The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Visual comparison for information visualization
Open accessAbstract
Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. Increasingly, information visualization tools support such comparisons explicitly, beyond simply allowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we see that all designs are assembled from the building blocks of juxtaposition, superposition and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a general understanding of comparative visualization and facilitating the development of more comparative visualization tools.
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Cites
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Cited by
SmileyCluster: supporting accessible machine learning in K-12 scientific discovery
Wan, X., Zhou, X., Ye, Z., Mortensen, C. K., & Bai, Z. (2020) · Proceedings of the Interaction Design and Children Conference · doi:10.1145/3392063.3394440 · Cited by 77
Bernard, J., Sessler, D., Kohlhammer, J., & Ruddle, R. A. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2803829 · Cited by 55
FAIRVIS: Visual Analytics for Discovering Intersectional Bias in Machine Learning
Cabrera, Á. A., Epperson, W., Hohman, F., Kahng, M., Morgenstern, J., & Chau, D. H. (2019) · 2019 IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast47406.2019.8986948 · Cited by 14
Metadata
- Authors
- Gleicher, M., Albers, D., Walker, R., Jusufi, I., Hansen, C. D., & Roberts, J. C.
- Venue
- Information Visualization
- Publisher
- SAGE Publications
- Published
- 2011-09-07
- Volume
- 10
- Issue
- 4
- Pages
- 289-309
- ISSN
- 1473-8716 (ISSN-L)
- 1473-8724
- OpenAlex
- W2030246490
- Cited by
- 567
- References
- 95
- FWCI
- 18.29
- Type
- journal article
- Language
- en
- Open access
- green
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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.
