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 ex...

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Bibliographic Details
Published inarXiv.org
Main Authors Hoque, Md Naimul, Elmqvist, Niklas
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 05.08.2023
Subjects
Datasets
Multidimensional data
Queries
Substrates
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Summary: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.
ISSN:2331-8422