Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations
By Kanit Wongsuphasawat, Dominik Moritz, Anushka Anand, Jock D. Mackinlay, Bill Howe, Jeffrey Heer. IEEE Transactions on Visualization and Computer Graphics, 2016
588 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/2b90cf70c77d91cf3d736c609ea2e43a614fc4b5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Xavier Serra
- Xavier Favory
- Jeffrey Heer
- Frederic Font
- Felix Ouk
- Aditya G. Parameswaran
- Bill Howe
- Dominik Moritz
In Computer Vision and Pattern Recognition
- Jeffrey Heer
- Xavier Serra
- Dominik Moritz
- Xavier Favory
- Frederic Font
- Bill Howe
- Kanit Wongsuphasawat
- Jock D. Mackinlay
Citations in the corpus
- Vega-Lite: A Grammar of Interactive Graphics (2018, background): “In prior work, Wongsuphasawat et al. [30] introduced the simplest Vega-Lite speci-fication—here referred to as a unit specification—that defines a single Cartesian plot with a specific mark type to encode data (e.g., bars, lines, plotting symbols).”
- Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco (2018, methodology): “While traditional systems rely on carefully designed rules and defaults, more recent systems like Voyager’s CompassQL [65–67] use hand-tuned scores to specify fine-grained criteria such as space efficiency and legibility based on encoding and data properties.”
- Effortless Data Exploration with zenvisage: An Expressive and Interactive Visual Analytics System (2016, methodology): “It provides a GUI to compose ZQL queries, and displays the resulting visualizations using Vega-lite [17].”
- Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization (2016, methodology): “Statistical packages (ggvis [18]), data exploration tools (Voyager [40]), computational notebooks (iPython [21]), and graphical design tools (Lyra [31]) use earlier versions of Vega to automatically construct visualizations as part of the data analysis process, or to facilitate visualization prototy”
- Search Result Clustering in Collaborative Sound Collections (2020, methodology): “Signal processing approaches and machine learning techniques have been employed for organising and visualising large amounts of audio content [13, 27, 40, 43].”
- Controlling False Discoveries During Interactive Data Exploration (2016, unknown): “Finally, in the context of data exploration there has been recent work on automatically recommending visualization [36, 23, 37] or correlations [8].”
- Voyager 2: Augmenting Visual Analysis with Partial View Specifications (2017, methodology): “The earlier Voyager system [39] presents a gallery of recommended charts to facilitate breadth-oriented exploration.”