GridSet: Visualizing Individual Elements and Attributes for Analysis of Set-Typed Data
Haeyong Chung, Santhosh Nandhakumar, Seungwon Yang
GridSet: a set visualization for analysis of individual elements and attributes. The main interface of GridSet: (a) the Main View, (b) the Visual Property Menu, (c) the Query View, (d) the Set View (orange-highlighted views represent added sets on the Main view), and (e) the Detail View that provides detailed information of the elements. This figure depicts an analysis of an Academy Awards (AA) dataset defining the award categories as sets, and the individual nominees as elements (blue or orange glyphs in the grids) who have been nominated in each category since the first AA ceremony in 1929. The sets are spatially arranged based on similar award categories. The nominees for the 2017 AA are highlighted in orange. The size of the element glyph is defined by the total number of nominations since 1929. The grids are divided into subdivisions based on common nominees across different award categories; note that they are connected by colored intersection links across different sets.
Abstract
We present GridSet, a novel set visualization for exploring elements, their attributes, intersections, as well as entire sets. Each set representation is composed of glyphs, which represent individual elements and their numerous attributes utilizing different visual encodings. In each set, elements are organized within a grid treemap layout that can provide space-efficient overviews of the elements structured by set intersections across multiple sets. These intersecting elements can be connected among sets through visual links. These visual representations for the individual set, elements, and intersection in GridSet facilitate novel interaction approaches for undertaking analysis tasks by utilizing both macroscopic views of sets, as well as microscopic views of elements and attribute details. In order to perform multiple set operations, GridSet supports a simple and straightforward process for set operations through dragging and dropping set objects. Our use cases involving two large set-typed datasets demonstrate that GridSet facilitates the exploration and identification of meaningful patterns and distributions of elements with respect to attributes for solving complex analysis problems in set-typed data.
Citation
GridSet: Visualizing Individual Elements and Attributes for Analysis of Set-Typed Data
Haeyong Chung, Santhosh Nandhakumar, Seungwon Yang
IEEE Trans. Visualization & Comp. Graphics, accepted, 2020
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