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Data visualization provides a powerful way for analysts to explore and make data-driven discoveries. However, current visual analytic tools provide only limited support for hypothesis-driven inquiry, as their built-in interactions and workflows are primarily intended for exploratory analysis. Visualization tools notably lack capabilities that would allow users to visually and incrementally test the fit of their conceptual models and provisional hypotheses against the data. This imbalance could bias users to overly rely on exploratory analysis as the principal mode of inquiry, which can be detrimental to discovery. In this paper, we introduce Visual (dis) Confirmation, a tool for conducting confirmatory, hypothesis-driven analyses with visualizations. Users interact by framing hypotheses and data expectations in natural language. The system then selects conceptually relevant data features and automatically generates visualizations to validate the underlying expectations. Distinctively, the resulting visualizations also highlight places where one's mental model disagrees with the data, so as to stimulate reflection. The proposed tool represents a new class of interactive data systems capable of supporting confirmatory visual analysis, and responding more intelligently by spotlighting gaps between one's knowledge and the data. We describe the algorithmic techniques behind this workflow. We also demonstrate the utility of the tool through a case study.
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Modeling and Leveraging Analytic Focus During Exploratory Visual Analysis
Visual analytics systems enable highly interactive exploratory data analysis. Across a range of fields, these technologies have been successfully employed to help users learn from complex data. However, these same exploratory visualization techniques make it easy for users to discover spurious findings. This paper proposes new methods to monitor a user’s analytic focus during visual analysis of structured datasets and use it to surface relevant articles that contextualize the visualized findings. Motivated by interactive analyses of electronic health data, this paper introduces a formal model of analytic focus, a computational approach to dynamically update the focus model at the time of user interaction, and a prototype application that leverages this model to surface relevant medical publications to users during visual analysis of a large corpus of medical records. Evaluation results with 24 users show that the modeling approach has high levels of accuracy and is able to surface highly relevant medical abstracts.
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- Award ID(s):
- 1704018
- PAR ID:
- 10252449
- Date Published:
- Journal Name:
- Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 1 to 15
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3411764.3445674
