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Keshif: Rapid and Expressive Tabular Data Exploration for Novices
IEEE Transactions on Visualization and Computer Graphics
|July 11, 2017
Summary
Keshif offers an automated approach to tabular data exploration, enhancing speed and reducing learning curves for novices. This system helps users discover numerous data insights efficiently, comparable to expert tools.
Area of Science:
- Computer Science
- Human-Computer Interaction
- Data Visualization
Background:
- Manual data exploration interfaces require significant user effort and time, limiting exploratory speed and accessibility for novices.
- Existing tools often demand high expertise, creating a barrier for new data analysts.
Purpose of the Study:
- To introduce Keshif, a novel systematic design for automated tabular data exploration.
- To enhance data exploration speed, reduce learning efforts, and support novice data analysts.
Main Methods:
- Keshif aggregates data by attribute values and visualizes summaries using a consistent, data-type-based design.
- Employs linked highlighting, filtering, and comparison selections for detailed distribution analysis.
- Features aggregate metrics, scale modes, calculated attributes, and saved selections for increased expressiveness.
Main Results:
- Keshif simplifies the authoring of interactive dashboards from raw data.
- Novice users achieved an average of nearly 30 data insights within 15 minutes of exploration.
- Performance is comparable to skilled users employing more complex, traditional data exploration tools.
Conclusions:
- Keshif's automated design effectively supports novice data analysts in achieving rapid and deep data insights.
- The system demonstrates the potential of principled automated design to democratize data exploration.
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