Title: DataSlicer: Task-Based Data Selection for Visual Data Exploration,
Abstract: 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.