Visualizing Dimension Coverage to Support Exploratory Analysis

Data analysis involves constantly formulating and testing new hypotheses and questions about data. When dealing with a new dataset, especially one with many dimensions, it can be cumbersome for the analyst to clearly remember which aspects of the data have been investigated (i.e., visually examined...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 23(2017), 1 vom: 11. Jan., Seite 21-30
1. Verfasser: Sarvghad, Ali (VerfasserIn)
Weitere Verfasser: Tory, Melanie, Mahyar, Narges
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2017
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
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520 |a Data analysis involves constantly formulating and testing new hypotheses and questions about data. When dealing with a new dataset, especially one with many dimensions, it can be cumbersome for the analyst to clearly remember which aspects of the data have been investigated (i.e., visually examined for patterns, trends, outliers etc.) and which combinations have not. Yet this information is critical to help the analyst formulate new questions that they have not already answered. We observe that for tabular data, questions are typically comprised of varying combinations of data dimensions (e.g., what are the trends of Sales and Profit for different Regions?). We propose representing analysis history from the angle of dimension coverage (i.e., which data dimensions have been investigated and in which combinations). We use scented widgets [30] to incorporate dimension coverage of the analysts' past work into interaction widgets of a visualization tool. We demonstrate how this approach can assist analysts with the question formation process. Our approach extends the concept of scented widgets to reveal aspects of one's own analysis history, and offers a different perspective on one's past work than typical visualization history tools. Results of our empirical study showed that participants with access to embedded dimension coverage information relied on this information when formulating questions, asked more questions about the data, generated more top-level findings, and showed greater breadth of their analysis without sacrificing depth 
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