ChartSeer: Interactive Steering Exploratory Visual Analysis With Machine Intelligence
Bibliographic record
Abstract
During exploratory visual analysis (EVA), analysts need to continually determine which subsequent activities to perform, such as which data variables to explore or how to present data variables visually. Due to the vast combinations of data variables and visual encodings that are possible, it is often challenging to make such decisions. Further, while performing local explorations, analysts often fail to attend to the holistic picture that is emerging from their analysis, leading them to improperly steer their EVA. These issues become even more impactful in the real world analysis scenarios where EVA occurs in multiple asynchronous sessions that could be completed by one or more analysts. To address these challenges, this work proposes ChartSeer, a system that uses machine intelligence to enable analysts to visually monitor the current state of an EVA and effectively identify future activities to perform. ChartSeer utilizes deep learning techniques to characterize analyst-created data charts to generate visual summaries and recommend appropriate charts for further exploration based on user interactions. A case study was first conducted to demonstrate the usage of ChartSeer in practice, followed by a controlled study to compare ChartSeer's performance with a baseline during EVA tasks. The results demonstrated that ChartSeer enables analysts to adequately understand current EVA status and advance their analysis by creating charts with increased coverage and visual encoding diversity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".