Dataopsy: Scalable and Fluid Visual Exploration using Aggregate Query Sculpting

TL;DR

Dataopsy introduces aggregate query sculpting (AQS), a scalable visual exploration technique for large multidimensional datasets, enabling users to interactively refine visualizations through a sequence of intuitive operations.

Contribution

The paper presents AQS, a novel scalable visual query method for large data, with a prototype implementation called Dataopsy supporting fluid interaction on various devices.

Findings

AQS enables efficient exploration of large datasets through intuitive operations.

Dataopsy demonstrates practical usability on desktop and mobile devices.

Case studies validate the effectiveness of the approach.

Abstract

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.