Show Me: Automatic Presentation for Visual Analysis
By Jock D. Mackinlay, P. Hanrahan, Chris Stolte. IEEE Transactions on Visualization and Computer Graphics, 2007
566 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/a66b19219b522efe7aa2225acf8bf7828305fdd7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Jeffrey Heer
- Maneesh Agrawala
- Li Fei-Fei
- Manolis Savva
- Luc Van Gool
- Jitendra Malik
- Kristen Grauman
- Kaiming He
In Sociology and Political Science
- Jeffrey Heer
- Maneesh Agrawala
- Michael Bostock
- Jock D. Mackinlay
- K. G. Karahalios
- Zhicheng Liu
- Nicholas Kong
- Aditya G. Parameswaran
Citations in the corpus
- Voyager 2: Augmenting Visual Analysis with Partial View Specifications (2017, methodology): “To this aim, we extended PoleStar to support filtering as well as automatic mark and add to shelf features [15].”
- ReVision: automated classification, analysis and redesign of chart images (2011, methodology): “Alternatively, users can export the data to create their own charts using tools such as Microsoft Excel or Tableau [21].”
- Empirical Studies in Information Visualization: Seven Scenarios (2012, unknown): “[49] used computer logs to evaluate the visual effectiveness of a function inserted into Tableau to suggest users’ predefined visual configurations for the data at hand.”
- Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation (2008, methodology): “Tableaus automated presentation features (named ShowMe) [21] help users create more perceptually effective visualizations.”
- Effortless Data Exploration with zenvisage: An Expressive and Interactive Visual Analytics System (2016, methodology): “Perhaps not surprisingly, analysts use both visualization tools (Tableau, Excel, BI), programming languages (Python), statistical tools (SAS, SPSS), and relational databases (SQL) for data exploration.”
- SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics (2015, unknown): “There has been work such as [19, 20] on recommending visual encodings for different types of data.”
- Crowdsourcing graphical perception: using mechanical turk to assess visualization design (2010, methodology): “It also has influenced the design of automated presentation techniques [21, 22] and been successfully extended by others (e.”
- Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco (2018, methodology): “For example, existing automated design systems [37–39,65] do not explicitly reuse the knowledge bases implemented in previous systems.”
- DataTone: Managing Ambiguity in Natural Language Interfaces for Data Visualization (2015, methodology): “Systems such as Show Me [16] incorporate these guidelines to automatically suggest appropriate visual encodings to the users.”
- Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations (2016, unknown): “Tableau’s Show Me [25] introduces a set of heuristics to aid in the construction of small multiples and recommend chart types.”
- Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations (2009, background): “Establishing the effectiveness of visual encodings for graphical perception tasks is also vital for the design of effective visualizations and the development of automatic presentation software [16, 17].”