Papers › Data Point Selection for Line Chart Visualization: Methodological Assessment and...
Data Point Selection for Line Chart Visualization: Methodological Assessment and Evidence-Based Guidelines
Jonas Van Der Donckt, Jeroen Van Der Donckt, Michael Rademaker, Sofie Van Hoecke
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Time series visualization plays a crucial role in identifying patterns and extracting insights across various domains. However, as datasets continue to grow in size, visualizing them effectively becomes challenging. Downsampling, which involves data aggregation or selection, is a well-established approach to overcome this challenge. This work focuses on data selection algorithms, which accomplish downsampling by selecting values from the original time series. Despite their widespread adoption in visualization platforms and time series databases, there is limited literature on the evaluation of these techniques. To address this, we propose an extensive metrics-based evaluation methodology. Our methodology analyzes visual representativeness by assessing how well a downsampled time series line chart visually approximates the original data. Moreover, our methodology includes a novel concept called "visual stability", which captures visual changes when updating (streaming) or interacting with the visualization (panning and zooming). We evaluated four data point selection algorithms across three open-source visualization toolkits using our proposed methodology, considering various figure-drawing properties. Following the analysis of our findings, we formulated a set of evidence-based guidelines for line chart visualization at scale with downsampling. To promote reproducibility and enable the qualitative evaluation of new advancements in time series data point selection, we have made our methodology and results openly accessible. The proposed evaluation methodology, along with the obtained insights from this study, establishes a foundation for future research in this domain.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections