The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations
Abstract
General visualization tools typically require manual specification of views: analysts must select data variables and then choose which transformations and visual encodings to apply. These decisions often involve both domain and visualization design expertise, and may impose a tedious specification process that impedes exploration. In this paper, we seek to complement manual chart construction with interactive navigation of a gallery of automatically-generated visualizations. We contribute Voyager, a mixed-initiative system that supports faceted browsing of recommended charts chosen according to statistical and perceptual measures. We describe Voyager's architecture, motivating design principles, and methods for generating and interacting with visualization recommendations. In a study comparing Voyager to a manual visualization specification tool, we find that Voyager facilitates exploration of previously unseen data and leads to increased data variable coverage. We then distill design implications for visualization tools, in particular the need to balance rapid exploration and targeted question-answering.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447
Random effects structure for confirmatory hypothesis testing: Keep it maximal
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013) · Journal of Memory and Language · doi:10.1016/j.jml.2012.11.001 · Cited by 10,328
On the Theory of Scales of Measurement
Stevens, S. S. (1946) · Science · doi:10.1126/science.103.2684.677 · Cited by 4,761 · cited 1×
“Vega-lite supports nominal, ordinal, quantitative, and temporal data types [34].”
Cleveland, W. S., & McGill, R. (1984) · Journal of the American Statistical Association · doi:10.2307/2288400 · Cited by 1,796
Exploratory search: from finding to understanding
Marchionini, G. (2006) · Communications of the ACM · doi:10.1145/1121949.1121979 · Cited by 1,506
Protovis: A Graphical Toolkit for Visualization
Bostock, M., & Heer, J. (2009) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2009.174 · Cited by 321 · cited 1×
“Visualization toolkits (e.g., [5] [6]) and design tools (e.g., [30] [32]) enable intricate designs but require detailed specification, hindering rapid exploration.”
Self-citation
Cited by
Vega-Lite: A Grammar of Interactive Graphics
Satyanarayan, A., Moritz, D., Wongsuphasawat, K., & Heer, J. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2599030 · Cited by 883
Self-citationAgency plus automation: Designing artificial intelligence into interactive systems
Heer, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1807184115 · Cited by 279
Self-citationTask-based effectiveness of basic visualizations
Saket, B., Endert, A., & Demiralp, C. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2829750 · Cited by 178
Understanding and Visualizing Data Iteration in Machine Learning
Hohman, F., Wongsuphasawat, K., Kery, M. B., & Patel, K. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376177 · Cited by 110
Self-citationInteractive Intent Modeling for Exploratory Search
Ruotsalo, T., Peltonen, J., Eugster, M. J. A., Głowacka, D., Floréen, P., Myllymäki, P., Jacucci, G., & Kaski, S. (2018) · ACM Transactions on Information Systems · doi:10.1145/3231593 · Cited by 57
MetaWriter: Exploring the Potential and Perils of AI Writing Support in Scientific Peer Review
Sun, L., Tao, S., Hu, J., & Dow, S. P. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637371 · Cited by 37
Metadata
- Authors
- Wongsuphasawat, K., Moritz, D., Anand, A., Mackinlay, J., Howe, B., & Heer, J.
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2016-01-31
- Volume
- 22
- Issue
- 1
- Pages
- 649-658
- ISSN
- 1077-2626 (ISSN-L)
- OpenAlex
- W1961845056
- Cited by
- 507
- References
- 6
- FWCI
- 31.19
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.