VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
© 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 t...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Association for Computing Machinery (ACM)
2021
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| Online Access: | https://hdl.handle.net/1721.1/132287 |
| _version_ | 1826201097804447744 |
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| author | Hu, Kevin Demiralp, Çağatay Gaikwad, Snehalkumar 'Neil' S Hulsebos, Madelon Bakker, Michiel A Zgraggen, Emanuel Hidalgo, César Kraska, Tim Li, Guoliang Satyanarayan, Arvind |
| author_facet | Hu, Kevin Demiralp, Çağatay Gaikwad, Snehalkumar 'Neil' S Hulsebos, Madelon Bakker, Michiel A Zgraggen, Emanuel Hidalgo, César Kraska, Tim Li, Guoliang Satyanarayan, Arvind |
| author_sort | Hu, Kevin |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T11:46:33Z |
| format | Article |
| id | mit-1721.1/132287 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:46:33Z |
| publishDate | 2021 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1322872021-09-21T03:39:58Z VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository Hu, Kevin Demiralp, Çağatay Gaikwad, Snehalkumar 'Neil' S Hulsebos, Madelon Bakker, Michiel A Zgraggen, Emanuel Hidalgo, César Kraska, Tim Li, Guoliang Satyanarayan, Arvind © 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. 2021-09-20T18:21:40Z 2021-09-20T18:21:40Z 2021-01-11T17:35:50Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/132287 en 10.1145/3290605.3300892 Conference on Human Factors in Computing Systems - Proceedings Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) MIT web domain |
| spellingShingle | Hu, Kevin Demiralp, Çağatay Gaikwad, Snehalkumar 'Neil' S Hulsebos, Madelon Bakker, Michiel A Zgraggen, Emanuel Hidalgo, César Kraska, Tim Li, Guoliang Satyanarayan, Arvind VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository |
| title | VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository |
| title_full | VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository |
| title_fullStr | VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository |
| title_full_unstemmed | VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository |
| title_short | VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository |
| title_sort | viznet towards a large scale visualization learning and benchmarking repository |
| url | https://hdl.handle.net/1721.1/132287 |
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