Dataopsy: Scalable and Fluid Visual Exploration using Aggregate Query Sculpting
This addresses the challenge of interactive data visualization for users dealing with complex datasets, though it appears incremental as it builds on existing faceted query and visualization methods.
The paper tackles the problem of exploring large-scale multidimensional data by introducing aggregate query sculpting (AQS), a faceted visual query technique that starts with a single aggregated visual mark and allows progressive exploration through P6 operations, resulting in a scalable and fluid prototype called Dataopsy validated with case studies and examples.
We present aggregate query sculpting (AQS), a faceted visual query technique for large-scale multidimensional data. As a "born scalable" query technique, AQS starts visualization with a single visual mark representing an aggregation of the entire dataset. The user can then progressively explore the dataset through a sequence of operations abbreviated as P6: pivot (facet an aggregate based on an attribute), partition (lay out a facet in space), peek (see inside a subset using an aggregate visual representation), pile (merge two or more subsets), project (extracting a subset into a new substrate), and prune (discard an aggregate not currently of interest). We validate AQS with Dataopsy, a prototype implementation of AQS that has been designed for fluid interaction on desktop and touch-based mobile devices. We demonstrate AQS and Dataopsy using two case studies and three application examples.