VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
Name
2019-VizNet-CHI.pdf
Description
Accepted version
Size
6.96 MB
Format
Unknown
Checksum (MD5)
66a93612acffc1f3601f14c1c7c531e0
Author(s)
Hu, Kevin
Gaikwad, Snehalkumar "Neil" S.
Hulsebos, Madelon
Bakker, Michiel A
Zgraggen, Emanuel
Hidalgo, Cesar Augusto
Kraska, Tim
Li, Guoliang
Satyanarayan, Arvind
Demiralp, Cagatay
Date Issued
2019
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.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3290605.3300892
