- Home
- Search Results
- Page 1 of 1
Search for: All records
-
Total Resources2
- Resource Type
-
20000
- Availability
-
20
- Author / Contributor
- Filter by Author / Creator
-
-
Heer, Jeffrey (2)
-
Lin, Halden (2)
-
Moritz, Dominik (2)
-
Howe, Bill (1)
-
Nelson, Greg L. (1)
-
Smith, Adam M. (1)
-
Wang, Chenglong (1)
-
#Tyler Phillips, Kenneth E. (0)
-
#Willis, Ciara (0)
-
& Abreu-Ramos, E. D. (0)
-
& Abramson, C. I. (0)
-
& Abreu-Ramos, E. D. (0)
-
& Adams, S.G. (0)
-
& Ahmed, K. (0)
-
& Ahmed, Khadija. (0)
-
& Akcil-Okan, O. (0)
-
& Akuom, D. (0)
-
& Aleven, V. (0)
-
& Andrews-Larson, C. (0)
-
& Archibald, J. (0)
-
- Filter by Editor
-
-
& Spizer, S. M. (0)
-
& . Spizer, S. (0)
-
& Ahn, J. (0)
-
& Bateiha, S. (0)
-
& Bosch, N. (0)
-
& Brennan K. (0)
-
& Brennan, K. (0)
-
& Chen, B. (0)
-
& Chen, Bodong (0)
-
& Drown, S. (0)
-
& Ferretti, F. (0)
-
& Higgins, A. (0)
-
& J. Peters (0)
-
& Kali, Y. (0)
-
& Ruiz-Arias, P.M. (0)
-
& S. Spitzer (0)
-
& Spitzer, S. (0)
-
& Spitzer, S.M. (0)
-
(submitted - in Review for IEEE ICASSP-2024) (0)
-
- (0)
-
-
Have feedback or suggestions for a way to improve these results?
!
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
Visualization recommender systems attempt to automate design decisions spanning choices of selected data, transformations, and visual encodings. However, across invocations such recommenders may lack the context of prior results, producing unstable outputs that override earlier design choices. To better balance automated suggestions with user intent, we contribute Dziban, a visualization API that supports both ambiguous specification and a novel anchoring mechanism for conveying desired context. Dziban uses the Draco knowledge base to automatically complete partial specifications and suggest appropriate visualizations. In addition, it extends Draco with chart similarity logic, enabling recommendations that also remain perceptually similar to a provided “anchor” chart. Existing APIs for exploratory visualization, such as ggplot2 and Vega-Lite, require fully specified chart definitions. In contrast, Dziban provides a more concise and flexible authoring experience through automated design, while preserving predictability and control through anchored recommendations.more » « less
-
Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in DracoMoritz, Dominik ; Wang, Chenglong ; Nelson, Greg L. ; Lin, Halden ; Smith, Adam M. ; Howe, Bill ; Heer, Jeffrey ( , InfoVis)There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on Answer Set Programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments.more » « less