Polaris: a system for query, analysis and visualization of multi-dimensional relational databases

By Chris Stolte, Diane Tang, P. Hanrahan. IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings, 2000

831 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1386888295419e1141a766fceb9c77c13d6d1a76

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Citations in the corpus

Profiler: integrated statistical analysis and visualization for data quality assessment (2012, background): “Visual analytic tools such as Tableau [31], GGobi [32], and Improvise [36] enable analysts to construct multi-dimensional views of data.”
Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips (2013, background): “Another notable effort in this direction is the Polaris system [33] and its successor, Tableau.”
Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey (2013, neutral): “The integration of abstract data from multiple sources is more common in information visualization (InfoVis), for example, when visualizing relational databases [8], [9], or web data [10].”
Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization (2016, background): “3 BACKGROUND: DECLARATIVE VISUALIZATION DESIGN Reactive Vega builds on a long-running thread of research on declarative visualization design, popularized by the Grammar of Graphics [39] and Polaris [34] (now Tableau).”
Mastering the Information Age - Solving Problems with Visual Analytics (2010, methodology): “Two pioneering systems in visual online analytical processing Polaris is a visual tool for multidimensional analysis developed at Stanford University[107] and inherits the basic idea of the classical pivot table interface, using embedded graphical marks rather than textual numbers in the table cells”
Protovis: A Graphical Toolkit for Visualization (2009, methodology): “A frequently-needed data transformation is to group relational data into a tree; this is accomplished using the nest operator [29, 37].”
Toward a Deeper Understanding of the Role of Interaction in Information Visualization (2007, methodology): “Beyond color encoding, many systems provide other encoding techniques, such as size (e.g., Dust & Magnet [55]), orientation (e.g., Polaris [ 43 ]), font (e.g., SemaSpace [31]), and shape (e.g., Spotfire [2]).”
Combining Design and Performance in a Data Visualization Management System (2017, background): “Data exploration tools and decision-support systems [38, 50, 51], knowledge exploration interfaces [6, 54], machine learning and statistical suites [2, 3], or news media outlets [1] are just a few examples that expose end-users to interactive visualizations.”
prefuse: a toolkit for interactive information visualization (2005, methodology): “Polaris [45] applies infovis techniques to provide a powerful system for visualizing relational databases.”
RAWGraphs: A Visualisation Platform to Create Open Outputs (2017, methodology): “Its drag - and - drop interface for mapping visual dimensions is similar to other tools, such as Tableau [3], Lyra [4] and Polestar 1 .”
Voyager 2: Augmenting Visual Analysis with Partial View Specifications (2017, methodology): “Similar to PoleStar [39] and Tableau [29], Voyager 2 maintains a one-to-one mapping between the interface and the underlying specification.”