Dataopsy: Scalable and Fluid Visual Exploration using Aggregate Query Sculpting

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
$\mathbb{P}^{6}$
:
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 D
ataopsy
, 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.