group is called IVRG (InfoVis Reading Group). The theme will be Big Data visualization and analytics. We will primarily touch on papers from the InfoVis, VAST, and HCI communities, though scivis papers are welcome, too. The link to our public website and schedule is available here.
Paper Queue
Add title/author/link below.
- Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines, Elmqvist et al., TVCG '10
Incremental, Approximate Database Queries and Uncertainty for Exploratory Visualization, Danyel Fisher, LDAV '11
- Parallel Visualization on Large Clusters Using MapReduce, Vo et al., LDAV '11
- Now Where Was I? Psychologically-Triggered Bookmarking, Pan et al., CHI '11
- Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design, Heer and Bostock, CHI '10
Abstracts
Extending the Slideshow InfoVis Paper
- We present work toward a new tool called pdf2ppt that uses a probabilistic generative model to create slideshow presentations from research papers. pdf2ppt generates the basic structure of a presentation so authors can spend less time on simple design tasks. We discuss the value of this tool in the context of information retargeting: redesigning information deliverables, like research articles, for different purposes or audiences. The generative model is trained using existing information about slideshow design conventions, which we found during an earlier ethnographic study. Finally, we describe findings from a user study that asked participants to make presentations with and without pdf2ppt. Participants found that producing slideshows with pdf2ppt was faster and made them more confident during the design process.--Steven Gomez 20:51, 10 September 2012 (EDT)
Surveying Visualization Use in a Domain
- We present a survey of visualization use and needs among graduate students in the biological and life sciences. The findings of our survey suggest a rough mapping between user-goals, like creating exploratory prototypes or production-quality graphics, and specific visualization methods and features. Commonly-used toolkits for visualization are evaluated with respect to their abilities to support these methods and features. Finally, we discuss holes in the design space for enduser-oriented visualization toolkits for the life sciences domain.--Steven Gomez 20:51, 10 September 2012 (EDT)
Crowdsourcing Interactive Visual Analysis for Brain Tractograms
- We present findings from an evaluation of interactive brain tractography analysis completed by online, crowdsourced workers. Crowdsourcing analysis can be used for evaluating visualizations without expert users, who can be difficult to recruit for traditional user studies. To the best of our knowledge, crowdsourcing has not been explored as a resource for completing interactive, scientific visualization tasks. Our goal is to validate the ability of workers to perform interactive analyses and contribute to crowdsourced evaluations, which can be executed quickly and with many users. We created a Web-based tractography visualization, defined a set of interactions and tasks, and recruited X workers from Amazon Mechanical Turk. We compared their results with the same tasks performed by a small set of brain experts. We found yadda ... yadda ... yadda.
Crowdsourcing Interactive Visual Analysis
- This is a more general version of the above. We present findings from an evaluation of crowdsourced interactive visualization tasks. (We systematically chose a set of tasks that embody a general set of unit interaction tasks.) These tasks were completed on (visualization X ... interactive versions of the Cleveland and McGill perception charts?).
Mining Online Visualizations
- We present a taxonomy of online visualizations created by publicly available visualization tools. We categorized x visualizations from [Many Eyes, name of other sources] by their subject area, data type and visualization type. We then discuss 1) the gap between people's data analysis needs and the availability of visualization techniques and 2) opportunities for visualization toolkits and/or methods that address the gap.--Hua Guo 11:51, 14 September 2012 (EDT)