AggreSet: Rich and Scalable Set Exploration using Visualizations of Element Aggregations

Datasets commonly include multi-value (set-typed) attributes that describe set memberships over elements, such as genres per movie or courses taken per student. Set-typed attributes describe rich relations across elements, sets, and the set intersections. Increasing the number of sets results in a c...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics Vol. 22; no. 1; pp. 688 - 697
Main Authors: Yalcin, M. Adil, Elmqvist, Niklas, Bederson, Benjamin B.
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aggregates
Chapters
Colleges & universities
Combinatorial analysis
data exploration
Data visualization
Datasets
design
Domains
Enrollments
Exploration
Filtering
Histograms
interaction
Intersections
Motion pictures
Multi-valued attributes
Scalability
set visualization
sets
Visualization
set visualization
visualization
sets
Multi-valued attributes
design
interaction
data exploration
scalability
ISSN:1077-2626, 1941-0506, 1941-0506
Online Access:Get full text
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Summary:Datasets commonly include multi-value (set-typed) attributes that describe set memberships over elements, such as genres per movie or courses taken per student. Set-typed attributes describe rich relations across elements, sets, and the set intersections. Increasing the number of sets results in a combinatorial growth of relations and creates scalability challenges. Exploratory tasks (e.g. selection, comparison) have commonly been designed in separation for set-typed attributes, which reduces interface consistency. To improve on scalability and to support rich, contextual exploration of set-typed data, we present AggreSet. AggreSet creates aggregations for each data dimension: sets, set-degrees, set-pair intersections, and other attributes. It visualizes the element count per aggregate using a matrix plot for set-pair intersections, and histograms for set lists, set-degrees and other attributes. Its non-overlapping visual design is scalable to numerous and large sets. AggreSet supports selection, filtering, and comparison as core exploratory tasks. It allows analysis of set relations inluding subsets, disjoint sets and set intersection strength, and also features perceptual set ordering for detecting patterns in set matrices. Its interaction is designed for rich and rapid data exploration. We demonstrate results on a wide range of datasets from different domains with varying characteristics, and report on expert reviews and a case study using student enrollment and degree data with assistant deans at a major public university.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2015.2467051