Cross-table linking and brushing: interactive visual analysis of multiple tabular data sets

Cross-table linking and brushing: interactive visual analysis of multiple tabular data sets Studying complex problems often requires identifying and exploring connections and dependencies among several, seemingly unrelated, data sets. Those data sets are often represented as data tables. We propose a novel approach to studying such data sets using linking and brushing across multiple data tables in a coordinated multiple views system. We first identify possible mappings from a subset of one data set to a subset of another data set. That collection of mappings is then used to specify linking among data sets and to support brushing across data sets. Brushing in one data set is then mapped to a brush in the destination data set. If the brush is refined in the destination data set, the inverse mapping, or a back-link, is used to determine the refined brush in the original data set. Brushing and back-links make it possible to efficiently create and analyze complex queries interactively in an iterative process. That process is further supported by a user interface that keeps track of the mappings, links and brushes. The proposed approach is evaluated using three data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Visual Computer Springer Journals

Cross-table linking and brushing: interactive visual analysis of multiple tabular data sets

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Computer Graphics; Computer Science, general; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision
ISSN
0178-2789
eISSN
1432-2315
D.O.I.
10.1007/s00371-018-1516-8
Publisher site
See Article on Publisher Site

Abstract

Studying complex problems often requires identifying and exploring connections and dependencies among several, seemingly unrelated, data sets. Those data sets are often represented as data tables. We propose a novel approach to studying such data sets using linking and brushing across multiple data tables in a coordinated multiple views system. We first identify possible mappings from a subset of one data set to a subset of another data set. That collection of mappings is then used to specify linking among data sets and to support brushing across data sets. Brushing in one data set is then mapped to a brush in the destination data set. If the brush is refined in the destination data set, the inverse mapping, or a back-link, is used to determine the refined brush in the original data set. Brushing and back-links make it possible to efficiently create and analyze complex queries interactively in an iterative process. That process is further supported by a user interface that keeps track of the mappings, links and brushes. The proposed approach is evaluated using three data sets.

Journal

The Visual ComputerSpringer Journals

Published: May 3, 2018

References

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