As line datasets grow larger, the demand for effective visual data analysis becomes increasingly important. Understanding large‑scale datasets remains a fundamental challenge. A critical trade‑off is presented by existing line selection methods: they either produce efficiency, ac
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As line datasets grow larger, the demand for effective visual data analysis becomes increasingly important. Understanding large‑scale datasets remains a fundamental challenge. A critical trade‑off is presented by existing line selection methods: they either produce efficiency, accuracy, or human interpretability, rarely achieving all three simultaneously. This gap is addressed by the development of human‑guided and context‑aware brushing techniques, which are supported by manual, semi‑automatic and automatic refinement methods. Through empirical evaluation via two user studies, it was found that, whilst context‑aware brushes offer theoretical promise, statistical superiority over conventional brushing approaches is not demonstrated. However, selection accuracy is consistently improved by refinement techniques, with manual refinement yielding the highest accuracy gains (12.6\%) followed by semi‑automatic refinement (9.8\%). Notably, efficiency gains from refinement remain dataset‑dependent, with no single technique universally dominating across varied data characteristics. Manual and semi‑automatic refinements are preferred by users seeking high‑accuracy improvements. Although similar efficiency scores are exhibited by manual and semi‑automatic refinements, the lowest variance is observed for the semi‑automatic method; consequently, it is recommended for users prioritising efficiency. The findings emphasise a fundamental design principle: Interpretability and user agency should be prioritised over full automation.
