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VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
Name
2019-VizNet-CHI.pdf
Description
Accepted version
Size
6.96 MB
Format
Adobe PDF
Checksum (MD5)
66a93612acffc1f3601f14c1c7c531e0
Author(s)
Hu, Kevin
Demiralp, Çağatay
Gaikwad, Snehalkumar 'Neil' S
Hulsebos, Madelon
Bakker, Michiel A
Zgraggen, Emanuel
Hidalgo, César
Kraska, Tim
Li, Guoliang
Satyanarayan, Arvind
Journal
Conference on Human Factors in Computing Systems - Proceedings
Publisher
Association for Computing Machinery (ACM)
Version
Author's final manuscript
Abstract
© 2019 Copyright held by the owner/author(s). Researchers currently rely on ad hoc datasets to train automated visualization tools and evaluate the efectiveness of visualization designs. These exemplars often lack the characteristics of real-world datasets, and their one-of nature makes it difcult to compare diferent techniques. In this paper, we present VizNet: a large-scale corpus of over 31 million datasets compiled from open data repositories and online visualization galleries. On average, these datasets comprise 17 records over 3 dimensions and across the corpus, we fnd 51% of the dimensions record categorical data, 44% quantitative, and only 5% temporal. VizNet provides the necessary common baseline for comparing visualization design techniques, and developing benchmark models and algorithms for automating visual analysis. To demonstrate VizNet’s utility as a platform for conducting online crowdsourced experiments at scale, we replicate a prior study assessing the infuence of user task and data distribution on visual encoding efectiveness, and extend it by considering an additional task: outlier detection. To contend with running such studies at scale, we demonstrate how a metric of perceptual efectiveness can be learned from experimental results, and show its predictive power across test datasets.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1145/3290605.3300892
