In visual exploration and analysis of data, determining how to select and transform the data for visualization is a challenge for data-unfamiliar or inexperienced users. Our main hypothesis is that for many data sets and common analysis tasks, there are relatively few "data slices" that result in effective visualizations. By focusing human users on appropriate and suitably transformed parts of the underlying data sets, these data slices can help the users carry their task to correct completion. To verify this hypothesis, we develop a framework that permits us to capture exemplary data slices for a user task, and to explore and parse visual-exploration sequences into a format that makes them distinct and easy to compare. We develop a recommendation system, DataSlicer, that matches a "currently viewed" data slice with the most promising "next effective" data slices for the given exploration task. We report the results of controlled experiments with an implementation of the DataSlicer system, using four common analytical task types. The experiments demonstrate statistically significant improvements in accuracy and exploration speed versus users without access to our system.
Finally, a good example of a collaborative tool for visualizing data is AstroShelf @cite_17 . This tool is specifically tailored for astrophysicists and, unlike ours, aims more at facilitating collaborations than recommending visualizations.
@cite_17
2058442663
This demo presents AstroShelf, our on-going effort to enable astrophysicists to collaboratively investigate celestial objects using data originating from multiple sky surveys, hosted at different sites. The AstroShelf platform combines database and data stream, workflow and visualization technologies to provide a means for querying and displaying telescope images (in a Google Sky manner), visualizations of spectrum data, and for managing annotations. In addition to the user interface, AstroShelf supports a programmatic interface (available as a web service), which allows astrophysicists to incorporate functionality from AstroShelf in their own programs. A key feature is Live Annotations which is the detection and delivery of events or annotations to users in real-time, based on their profiles. We demonstrate the capabilities of AstroShelf through real end-user exploration scenarios (with participation from "stargazers" in the audience), in the presence of simulated annotation workloads executed through web services.