A Pipeline for Tailored Sampling for Progressive Visual Analytics

Proceedings of the 2022 International EuroVis Workshop on Visual Analytics (EuroVA)

Authors

Marius Hogräfer Jakob Burkhardt Hans-Jörg Schulz
Screenshot of ProSample.
Screenshot from ProSample showing a side-by-side view from two pipelines (left and right views) sampling the spotify dataset, shown as binned scatterplots, as well as the delta between these bins (center view).

Abstract

Progressive Visual Analytics enables analysts to interactively work with partial results from long-running computations early on instead of forcing them to wait. For very large datasets, the first step is to divide that input data into smaller chunks using sampling, which are then passed down the progressive analysis pipeline all the way to their progressive visualization in the end. The quality of the partial results produced by the progression heavily depends on the quality of these chunks, that is, chunks need to be representative of the dataset. Whether or not a sampling approach produces representative chunks does however depend on the particular analysis scenario.

Citation in BibTeX

To cite this article, we encourage you to use the following bibtex entry in your citation manager:

@inproceedings{Hograefer2022_sampling,
  title = {A Pipeline for Tailored Sampling for Progressive Visual Analytics},
  author = {Hogr\"afer, Marius and Burkhardt, Jakob and Schulz, Hans-J\"org},
  booktitle = {Proc. of the 13th International {EuroVis} Workshop on Visual Analytics ({EuroVA}'22)},
  pages = {49--53},
  editor = {Bernard, J\"urgen and Angelini, Marco},
  publisher = {Eurographics Association},
  isbn = {978-3-03868-183-0},
  doi = {10.2312/eurova.20221079},
  year = {2022}
}