VLDB 2026 Research / reviewers in the wild / expert
Katerina Batziakoudi
dblp:267/0050
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2026
0009-0003-7069-9339ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
2 papers |
Visualization and visual analytics · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › visual encoding
bar chart |
1.0 | 1 | 2026 | Beyond Log Scales: Toward Cognitively Informed Bar Charts for Orders of Magnitude Values · IEEE Trans. Vis. Comput. Graph. 2026 |
Visualization and visual analytics
visualization design |
0.9 | 1 | 2025 | Lost in Magnitudes: Exploring Visualization Designs for Large Value Ranges · CHI 2025 |
Visualization and visual analytics
perception |
0.3 | 1 | 2026 | Beyond Log Scales: Toward Cognitively Informed Bar Charts for Orders of Magnitude Values · IEEE Trans. Vis. Comput. Graph. 2026 |
Methods — techniques the papers use, named apart from their topics
crowdsourced experiment · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Beyond Log Scales: Toward Cognitively Informed Bar Charts for Orders of Magnitude ValuesabstractIn this work, we challenge the dominant use of logarithmic scales to communicate values spanning multiple orders of magnitude-Orders of Magnitude Values (OMVs)-to the general public. Focusing on bar charts, we incorporate cognitive insights into visualization design to better align with how humans perceive OMVs. Studies in cognitive psychology suggest that, for large numerical ranges such as millions and billions, people do not think logarithmically. Instead, they perceive numbers in a piecewise linear manner, grouping values into scale words (e.g., millions) and applying linear reasoning within each group. We build upon a recently introduced piecewise linear scale, EplusM, and validate its use in bar charts, which we refer to as EplusM bar charts. We also introduce two novel variants of the EplusM bar chart informed by findings in numerical perception: Bricks, which builds on the concepts of round numbers and subitizing, and Multi-Magnitude, which leverages categorical perception of large numbers. In a crowdsourced experiment, we evaluate four bar chart designs: 1) Log, 2) EplusM, 3) Bricks, and 4) Multi-Magnitude, across value retrieval and quantitative comparison tasks. Our results show that EplusM bar charts are significantly preferred over logarithmic designs, increase user confidence, and reduce perceived mental demand, while maintaining task performance. These findings suggest that EplusM bar charts can serve as effective alternatives to logarithmic ones when visualizing OMVs for general audiences. Katerina Batziakoudi, Stéphanie Rey, Jean-Daniel Fekete |
IEEE Trans. Vis. Comput. Graph. | 1 |
| 2025 | Lost in Magnitudes: Exploring Visualization Designs for Large Value RangesabstractBest Paper Award Katerina Batziakoudi, Florent Cabric, Stéphanie Rey, Jean-Daniel Fekete |
CHI | 1 |