Two Papers Accepted for IEEE VIS

We’ve recently learned that not one, but TWO articles from the VACLab will be part of IEEE VIS 2019 and published in the January 2020 issue of IEEE Transactions on Visualization and Computer Graphics (TVCG).   Both papers are related to our NSF-sponsored research exploring contextual visualization methods.

The first paper describes our latest work to address selection bias in high-dimensional exploratory visualization.  A pre-print is available on arxiv and a video figure can be found on our Vimeo account.

The second paper describes our work exploring context-aware aggregation methods for high-dimensional visualization.  This work uses a data-driven approach to help users identify the right level of granularity for aggregation at the time of interaction.  As with the first paper, a pre-print has been posted to arxiv and a video figure can be found on Vimeo.

Click here to find more information about this research, links to software, and references to other related articles.


David Borland, Wenyuan Wang, Jonathan Zhang, Joshua Shrestha, David Gotz. Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data.  To appear in IEEE TVCG and IEEE VAST 2019.
[Preprint available on arXivarXiv:1906.07625]
[Video figure available by clicking here.]

David Gotz, Jonathan Zhang, Wenyuan Wang, Joshua Shrestha, David Borland. Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation.  To appear in IEEE TVCG and IEEE VAST 2019.
[Preprint available on arXiv: arXiv:1906.07617]
[Video figure available by clicking here.]

Sch of Inform and Libr Science